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U. S. DEPARTMENT OF COMMERCE 
 ENVIRONMENTAL SCIENCE SERVICES ADMINISTRATION 
 
 A World Atlas of 
 Atmospheric Radio Refractivity 
 
 B. R. Bean, B. A. Cahoon, C. A. Samson, and G. D. Thayer 
 
 Institute for Telecommunication Sciences and Aeronomy 
 
 Institutes for Environmental Research 
 
 Boulder, Colo. 
 
 ESSA Monograph 1 
 
 UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1966 
 
U.S. DEPARTMENT OF COMMERCE 
 John T. Connor, Secretary 
 
 ENVIRONMENTAL SCIENCE SERVICES ADMINISTRATION 
 
 Robert M. White, Administrator 
 
 Institutes for Environmental Research 
 George S. Benton, Director 
 
 This work was supported in part by the 
 
 U.S. Navy Weather Research Facility, 
 
 Contract Number 7560: NABUSTDS 65 Ser. 300. 
 
 Library of Congress Catalog Card Number: Map 66-74 
 
 For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402. Price $2.25 
 
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 Preface 
 
 This atlas has been prepared for the radio engineer who wants an estimate of the 
 behavior of the radio refractive index at any point on the earth. It has many limitations, 
 which the authors have tried to point out in the text, but should provide useful informa- 
 tion for engineering design and predictions of tropospheric radio circuits. 
 
 The work upon which this atlas is based has been in progress for several years in 
 the Radio Meteorology Section of the Central Radio Propagation Laboratory, National 
 Bureau of Standards, Boulder, Colo.* Many people have contributed to this research effort 
 through the years, and it is not feasible to acknowledge them all individually. Those most 
 directly involved with the preparation of material for this atlas include W. B. Sweezy and 
 W. A. Williams, who were responsible for most of the computer programming ; Mrs. B. J. 
 Weddle, who assisted in processing and cataloging of the data; T. D. Stevens, who did 
 most of the compiling, checking, and plotting of the maps, graphs, and tables; and 
 L. P. Riggs, J. D. Horn, and Mrs. G. E. Richmond (deceased), who contributed to the 
 atlas project in its early stages. 
 
 The National Weather Records Center of the U. S. Weather Bureau in Asheville, 
 N. C, supplied the meteorological data upon which this atlas is based, and also performed 
 the preliminary conversions of these data to refractivity parameters. 
 
 Finally, we thank the personnel of the U. S. Navy Weather Research Facility at 
 Norfolk, Va., who foresaw a need for such an atlas and have arranged for support of the 
 project over a period of years. 
 
 *Now the Institute for Telecommunication Sciences and Aeronomy (ITSA), Environmental Science Services Administration. 
 
 Ill 
 
Digitized by the Internet Archive 
 
 in 2013 
 
 http://archive.org/details/worldatlasofatmOOinst 
 
Contents 
 
 Page 
 
 Preface m 
 
 Abstract * x 
 
 1. Introduction 1 
 
 2. Discussion of Basic Data 2 
 
 3. World Maps of JV(z) 4 
 
 3.1. Development 4 
 
 Figure 1. Three-part exponential fit to mean TV-profile: Koror. 5 
 
 Figure 2. Three-part exponential fit to mean TV-profile: Dakar 6 
 
 3.2. Discussion of N(z) Map Contours 4 
 
 3.3. Reliability of Contours of N(z) Maps 6 
 
 3.4. Problem Areas of N(z) Maps 7 
 
 Figure 3. Five-year mean wet refractivity term: February 8 
 
 Figure 4. Five-year mean wet refractivity term : May 9 
 
 4. World Maps of AJV 10 
 
 4.1. Development 10 
 
 4.2. Discussion of Contours and Reliability of A TV Maps 10 
 
 5. World Maps of Extreme TV-Gradients 12 
 
 5.1. Development 12 
 
 5.2. Discussion of Gradient Map Contours (Subrefraction) 12 
 
 5.3. Discussion of Gradient Map Contours ( Superref raction and Ducting) 14 
 
 5.4. Discussion of Cumulative Distributions of Ground-Based Gradients 15 
 
 Figure 5. Five-year mean vertical refractive gradient profile: Aden 16 
 
 Figure 6. Five-year mean vertical refractive gradient profile: Nicosia 17 
 
 5.5. Reliability and Limitations of Ground-Based Refractivity Gradient Data 17 
 
 6. World Maps of Mean Tropopause Altitudes 19 
 
 7. Appraisal of Results 20 
 
 7.1. Accuracy of N(z) Maps 20 
 
 Table 1. Standard errors of 5-year mean values of monthly mean A r s for 40 stations 20 
 
 Figure 7. Correlation of A TV: monthly mean A r -profiles versus monthly mean exponential model 21 
 
 Table 2. Absolute errors in recovering mean TV from map contours for 32 stations 21 
 
 Table 3. Approximate total standard errors, <>>, for N (z) maps in appendix A 22 
 
 7.2 Accuracy of ATV Maps. 23 
 
 Figure 8. Correlation of A TV: monthly mean TV-profiles versus monthly mean weather 
 
 summary data 22 
 
 7.3. Accuracy of Gradient Maps 24 
 
 8. Conclusions 25 
 
 9. References 26 
 
 10. Appendix A. World Maps of N(z) Parameters 29 
 
 Table A-l. Cumulative distribution levels of surface refractivity 30 
 
 Figure A-l. Location of N(z) data stations 32 
 
 Figure A-2. Mean sea-level dry term, D„: February 33 
 
 Figure A-3. Mean sea-level wet term, W : February 33 
 
 Figure A-4. Dry-term tropospheric scale height in km, Hi : February 34 
 
 Figure A-5. Dry-term stratospheric scale height in km, H 2 : February 34 
 
 Figure A-6. Wet-term scale height in km, H w : February 35 
 
 Figure A-7. Mean density tropopause altitude in km, z t : February 35 
 
 Figure A-8. Mean sea-level dry term, D : May 36 
 
 Figure A-9. Mean sea-level wet term, W a : May 36 
 
 Figure A-10. Dry-term tropospheric scale height in km, Hi : May 37 
 
 Figure A-ll. Dry-term stratospheric scale height in km, H 2 : May 37 
 
 Figure A-12. Wet-term scale height in km, H w : May 38 
 
 Figure A-13. Mean density tropopause altitude in km, z t : May 38 
 
 Figure A-14. Mean sea-level dry term, D a : August 39 
 
Page 
 
 Figure A-15. Mean sea-level wet term, W : August 39 
 
 Figure A-16. Dry-term tropospheric scale height in km, Hi : August 40 
 
 Figure A-17. Dry-term stratospheric scale height in km, H 2 : August 40 
 
 Figure A-18. Wet-term scale height in km, H w : August 41 
 
 Figure A-19. Mean density tropopause altitude in km, z t : August 41 
 
 Figure A-20. Mean sea-level dry term, D„: November 42 
 
 Figure A-21. Mean sea-level wet term, W a : November „ 42 
 
 Figure A-22. Dry-term tropospheric scale height in km, Hi : November 43 
 
 Figure A-23. Dry-term stratospheric scale height in km, ff 2 : November 43 
 
 Figure A-24. Wet-term scale height in km, H w : November 44 
 
 Figure A-25. Mean density tropopause altitude in km, z t : November 44 
 
 Figure A-26. Standard prediction error of the exponential fit to the mean 
 
 wet-term profile: February 45 
 
 Figure A-27. Standard prediction error of the exponential fit to the mean 
 
 wet- term profile : May 45 
 
 Figure A-28. Standard prediction error of the exponential fit to the mean 
 
 wet-term profile : August 46 
 
 Figure A-29. Standard prediction error of the exponential fit to the mean 
 
 wet-term profile : November 46 
 
 Figure A-30. Areas of doubtful applicability of three-part exponential model of N(z) 
 
 f or z < 6 km 47 
 
 11. Appendix B. World Maps of AN , 48 
 
 Table B-l. Mean surface refractivity. 49 
 
 Figure B-l. Location of AN data stations 52 
 
 Figure B-2. Monthly mean AN: January 53 
 
 Figure B-3. Monthly mean A N : February 53 
 
 Figure B-4. Monthly mean AN: March 54 
 
 Figure B-5. Monthly mean A N : April 54 
 
 Figure B-6. Monthly mean A N : May 55 
 
 Figure B-7. Monthly mean A N : June 55 
 
 Figure B-8. Monthly mean AN: July. 56 
 
 Figure B-9. Monthly mean A N : August 56 
 
 Figure B-10. Monthly mean AN: September 57 
 
 Figure B-ll. Monthly mean AN: October 57 
 
 Figure B-12. Monthly mean AN: November 58 
 
 Figure B-13. Monthly mean AN: December ._. 58 
 
 Figure B-14. Annual mean of sea-level refractivity, N 59 
 
 Figure B-15. Annual mean of refractivity gradient between surface and 1 km, AN. 59 
 
 Figure B-16. Slope of regression line o f AN versus N s , b 60 
 
 Figure B-17. Correlation coefficient of AN versus N s 60 
 
 Figure B-18. Standard prediction error of the regression line of AN versus N, _;_;_ 61 
 
 Figure B-19. Standard prediction error of the regression line of AN versus N, as a percent of AN 61 
 
 Figure B-20. Areas of doubtful applicability of using N s to predict AN 62 
 
 12. Appendix C. World Maps and Cumulative Distribution Charts of Gradients of 
 
 Ground-Based Atmospheric Layers 63 
 
 Figure C-l. Percent of time gradient ^ (N/km) : February 64 
 
 Figure C-2. Percent of time gradient ^ (N/km) : May. 64 
 
 Figure C-3. Percent of time gradient ^ (N/km) : August 65 
 
 Figure C-4. Percent of time gradient ^ (N/km) : November 65 
 
 Figure C-5. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: February 66 
 
 Figure C-6. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: February 66 
 
 Figure C-7. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: May 67 
 
 Figure C-8. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: May 67 
 
 Figure C-9. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: August 68 
 
 Figure C-10. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: August 68 
 
 Figure C-ll. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: November. ... 69 
 
 Figure C-12. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: November 69 
 
 Figure C-13. Percent of time gradient f^ -100 (N/km) : February 70 
 
 Figure C-14. Percent of superrefractive layers thicker than 100 m: February 70 
 
 Figure C-15. Percent of time gradient f= -100 (N/km) : May 71 
 
 Figure C-16. Percent of superrefractive layers thicker than 100 m: May 71 
 
 vi 
 
Page 
 
 Figure C-17. Percent of time gradient ^ -100 (N/km) : August 72 
 
 Figure C-18. Percent of superrefractive layers thicker than 100 m: August 72 
 
 Figure C-19. Percent of time gradient ^ -100 (N/km) : November 73 
 
 Figure C-20. Percent of superrefractive layers thicker than 100 m: November 73 
 
 Figure C-21. Percent of time gradient ^ -157 (TV/km) : February 74 
 
 Figure C-22. Percent of ducting layers thicker than 100 m: February 74 
 
 Figure C-23. Percent of time gradient ^ -157 (N/km) : May 75 
 
 Figure C-24. Percent of ducting layers thicker than 100 m: May 75 
 
 Figure C-25. Percent of time gradient ^ -157 (N/km) : August 76 
 
 Figure C-26. Percent of ducting layers thicker than 100 m : August 76 
 
 Figure C-27. Percent of time gradient ^ -157 (N/km) : November 77 
 
 Figure C-28. Percent of ducting layers thicker than 100 m : November 77 
 
 Figure C-29. Percent of time trapping frequency < 3000 Mc/s : February 78 
 
 Figure C-30. Percent of time trapping frequency < 1000 Mc/s: February 78 
 
 Figure C-31. Percent of time trapping frequency < 300 Mc/s: February 79 
 
 Figure C-32. Percent of time trapping frequency < 3000 Mc/s : May 79 
 
 Figure C-33. Percent of time trapping frequency < 1000 Mc/s: May 80 
 
 Figure C-34. Percent of time trapping frequency < 300 Mc/s: May 80 
 
 Figure C-35. Percent of time trapping frequency < 3000 Mc/s: August 81 
 
 Figure C-36. Percent of time trapping frequency < 1000 Mc/s : August 81 
 
 Figure C-37. Percent of time trapping frequency < 300 Mc/s: August 82 
 
 Figure C-38. Percent of time trapping frequency < 3000 Mc/s : November 82 
 
 Figure C-39. Percent of time trapping frequency < 1000 Mc/s: November 83 
 
 Figure C-40. Percent of time trapping frequency < 300 Mc/s: November 83 
 
 Figure C-41. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 
 
 100-m layer: February 84 
 
 Figure C-42. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 
 
 100-m layer : February 84 
 
 Figure C-43. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 
 
 100-m layer: February 85 
 
 Figure C-44. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 
 
 100-m layer: February 85 
 
 Figure C-45. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 
 
 100-m layer: May 86 
 
 Figure C-46. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 
 
 100-m layer: May 86 
 
 Figure C-47. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 
 
 100-m layer: May 87 
 
 Figure C-48. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 
 
 100-m layer: May 87 
 
 Figure C-49. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 
 
 100-m layer : August 88 
 
 Figure C-50. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 
 
 100-m layer : August 88 
 
 Figure C-51. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 
 
 100-m layer : August 89 
 
 Figure C-52. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 
 
 100-m layer : August 89 
 
 Figure C-53. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 
 
 100-m layer: November 90 
 
 Figure C-54. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 
 
 100-m layer : November 90 
 
 Figure C-55. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 
 
 100-m layer: November 91 
 
 Fjgure C-56. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 
 
 100-m layer : November 91 
 
 Table C-l. Median and minimum trapping frequency (Mc/s) of ducting layers 92 
 
 Figure C-57. (a) Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Aden, Arabia ( February, May) 93 
 
 Figure C-57. (b) Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Aden, Arabia (August, November) 94 
 
 vii 
 
Page 
 
 Figure C-58. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Amundsen-Scott, Antarctica 95 
 
 Figure C-59. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Balboa (Albrook) , Panama C. Z 96 
 
 Figure C-60. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Bangui, Central African Republic 97 
 
 Figure C-61. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Bordeaux, France 98 
 
 Figure C-62. (a) Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer; Dakar, Republic of Senegal (February, May) 99 
 
 Figure C-62. (b) Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Dakar, Republic of Senegal (August, November) 100 
 
 Figure C-63. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Denver, Colo 101 
 
 Figure C-64. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Ezeiza, Argentina 102 
 
 Figure C-65. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Fort Smith, Northwest Territories, Canada 103 
 
 Figure C-66. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Hilo, Hawaii 104 
 
 Figure C-67. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Long Beach, Calif 105 
 
 Figure C-68. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Lourenco Marques, Portuguese East Africa 106 
 
 Figure C-69. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Nandi, Fiji Islands 107 
 
 Figure C-70. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : New York, N. Y 108 
 
 Figure C-71. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Nicosia, Cyprus 109 
 
 Figure C-72. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Ostersund, Sweden 110 
 
 Figure C-73. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Perth, Australia Ill 
 
 Figure C-74. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Saigon, Viet Nam 112 
 
 Figure C-75. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : San Juan, P. R 113 
 
 Figure C-76. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Ship Station "C" 114 
 
 Figure C-77. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer : Tashkent, U.S.S.R 115 
 
 Figure C-78. Cumulative probability distributions of dN/dh for ground-based 
 
 100-m layer: Vladivostok, U.S.S.R '. 116 
 
 13. Appendix D. World Charts of Tropopause Heights 117 
 
 Figure D-l. Tropopause heights (km), based on temperature lapse rate: February 117 
 
 Figure D-2. Tropopause heights (km) , based on temperature lapse rate: May 118 
 
 Figure D-3. Tropopause heights (km), based on temperature lapse rate: August 118 
 
 Figure D-4. Tropopause heights (km) , based on temperature lapse rate: November 119 
 
 14. Appendix E. Sample Listing of the Computer Output for San Juan, P. R., and 
 
 Amundsen-Scott, Antarctica 120 
 
 Table E-l. Mean iV-profiles: San Juan, P. R 121 
 
 Table E-2. Mean 2V-profiles: Amundsen-Scott, Antarctica 123 
 
 Table E-3. Cumulative distributions of ground-based gradients: Amundsen-Scott, Antarctica. .. 125 
 Table E-4. Analysis of ground-based superrefractive and ducting layers: 
 
 Amundsen-Scott, Antarctica 127 
 
 Vlll 
 
Abstract 
 
 This atlas presents world maps and graphs of upper-air radio refractivity, N(z) 
 (where z is the altitude), mean monthly AN (the difference between the refractivity 
 at the surface and at 1 km above the surface), extreme values of gradients of refrac- 
 tivity observed in the lowest layer of the atmosphere (including maps of minimum 
 trapped frequency ducting gradients), and monthly mean tropopause heights. All 
 refractivity values were derived from radiosonde data. The N(z) maps are presented 
 in terms of a three-part exponential model, with separate exponentials for the water 
 vapor and air density terms of N(z), with the latter separated into tropospheric and 
 stratospheric terms. The A TV data were derived by interpolation from fixed pressure- 
 level data. 
 
 IX 
 
1. Introduction 
 
 There has been a need in radio engineering 
 for a method whereby the radio refractivity, 
 N, at some height, z, or the gradient of N with 
 respect to height, dN/dz, could be accurately 
 estimated for any world location during any 
 season of the year. Previous studies have at- 
 tempted to solve this problem by determination 
 of the value of surface refractivity, N s , and the 
 use of an exponential decay with height [Bean 
 et al., 1960a] 1 , or by presenting seasonal means 
 and distributions of N at fixed pressure levels in 
 the atmosphere for various radiosonde stations 
 [U. S. Navy, 1955-59; Michaelis and Gossard, 
 1958]. However, these results did not provide 
 any means whereby N (z) could be obtained at 
 any arbitrary location, i.e., places for which 
 meteorological measurements were not avail- 
 able. In addition, specific information on the 
 gradient of the refractive index has not been 
 available previously on a worldwide basis, espe- 
 cially for the very important layers of the at- 
 mosphere at or near the surface where the pres- 
 ence of superref ractive or ducting gradients can 
 produce anomalous propagation of microwaves. 
 
 This atlas presents maps, charts, and discus- 
 
 1 Literature references on page 26. 
 
 sions of the worldwide variations in the radio 
 refractive index. With the aid of this atlas, 
 estimates of the following parameters may be 
 readily determined for any part of the world : 
 the refractivity at any height, N(z) ; the average 
 gradient of N over the first kilometer above the 
 surface, AN ; and the gradient of iV in the lowest 
 layer of the atmosphere (with emphasis on sub- 
 refractive, superrefractive, and ducting layers 
 and the probability of trapping of radio waves 
 by ducting layers). The world distribution of 
 N (z) is presented in the form of a three-part 
 exponential model, with separate exponential 
 terms for the water-vapor term, the density 
 term in the troposphere, and the density term 
 in the stratosphere; the parameters used to 
 represent this N (z) model are the reduced-to- 
 sea-level values of surface water vapor and den- 
 sity terms, the scale heights of the three ex- 
 ponential terms, and the transition height 
 between the tropospheric and stratospheric 
 density exponentials. Seasonal maps of mean 
 tropopause heights, which were obtained in the 
 course of the data reduction required for the 
 three-part exponential model, are also presented. 
 
2. Discussion of Basic Data 
 
 The radio refractive index of the atmosphere, 
 n, exceeds unity by at most 450 parts per mil- 
 lion; it is therefore customary to utilize the 
 radio ref ractivity, N, given by 2 
 
 N = (n-1) X 10 6 . (1) 
 
 The radio refractivity of air for frequencies up 
 to 30,000 Mc/s is given by Smith and Wein- 
 traub [1953] : 
 
 N = 77.6 ^-+3.73 X 10 5 -^, (2) 
 
 where P is the total atmospheric pressure in 
 millibars (mb), T is the absolute temperature 
 in degrees Kelvin (K) , and e is the partial pres- 
 sure of water vapor in millibars. [For the de- 
 velopment of this equation from theory, c.f. 
 Bean, 1962]. The P/T term in (2) is frequently 
 referred to as the "dry term" (even though 
 there is a small water vapor component in the 
 total pressure) and the e/T 2 term, as the "wet 
 term." 
 
 The radiosonde is in general use throughout 
 the world to measure the pressure, temperature, 
 and relative humidity of the upper air. These 
 data can be used to obtain the corresponding 
 vertical profile of radio refractivity. However, 
 there are a number of disadvantages in the use 
 
 a, iOsonde data for the purpose of obtaining 
 N -profiles; perhaps the most important of these 
 is the relatively slow response (large lag con- 
 stants) of the radiosonde temperature and hu- 
 midity sensors [Bunker, 1953; Wagner, 1960, 
 1961; Bean and Dutton, 1961]. Also of some 
 importance is the method of selecting levels for 
 which data are reported. The procedure fol- 
 lowed by most meteorological services consists 
 of reporting temperature, humidity, and height 
 at certain fixed pressure levels, called "manda- 
 tory levels" (e.g., 850 mb), plus a sufficient 
 number of additional "significant" levels to 
 provide a profile of temperature and relative 
 humidity such that linear segments between 
 levels will not depart from the original data at 
 any point by more than 1°C or 10 percent rela- 
 tive humidity. Such tolerances, although ac- 
 ceptable for most meteorological purposes, may 
 result in errors of as much as 30 TV-units (under 
 
 2 Throughout this atlas, the term atmosphere should be understood 
 to mean the nonionized atmosphere, i.e., excluding the ionosphere. 
 
 3 A list of these stations is included in appendix A. 
 
 extreme conditions) in a linear-segment IV-pro- 
 file constructed from radiosonde data. Some 
 punched-card refractivity data are available 
 which were calculated from significant levels 
 chosen with an even wider tolerance, 2°C and 
 30 percent relative humidity [Bean and Cahoon, 
 1961a]. In spite of these deficiencies, this atlas 
 is based entirely upon radiosonde data, since no 
 other worldwide, long-term upper-air data are 
 available. (More detailed measurements, ob- 
 tained primarily from wiresonde and aircraft 
 refractometer flights, are available only for a 
 very few locations and for very limited periods 
 of time.) 
 
 The first step toward obtaining a broad 
 sample of upper-air refractivity data was the 
 selection (by geographic-climatic considerations 
 and period of record available) of 112 radio- 
 sonde stations from the worldwide network. 3 
 Wherever possible, a total of 5 years of data was 
 obtained for each of the 4 representative "sea- 
 sonal" months of February, May, August, and 
 November. 
 
 Five-year means were selected for use in the 
 preparation of this atlas for two reasons : (a) a 
 large number of stations, including all of those 
 in the U.S.S.R., have available records dating 
 back only to the International Geophysical Year 
 (IGY), 1958; (b) 5 years seemed to represent 
 the best compromise between the number of 
 stations and the length of record, since the total 
 amount of. data which could be processed was 
 naturally limited. A large number of stations 
 is desirable for mapping purposes (better cov- 
 erage), while longer periods of record yield 
 more stable (accurate) estimates of long-term 
 means (of climatic variables) . For each radio- 
 sonde ascent, the reported values of pressure, 
 temperature, and humidity at each mandatory 
 or significant level were converted by means of 
 (2) to radio refractivity values (by the Nation- 
 al Weather Records Center, Asheville, N. C). 
 These data, when received at ITSA, were used 
 to obtain four monthly mean N-profiles for the 
 available period of record for each of the 112 
 stations. The procedure followed was to obtain 
 for each profile the values of iV at a number of 
 standard height levels by assuming separate 
 exponentials for the dry and wet terms between 
 each reported level. The mean iV-profile for the 
 
DISCUSSION OF BASIC DATA 3 
 
 standard height levels was then computed as the 
 arithmetic mean of the individually interpolated 
 values of N at each standard height for all of 
 the profiles in the sample. In this way, 5-year 
 mean values of refractivity were obtained for 
 a large number of levels, ranging from the sur- 
 face to 30 km (or more) above the surface, for 
 a worldwide sample of stations. Over 18,000 in- 
 dividual values of mean N were determined in 
 this way. 
 
 In addition to these calculations, the approxi- 
 mate height of the tropopause was determined 
 for each profile ; this was the height of the bot- 
 tom of the lowest atmospheric layer which met 
 the following criteria: 
 
 (a) the layer thickness was ^ 2 km, 
 
 (b) the temperature gradient ^ -2°C/km. 
 The 2-km thickness could be made up of two or 
 more consecutive layers from the radiosonde 
 data. Mean monthly values of tropopause 
 heights were obtained for the period of record 
 for each station by averaging the individual 
 profile tropopause heights ; maps of these tropo- 
 pause heights are shown in appendix D. 
 
 The monthly mean value of the average N- 
 gradient between the surface and 1 km above 
 the surface has been recognized as a radio- 
 meteorological parameter of some importance 
 
 4 Data from these two publications (published by the National 
 Weather Records Center, Asheville, N.C., under the sponsorship of 
 
 the World Meteorological Organization [WMO] and the U.S. Weather 
 Bureau) will hereafter be referred to as "weather summary data." 
 
 [Saxton, 1951 ; Bean and Meaney, 1955 ; CCIR, 
 1965]. Therefore, maps are included of the 
 mean value of this parameter for all 12 months, 
 with a more comprehensive network of stations 
 than was available from the complete radio- 
 sonde data sample discussed above. For this 
 purpose a sample of 268 stations was chosen 
 from those available in the Monthly Climatic 
 Data for the World and the National Summary 
 of Climatological Data (U.S.A.). 1 These pub- 
 lications list monthly mean values of pressure, 
 temperature, and relative humidity or dew point 
 for the surface and some of the mandatory pres- 
 sure levels from radiosonde data. For stations 
 near sea level, the 900-mb level is very close to 
 1 km above the surface, but for most stations 
 outside of the U.S.A. the 850-mb level was the 
 lowest reported ; the altitude of this pressure 
 level varies between roughly 1.3 and 1.5 km 
 above sea level. However, it was felt that inter- 
 polation from the 850-mb data, using separate 
 exponentials for the wet and dry terms, would 
 yield fairly accurate values of monthly mean N 
 at the required 1-km height. 
 
 The radiosonde-derived data described in the 
 preceding paragraphs constitute the basic data 
 from which this atlas has been prepared. In the 
 following sections the methods of reduction and 
 presentation of these data are discussed, as well 
 as the consistency and reliability of the results 
 so obtained. 
 
3. World Maps of N(z) 
 
 3.1. Development 
 
 In order to prepare worldwide maps of upper 
 atmospheric N from the 5-year mean iV-profiles 
 described previously, it was decided to reduce 
 the quantity of necessary N (z) maps by ab- 
 stracting each mean profile in terms of a model 
 atmosphere which would use three negative ex- 
 ponential functions of altitude. The three func- 
 tions which are used to represent each mean pro- 
 file are a single exponential for the wet term, W, 
 and two exponentials for the dry term, D. Two 
 exponential functions are necessary for the dry 
 term because of the change in the lapse rate of 
 the temperature from the normal 6.5°C/km in 
 the troposphere to the nearly isothermal strato- 
 sphere where the temperature may increase 
 with height. Least-squares fits were obtained for 
 log W versus height over the interval to 3 km 
 above the surface. The ranges to be covered by 
 the two fits for the dry term were determined 
 from the mean tropopause heights and their 
 standard deviations which had been obtained 
 during the analysis of the N-profiles in each 
 sample. The tropospheric dry term, D 1} was fit 
 over the interval to the tropopause height 
 minus one standard deviation, and the strato- 
 spheric dry term, D- 2 , was fit from the tropo- 
 pause height plus one standard deviation to the 
 upper limit of data for that profile. In both 
 cases, log D was fit to height using least squares. 
 The resulting model atmosphere is given by 
 
 N(z) = D exp(~ -~- J 
 
 + f„ exp ( — jj?- J . 
 
 (3) 
 
 Zt, 
 
 N(z) = Do exp 
 
 Zt 
 
 (z-ZtY 
 
 H, 
 
 z >z t ; 
 
 Hi 
 
 + F exp (--!-), 
 
 (4) 
 
 D and W Q are the mean sea-level values of the 
 dry and wet terms (reduced from the surface 
 values using the free-atmosphere scale heights) , 
 H w is the wet-term scale height, H^ is the tropo- 
 spheric dry-term scale height, H 2 is the strato- 
 spheric dry-term scale height, and z t is the 
 altitude above mean sea level of the point of 
 transition between the tropospheric and strato- 
 spheric dry-term exponentials. The altitude, z t , 
 
 may thus be thought of as an effective density 
 tropopause. 
 
 Examples of the application of this model to 
 actual mean refractivity profiles are given in 
 figures 1 and 2: a very good fit (Koror) and one 
 of the worst fits encountered (Dakar). The 
 good fit obtained in figure 1 is especially signifi- 
 cant since Koror represents a climatic type 
 (equatorial station with a very high mean sur- 
 face refractivity, 387.6) for which exponential 
 models of N were previously thought to be un- 
 satisfactory [Mismeet al., I960]. Dakar (fig. 2) 
 is an example of the climatic type (character- 
 ized by a persistent low-level temperature in- 
 version with dry subsiding air above) where 
 this model (or any other simple model) of N 
 versus z is inadequate to explain the iV-structure 
 at low latitudes. It was found that the behavior 
 of the wet term (measured on figs. 1 and 2 by 
 S w , the rms error over the first 3 km) was a good 
 indicator of whether or not the data would pro- 
 vide a good fit to the profile. However, it can 
 be noted in figure 2 that, even though the rms 
 wet-term error below 3 km is 14.6 iV-units, the 
 profile at Dakar above an altitude of 6 km is 
 well represented by the three-part exponential. 
 
 Maps were prepared for each of the 4 "sea- 
 sonal" months of the parameters necessary to 
 utilize the three-part exponential in estimating 
 upper-air refractivity. These are the reduced- 
 to-sea-level values, Do and W ; the three scale 
 heights, H w , H lf and H 2 ; and the_transition alti- 
 tude, z t . The surface values of N can be recov- 
 ered by substituting the elevation of the surface 
 above sea level at -the desired location in (3), 
 which amounts to inverting the process used to 
 reduce the surface values of N to sea level. The 
 series of maps given in appendix A can be used 
 to estimate the mean value of N at any desired 
 altitude for each of the seasonal months at any 
 world location except those areas outlined in 
 figure A-30 (which summarizes the wet-term 
 rms error values found in figures A-26 through 
 A-29). 
 
 3.2. Discussion of N(z) Map Contours 
 
 The world maps of N (z) parameters reveal 
 a number of interesting trends. Some of these 
 are: 
 
 ( 1 ) The D x scale height, H, : 
 
 (a) is smaller than average over the arctic 
 seas in winter because of dense stratified air ; 
 
WORLD MAPS OF N(z) 5 
 
 (b) remains higher than average over land 
 areas during their warm seasons due to a steep 
 temperature lapse rate with height. 
 (2) The D 2 scale height, H 2 : 
 shows a minimum in the equatorial region 
 because of the colder temperatures found above 
 the tropical tropopause. 
 
 (3) The wet scale height, H w : 
 
 (a) is larger than average in the general 
 area of the equatorial heat belt during all sea- 
 sons. This indicates a steep temperature gradi- 
 ent with a very small lapse of absolute humidity 
 with height in the turbulently mixed deep layer 
 of warm air. However, in some tropical areas 
 
 
 400 
 iOO 
 
 200 
 
 100 
 
 90 
 
 80 
 70 
 60 
 
 SO 
 40 
 
 50 
 20 
 
 10 
 1 
 8 
 1 
 b 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 KOROR, CAROLINE ISLANDS 
 
 
 
 
 
 
 
 
 
 
 
 7°20'N, 134° 29' E, ELEV 29 m 
 
 
 
 
 
 
 
 
 
 
 
 MAY - 141 PROFILES 
 
 
 
 
 
 — SUM OF WET AND DRY EXPONENTIAL TERMS 
 
 
 
 
 N s = 38 7.6 
 
 
 
 
 /• 
 
 
 
 — N 
 
 
 
 
 
 
 
 
 
 $w T 
 
 t 0.27N 
 
 
 (. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 ^D, (EX 
 
 H, = 9. 
 
 P) 
 38 km 
 
 
 \ 
 
 fv S 
 
 <" 
 
 - D z (E 
 
 XR) 
 94 km 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 fcA 
 
 
 
 
 
 
 
 
 
 — 
 
 
 \ 
 
 
 
 
 
 \ 
 
 
 
 
 
 
 
 
 
 — 
 
 
 - V 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 - 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 r— W (EXP) 
 
 H w = 2.66 km 
 
 
 
 
 
 
 -^-D (N 
 
 
 
 
 
 
 Z 
 
 
 V 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Z T 
 
 
 \ / 
 V) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — W 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 
 o\ 
 
 
 
 
 
 
 
 
 
 < 
 
 \ 
 
 — 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 o 
 
 \ 
 
 
 
 
 
 
 
 
 
 
 k - 
 
 10 12 14 IE 
 
 Z - km 
 
 !0 11 24 26 21 iO 
 
 Figure 1. Three-part exponential fit to mean ~N-profile: Koror. 
 
 definite changes may occur in H w because of 
 seasonal shifting of small, but persistent, anti- 
 cyclonic circulations which modify to a consid- 
 erable vertical extent the normal zonal trans- 
 port of water vapor in those latitudes. The 
 seasonal differences of H w in the Coral Sea area 
 seem to confirm the existence of such a cellular 
 structure northeast of Australia [Hutchings, 
 1961]. 
 
 (b) is larger than average over two types 
 of convectively heated continental interiors: 
 
 (1) high-latitude land masses where the 
 sea-level wet term is less than 20 iV-units ; 
 
 (2) temperate desert steppe regions 
 where the sea-level wet term is between 20 and 
 60 N-units. 
 
 (c) is lower than average in areas where 
 subsidence or tradewind ducting persistently 
 occurs below 3 km. 
 
 (4) The dependence of the dry sea-level 
 values, D , upon temperature (because D = 
 77.6 P/T) is revealed in such features as the 
 332 high in Siberia during February and the 
 260 low in the Sahara Desert during May and 
 August. 
 
 (5) The wet sea-level values, W , are also 
 
6 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 very dependent upon temperatures because of 
 the larger water content possible at high tem- 
 peratures. There are two exceptions : 
 
 (a) Large interior deserts, where moun- 
 tains block the normal moisture flow, tend to 
 have low wet-term values relative to their tem- 
 peratures. 
 
 (b) In August, when the monsoonal mois- 
 ture is trapped south of the Himalayas, India 
 shows unusually high wet-term values. 
 
 (6) The intersection height, z t , is closely re- 
 lated to the height of the tropopause, but in 
 areas where an isothermal layer precedes the 
 stratospheric increase of temperature, the D 2 
 
 
 
 
 
 
 
 
 
 
 
 
 101) 
 
 
 
 
 
 
 
 
 
 OAKAR, SENEGAL 
 
 
 , ~s 
 
 
 
 
 
 
 
 I4°40' N, 17° 30' W, ELEV 40m 
 
 
 J00 
 
 
 
 
 
 
 
 
 
 MAY - 246 PROFILES 
 
 
 c\ 
 
 ^^SUM OF WET AND DRY EXPONENTIAL TERMS 
 
 
 
 
 
 N s = 357.5 
 
 
 200 
 
 /* 
 
 S/Sd / 
 
 ^N 
 
 
 
 
 
 
 
 
 
 
 
 
 
 XT 
 
 ^^x 
 
 
 
 
 
 
 
 
 
 
 
 
 
 100 
 90 
 SO 
 70 
 SO 
 50 
 
 ^D 
 
 (EXP) 
 •- 9 41 h 
 
 m 
 
 
 \ 
 
 x 
 
 ^-D 2 (E 
 
 (P.) 
 
 >.72 km 
 
 
 
 
 
 
 
 
 5 
 
 
 
 
 
 \ 
 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 
 
 
 \, 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 
 
 
 N\ 
 
 
 
 
 
 
 
 
 — 
 
 r 
 
 
 
 
 
 
 ^ 
 
 
 
 
 
 
 
 
 - 
 
 \ 
 
 
 
 
 
 
 
 >\ 
 
 
 
 
 
 
 
 ~ 
 
 \ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 40 
 
 \ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 °\ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 !() 
 
 v 
 
 _^--W (EXP) 
 
 
 
 2 
 
 
 
 
 
 
 
 
 
 m -\ 
 
 -n 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 20 
 
 o 
 
 U 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 5 
 
 r 
 
 
 
 
 
 
 
 
 
 
 
 
 
 10 
 
 9 
 
 
 1 
 
 > 
 
 o 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 ZA 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 i 
 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 V 
 
 
 
 
 
 
 
 
 
 
 
 
 — 
 
 
 
 
 \ 
 
 
 
 
 
 
 
 
 
 
 
 
 - 
 
 
 
 
 V 
 
 
 
 
 
 
 
 
 
 
 
 
 N 
 
 2 4 6 8 10 12 14 16 IB 20 22 24 26 28 30 
 
 Z - km 
 
 Figure 2. Three-part exponential fit to mean N-profile: Dakar. . 
 
 curve may intersect the D t curve as much as 2 
 km below the tropopause heights given in fig- 
 ures D-l through D-4 (see appendix D) . 
 
 3.3. Reliability of Contours of N(z) Maps 
 
 Although radiosonde stations are the only 
 worldwide source of upper -air meteorological 
 data, many areas of the world had few, if any, 
 radiosonde reporting stations before 1957. As 
 a result of the IGY, many new stations were 
 established, especially in the lower latitudes; 
 however, radiosonde data are still not available 
 
 for a number of large areas, such as Brazil, 
 China, and the Indian Ocean. High latitudes 
 also show a noticeable sparsity of upper-air 
 data; fortunately, there is a fairly small and 
 uniform transition in most parameters at these 
 far-south and far-north latitudes. Even in the 
 U.S.A., where the first radiosonde network was 
 established in 1938, radiosonde stations are still 
 several hundred miles apart. 
 
 The maps were hand-contoured by interpola- 
 tion between the widely-spaced plotted data 
 points, using a technique similar to that used 
 in the analysis of synoptic weather maps. Each 
 
WORLD MAPS OF N(z> 
 
 map was then carefully checked by another an- 
 alyst to make certain all data points had been 
 properly considered. The contours were modi- 
 fied in some areas on the basis of other informa- 
 tion or considerations not accounted for in the 
 machine-analyzed radiosonde data. For in- 
 stance, supplementary surface data [Knoll, 
 1941 ; Serra, 1955 ; Bean and Cahoon, 1957 ; 
 UNESCO, 1958; Bean et al., 1960b; Air Min- 
 istry, 1961 ; Dodd, 1965] were considered in the 
 contouring of those parameters (D Q and W ) 
 dependent upon surface observations. Also, if 
 spurious "high" centers of the wet term (such 
 as the isolated values found at Tananarive, 
 Malagasy Republic) were produced at high ele- 
 vation stations by reduction-to-sea-level pro- 
 cedures, these values were smoothed to some 
 extent. It was also found that the wet scale 
 heights derived from mean N-profile data for 
 stations at altitudes greater than 1 km tend to 
 give more unrealistic sea-level wet terms than 
 the average wet-term scale height of 3.0 km 
 suggested by Hann [List, 1958]. When this 
 "standard atmosphere" scale height was sub- 
 stituted for Hw, the maximum error of N (z) 
 values calculated from (3) or (4) for all sta- 
 tions above 1 km which are listed in table A-l 
 was 6.2 percent of the true 5-year mean value 
 at Tananarive in August; the second largest 
 error was 5.5 percent at Nairobi in February. 
 
 Another contouring check was made of all 
 modified contours ; a third analyst reviewed the 
 smoothing to be sure it was consistent with the 
 original plotted data as well as with the sup- 
 plementary information. 
 
 To further check the contouring, calculated 
 N(z) values (using (3) and (4) with values 
 read from figs. A-l through A-25) were com- 
 pared with actual observed values at 32 repre- 
 sentative stations. The results of this check 
 (reported in detail in table 2 of sec. 7) em- 
 phasize that, although some error undoubtedly 
 results in N(z) values below 1 km due to con- 
 touring, it is not a problem for N(z) values at 
 3 km or above. 
 
 3.4. Problem Areas of N(z) Maps 
 
 The use of wet and dry scale heights in a bi- 
 exponential radio refractive index model has 
 proved to be a good indicator of climatic differ- 
 ences [Bean, 1961; Misme, 1964]. The dry 
 term, or atmospheric density component of re- 
 fractivity, decreases with height in a uniform 
 manner throughout the troposphere so that its 
 scale height is an accurate indicator of the 
 degree of density stratification, but the water- 
 vapor component (the wet term) is not so well- 
 behaved. Because the saturation vapor pres- 
 sure, e s , is itself an exponential function of 
 temperature (which generally decreases linearly 
 
 with height), one of the best wet-term models 
 is probably an exponential curve [Reitan, 1963 ; 
 Dutton and Bean, 1965]. However, an exponen- 
 tial model of the wet term must be used with 
 discretion because humidity is extremely vari- 
 able, both vertically and horizontally (because 
 of its high dependence upon the temperatures 
 within the different air masses, as well as var- 
 ious terrain and land-water effects). 
 
 To show actual physical changes in H w , the 
 wet scale height, it would be desirable to pre- 
 sent contoured values based not only on a large 
 number of stations, but also on data representa- 
 tive of various times of day. Figures A-6, A-12, 
 A-18, and A-24 present the seasonal values of 
 H w , but worldwide maps of the diurnal vari- 
 ability of the wet scale heights are not yet avail- 
 able. 
 
 There are three specific areas of the world 
 where the assumption of an exponential distri- 
 bution of the wet term is largely invalid and can 
 be used only with reservations. Two of the 
 areas have one thing in common — a low sea- 
 level wet term. At continental stations in high 
 latitudes where strong temperature inversions 
 persist during winter months, the wet term at 
 3 km may be as large as, or even larger than, 
 that at the surface (because of the increase of 
 water vapor "capacity" with temperature) , and 
 the result is a negative or a very large positive 
 value of the wet scale height, neither of which 
 is physically realistic. At any tropical desert 
 station where the sea level wet term is < 30 
 N-units, deceivingly high wet scale heights also 
 may result. Fortunately, because of the small 
 contribution of the wet term in these cases, the 
 total iV-error remains small. The wet-term pro- 
 files at nine stations with low values of W„ were 
 examined, and the largest error at any height 
 was 3.7 percent of the true 5-year N (z) value 
 at Niamey (a desert station) in February (fig. 
 3) . In the arctic areas (represented by Barrow, 
 Alaska, in this same figure) the maximum error 
 never exceeded 1.5 percent of the total N(z) 
 value. 
 
 The third area presents a more serious prob- 
 lem because it exists in a subtropical climate 
 (15°-35° north and south of the equator) where 
 the wet term contributes from 14 to Vs of the 
 total N. The sharp decrease of humidity and 
 increase of temperature which is found in at- 
 mospheric layers between the surface and 3 km 
 in the subsidence regions of semipermanent sub- 
 tropical highs destroys the exponential distri- 
 bution of the wet term. In fact, in regions such 
 as this, the exponential fit may be valid only at 
 two or three points. This can be noted in figure 
 4, where the mean wet term for May is graphed, 
 and the H w value from the least-squares fit from 
 0-3 km of log W versus height is indicated, for 
 
8 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 four dissimilar stations: Dakar (low-level sub- 
 sidence), Antofagasta (intermediate subsid- 
 ence-trade wind ducting) , Hilo (trade wind in- 
 version), and Balboa (steep exponential gra- 
 dient) . A check of figure A-30 reveals that the 
 
 first three of these stations are located in areas 
 where, because the rms of the wet-term error 
 exceeds 5 N-units for at least 1 month, the three- 
 part exponential model is not recommended. 
 
 90 
 80 
 70 
 60 
 50 
 
 40 
 30 
 
 20 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 - NIAMEY £H W = 4.95) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 \ 
 
 \ 
 \ 
 
 
 
 
 
 
 
 \____ 
 
 ^-^ 
 
 *"--■■"■.. 
 
 
 
 
 
 10 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 5 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 IjO 1.5 2 2 5 
 
 HEIGHT ABOVE SURFACE IN KILOMETERS 
 
 FIGURE 3. Five-year mean wet refractivity term: February. 
 
WORLD MAPS OF N(z) 9 
 
 
 
 
 
 
 
 
 B4LB0i (tV'2 83) 
 
 '-^ 
 
 
 
 
 
 ^LO (H W :2.I6) 
 
 
 
 ^^^ 
 
 
 
 
 
 
 "V-^, 
 
 - 
 
 «• ^ 
 
 
 
 
 
 **■■ 
 
 '■-.. 
 
 ~~^^ 
 
 
 
 
 
 
 1 • 
 
 ~».. 
 
 ** ^^ 
 
 
 
 
 
 
 
 ■\ 
 
 "^^ 
 
 
 
 \ 
 
 
 
 
 s 
 
 *»«. 
 
 
 
 
 
 
 \ 
 
 \ 
 
 
 " - 
 
 
 
 
 
 
 \ 
 \ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 \^ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 10 IS 20 25 
 
 HEIGHT ABOVE SURFACE IN KILOMETERS 
 
 Figure 4. Five-year mean wet refractivity term: May. 
 
4. World Maps of aN 
 
 4.1. Development 
 
 Twelve maps of monthly mean AN were pre- 
 pared from the 5-year mean values obtained by 
 interpolation of mandatory pressure level radio- 
 sonde data for the 268 stations listed in table 
 B-l and located in figure B-l in appendix B. 
 These maps, contoured in the same manner as 
 the N(z) maps (sec. 3.3), are figures B-2 
 through B-13. 
 
 Previous work has shown that a good corre- 
 lation may exist between AN and surface N on 
 a monthly mean basis [Bean and Thayer, 1959 ; 
 Bean and Cahoon, 1961b; CCIR, 1965]. In fact, 
 in many areas of the world this seasonal corre- 
 lation is very high, and in such areas a regres- 
 sion line might provide better estimates of 
 monthly mean AN than the maps in appendix B 
 (if the mean value of surface N were available 
 for that particular month of that year for the 
 desired location). This regression line could 
 also be used with the N s distribution data from 
 table A-l to provide estimates of the AN distri- 
 bution. Correlations were therefore calculated 
 for the 12 monthly mean values of AN and sur- 
 face N for each of the stations in the sample. 
 The equations resulting from these calculations 
 were put into the form of deviations from the 
 annual means : 
 
 AN = b (N s - W s ) + aN ± S.E., (5) 
 
 where N s is the surface value of N, the single 
 bar represents a monthly mean value, the dou- 
 ble bar represents the annual mean, b is the 
 slope of the regression line, and S.E. is the 
 standard error of estimate. The equations were 
 put in this form because the intercept o f th e 
 
 equations in the ordinary form is equal to AN - 
 
 bN s , which is too unwieldy for contouring on 
 maps. Maps, which appear in appendix B, were 
 prepared of the slope (b) , the annual means 
 
 (aN and N ) , and the standard error of pre- 
 diction and correlation coefficient of the regres- 
 sion lines. 5 
 
 4.2. Discussion of Contours and Reliability 
 of a]V~ Maps 
 
 The world AN maps of this atlas do not show 
 as much detail as may be found in other publi- 
 cations which consider only specific areas [Bean 
 
 5 N is an approximate sea-level value of N s , defined by the equa- 
 tion N = N s e"- ,z , where z is the elevation above sea level in km. 
 
 et al, 1960b; du Castel, 1961; Rydgren, 1963; 
 CCIR, 1963]. It was necessary in this study to 
 omit some of the available radiosonde data in 
 areas with relatively dense weather networks 
 (e.g., the U.S.A. and Europe) in order to obtain 
 a more nearly uniform worldwide coverage. 
 Even with this coverage, the map scale size pre- 
 cluded the contouring detail which would be 
 necessary if localized terrain effects were to be 
 included ; for example, mountainous locations 
 (higher than 1 km) probably have lower values 
 of aN than those indicated in figures B-2 
 through B-13. Some dissimilarity in the con- 
 tour patterns between the maps in appendix B 
 and other AN maps may also be found because 
 of the differences in the time period used in the 
 samples ; such disagreements emphasize the 
 fact that 5 years of data are not adequate for 
 reliable means in many areas. 
 
 The map contours of worldwide AN indicate 
 that: 
 
 (1) Low values of AN are characteristic of: 
 
 (a) large desert and steppe regions such as 
 the Sahara, the Australian interior, the south- 
 western U.S.A., and the Asian region southeast 
 of the Caspian Sea all year ; 
 
 (b) high plateau areas during all seasons 
 except winter. 
 
 (2) High values of AN are found in: 
 
 (a) all areas where large masses of subsid- 
 ing air prevent the normal diffusion of water 
 vapor, creating an unusually large iV-gradient 
 between the moist surface air and the very dry 
 air at 1 km. Specific examples are : 
 
 (1) continental west coasts at latitudes 
 20°- 35° in the summer hemispher e an d 10°- 
 25° in the winter. In fact, the true AiV may be 
 higher than indicated on the maps at locations 
 such as Dakar, Senegal, where a very thick 
 (~250 m) surface or near-surface ducting 
 layer occurs much of the time ; 
 
 (2) tropical ocean areas where a trade- 
 wind inversion leads to a persistent elevated 
 ducting layer below 1 km. [Note : In a few cases 
 where the entire thickness of an elevated layer 
 lies between 1 km and the height of the 850-mb 
 level, the interpolation method gives a map 
 value which may be 5 to 10 N-units too high.] 
 
 (b) southeast Arabia and the Gulf of Per- 
 sia during July and August, when orographic 
 subsidence traps moisture from the southwest 
 monsoon in the gulf and lowlands between the 
 mountains. 
 
 10 
 
(c) Siberia and the Canadian interior in 
 winter because of the intense surface tempera- 
 ture inversion. 
 
 (d) the Mediterranean and Black Seas 
 during summer when convective mixing greatly 
 increases the near-surface humidity. 
 
 (e) India in the spring, when increased 
 heating over land produces a low-pressure re- 
 gion which leads to onshore winds and humid 
 
 WORLD MAPS OF AN 11 
 
 weather conditions until the onset of the mon- 
 soon. 
 
 In winter a combination of high and low 
 a2V values appear near the tip of southwest 
 Africa as the subsidence from the South At- 
 lantic High causes a large moisture gradient to 
 appear off the coast, whereas a small humidity 
 gradient is characteristic of the dry plateau 
 region inland. 
 
5. World Maps of Extreme N- Gradients 
 
 5.1. Development 
 
 The gradient of N near the surface of the 
 earth is of particular importance in many appli- 
 cations of telecommunications ; e.g., extreme 
 values of these initial gradients are responsible 
 for much of the unusual behavior of radio sys- 
 tems. Superrefractive gradients (defined here as 
 values between -100.0 TV/km and -156.9 N/km) 
 are responsible for greatly extended service hori- 
 zons, and may cause interference between widely 
 separated radio circuits operating on the same 
 frequency. Ground-based radio ducts (layers 
 having a negative gradient larger in absolute 
 value than 156.9 N/km) can cause prolonged 
 spacewave fadeouts within the normal radio ho- 
 rizon [Bean, 1954] and allow radar to track ob- 
 jects many hundreds of kilometers beyond the 
 normal radio horizon. On the other hand, subre- 
 fractive gradients (zero or positive gradients) 
 produce greatly reduced radio horizons, and 
 may result in diffraction fading on normally 
 line-of -sight microwave paths. 
 
 In the process of obtaining the mean 2V-profile 
 for each station and month, cumulative distri- 
 butions were prepared of the gradients occur- 
 ring between the surface and the 50-m and 
 100-m levels. Each gradient was calculated as 
 the simple difference between the surface N 
 and the value at 50 or 100 m above the surface, 
 divided by the height interval. For 99 out of the 
 112 stations in the mean N sample, cumulative 
 distributions were also prepared of the gradi- 
 ents and thicknesses of all observed ground- 
 based superrefractive or ducting layers, regard- 
 less of the thickness of the layer (except that 
 no layer less than 20 m thick was included, be- 
 cause the gradients obtained in such cases are 
 not considered to be reliable). In addition, 
 cumulative distributions were prepared of the 
 minimum trapped frequency for each of the ob- 
 served ducts in the sample. (This sample size 
 averaged 208 pieces of data for each month ; 
 for all months the smallest sample was 30 and 
 the largest, 620). The minimum trapped fre- 
 quency refers to the approximate lower limit of 
 frequencies that will be propagated in a duct in 
 a waveguide-like mode, and is given by [Kerr, 
 1951] 
 
 - 1.2 X 10 5 (c/s) ,_. 
 
 clz r 
 
 c/s, t is the total thickness of the duct in km, 
 dn/dz is the average gradient over the duct 
 
 (n/km) , and r is the radius of the earth in km. 
 Equation (6) is derived under the assumption 
 of a constant gradient throughout the duct, but 
 moderate departures from this assumption do 
 not seem to affect the results greatly. The / m in 
 corresponds to an absolute attenuation of the 
 guided energy of about 3 dB/km (5 dB/mile) 
 
 [Kerr, 1951]. 
 
 The maps in appendix C were prepared from 
 the cumulative distributions discussed above. 
 The distributions of gradients for the to 
 100-m layer were used to obtain maps of the 
 positive (subrefractive) gradient exceeded for 
 10 and 2 percent of the observations at any lo- 
 cation, and the percent of observations with 
 or positive iV-gradients. Maps were also pre- 
 pared of the extreme values of negative gradi- 
 ents observed ; these are referred to as "lapse 
 rates" of N (i.e., decrease with height, a term 
 normally used in referring to atmospheric tem- 
 perature gradients ; it is used here to avoid the 
 awkwardness of referring to a very strong nega- 
 tive gradient as being "less than" a given nega- 
 tive value) . Included in appendix C are maps 
 of the lapse rate of N exceeded for 25, 10, 5, 
 and 2 percent of the observations for the 100-m 
 layer. Cumulative probability distribution 
 charts of the gradients at 22 representative 
 world locations are also presented. 
 
 Other maps in appendix C were prepared 
 from the distributions of superrefractive and 
 ducting layer gradients, thicknesses, and /min 
 values for ducts. These include the percent of 
 time that the lapse rate of N in the ground- 
 based layer is equal to or larger than 100 N/km 
 and equal to or larger than 157 N/km (ducting 
 gradient) , the percent of superrefractive layers 
 that were more than 100 m thick, and the per- 
 cent of ducting layers that were more than 100 
 m thick (the last two refer to the percent of 
 thick layers out of the number of observed lay- 
 ers of that type). The distributions of /,„in 
 values were used to prepare maps of the per- 
 cent of all observations which showed ground- 
 based ducts having an / m in value of less than 
 3000 Mc/s, 1000 Mc/s, and 300 Mc/s. 
 
 5.2. Discussion of Gradient Map Contours 
 (Subrefraction) 
 
 where / min is the minimum trapped frequency in Ground-based subrefractive layers may be 
 
 12 
 
WORLD MAPS OF EXTREME N-GRADIENTS 13 
 
 found in the same tropical and subtropical loca- 
 tions as superref ractive layers, because a small 
 change in relative humidity at high tempera- 
 tures produces a very noticeable change in ab- 
 solute humidity, and the A/-change (either posi- 
 tive or negative) with height is highly depend- 
 ent upon the variation of absolute humidity. 
 For instance, subrefractive gradients occur 
 quite often during the afternoon at stations 
 which experience superrefraction or ducting 
 during the night and early morning. Other sta- 
 tions may have nocturnal subrefraction during 
 winter and superrefraction during the same 
 hours in summer. However, subrefraction, un- 
 like superrefraction, rarely occurs at surface 
 temperatures below 10 °C (the only exception 
 would be locations greater than 1 km above sea 
 level). 
 
 The surface conditions conducive to subre- 
 fractive gradients are of two rather opposite 
 types : 
 
 (a) temperature > 30°C; relative humidity 
 < 40 percent ; 
 
 (b) temperature 10° to 30°C ; relative humid- 
 ity > 60 percent. 
 
 Type (a) is usually found during the day- 
 light hours of months when intense solar heat- 
 ing occurs at warm, dry continental locations 
 and forms a very nearly homogeneous surface 
 layer (no decrease of density with height) 
 which may be several hundred meters thick. 
 Since a moist parcel of air is less dense than a 
 dry parcel at the same temperature and pres- 
 sure, the intense convection which occurs with- 
 in such a layer of absolutely unstable air tends 
 to concentrate the available water vapor near 
 the top of the layer, because a moist adiabatic 
 upper boundary is formed where the super- 
 adiabatic lapse rate changes abruptly to a sub- 
 adiabatic or very stable lapse rate. The result is 
 an increase (sometimes as large as 50 percent 
 of the surface value) with height of the wet 
 term through the ground-based layer. This in- 
 crease, coupled with no change in the dry term, 
 leads to a subrefractive (or positive) gradient. 
 
 This layer may retain its subrefractive na- 
 ture throughout the early evening hours at sta- 
 tions where conditions are favorable for the 
 development of a temperature inversion. As the 
 ground cools rapidly, the air very near the 
 ground cools and becomes more dense, but the 
 water vapor which is trapped between the two 
 stable layers causes the positive wet-term gradi- 
 ent from surface to the top of the original layer 
 to remain large enough to overbalance the 
 slightly decreasing gradient of the dry term. 
 This evening subrefraction is an outgrowth of 
 type (a) ; however, it may resemble type (b) 
 at the surface because it can be found with a 
 temperature as low as 20 °C and a relative hu- 
 midity as high as 60 percent. 
 
 Type (b) occurs most often during night and 
 early morning hours, and is characteristic of 
 coastal trade-wind and sea-breeze areas where 
 differential heating of land and sea results in 
 the advection of air which is warmer and more 
 humid than the normal surface layer. In this 
 type, both dry and wet terms may increase with 
 height, creating a surface layer of subrefrac- 
 tion which is generally more intense in gradient 
 than type (a) but not so thick. This form of 
 subrefraction might also be found for short 
 periods in any location where frontal passages 
 or other synoptic changes create the necessary 
 conditions. 
 
 Type (a) subrefraction is hard to evaluate 
 from figures C-l through C-4 because its per- 
 centage occurrence at any specific location is so 
 dependent upon the time of day represented by 
 the radiosonde data at that location. For in- 
 stance, because the local radiosonde observa- 
 tion times in the southwestern U.S.A. were 
 0800 and 2000 for the data period used in this 
 atlas, only the subtype (a) of evening subre- 
 fraction is recorded. Because conditions are 
 more suitable for inversions in February and 
 May, these months appear to have surface- 
 based subrefractive layers more often than Au- 
 gust. However, a detailed check of midafter- 
 noon observations near White Sands, N. Mex., 
 reveals that midday subrefractive conditions are 
 quite prevalent during much of August and Sep- 
 tember. The same diurnal problem is found in 
 northern Africa and the desert region south and 
 east of the Caspian Sea, where many of the sta- 
 tions take observations between 0300 and 0600 
 LST. Furthermore, even at those stations 
 which do have midday data, the "motorboating" 
 problem (i.e., humidities too low to be mea- 
 sured by the radiosonde — see sec. 5.5) during 
 the warmest seasons at very dry locations prob- 
 ably masks out a large percentage of subrefrac- 
 tive occurrences; e.g., the occurrence of subre- 
 fraction recorded in November and February 
 for the interior of Australia (where afternoon 
 observations are included) is probably too low. 
 
 Figures C-l through C-4 reveal that type (b) 
 subrefraction can be expected 10 to 20 percent 
 of the time in the western Mediterranean Sea 
 and the Red Sea area, and also in the Indone- 
 sian-Southwest Pacific Ocean region. These lo- 
 cations seem to indicate a slight seasonal trend, 
 with a higher probability of occurrence during 
 winter months. Another region with a 10 to 20 
 percent level of subrefractive gradient occur- 
 rence is the Ivory Coast and Ghana lowlands of 
 Africa where onshore winds prevail all year. 
 
 Occurrences of type (b) subrefraction exceed 
 5 percent at these locations and times of year: 
 
 (1) Southeast coast of U.S.A. all months; 
 
14 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 (2) Hawaiian Islands all months except 
 May; 
 
 (3) South Africa all months except Novem- 
 ber; 
 
 (4) Southeast coast of South America in No- 
 vember and February ; 
 
 (5) Southern California in November ; 
 
 (6) North Indian Ocean in May ; 
 
 (7) Isthmus of Panama in November. 
 
 5.3. Discussion of Gradient Map Contours 
 (Superrefraction and Ducting) 
 
 Superrefractive and ducting gradients in 
 ground-based layers are most often associated 
 with temperature inversions (temperature in- 
 creasing with height within the layer) , not only 
 because a positive temperature height-gradient 
 causes a negative iV-gradient, but also because 
 the low eddy diffusion qualities associated with 
 a temperature inversion often lead to a steep 
 negative gradient of humidity through the in- 
 version. However, previous investigations 
 [Bean, 1959] have shown that there are at least 
 two other typical situations encountered in the 
 formation of strong ground-based gradients : 
 The first of these is the arctic situation, where, 
 with surface temperatures below about — 20°C, 
 a strong temperature inversion (typical of con- 
 tinental arctic air masses) produces a superre- 
 fractive or ducting layer, while the vapor pres- 
 sure may actually increase with height. More 
 often, in this case, the wet term is negligible 
 throughout the layer. The second case is typical 
 of very humid tropical areas when the surface 
 temperature is 30 °C or greater. In these loca- 
 tions (which are usually coastal) a common 
 occurrence is a slight decrease of temperature 
 with height, accompanied by a very strong 
 lapse of absolute humidity. Such profiles may 
 show only a slight decrease of relative humidify 
 with height, but, because the saturation vapor 
 pressure is nearly an exponential function of 
 temperature, the resulting vapor pressure gra- 
 dient may be very large, thus causing a steep 
 ^-gradient. 
 
 Figures C-41, C-45, C-49, and C-53 show that 
 persistent ducting (D) or superrefractive (SR) 
 initial gradients can be found more than 25 
 percent of the time for at least two seasons in 
 seven general areas of the world : 
 
 (1) Dakar -Fort Lamy transitional strip in 
 Africa (D : all seasons) , 
 
 (2) northern Arabian Sea including coastal 
 areas of the Gulf of Aden and the Persian Gulf 
 (D : all seasons), 
 
 (3) India, Bay of Bengal, southeast Asia, 
 Indonesia, and north tip of Australia (SR: all 
 seasons), 
 
 (4) southwest coast of North America, in- 
 cluding portions of the North Pacific (SR : Feb- 
 
 ruary, May, November) , 
 
 (5) Gulf of Mexico and Caribbean region 
 (SR: May, August, November), 
 
 (6) northwest coast of Africa and western 
 Mediterranean (SR: May, August), 
 
 (7) Antarctica (D: May, August) . 
 
 Area (1) : Tropical west coast locations in the 
 vicinity of 15°-22°N or S are affected annually 
 by three or four latitudinal weather zones [Tre- 
 wartha, 1961]. In winter the Dakar-Fort Lamy 
 region is under the influence of dry anticyclonic 
 Saharan air, but even at the time of low sun, the 
 prevailing surface air movement is onshore 
 from the southwest. The vertical depth of this 
 maritime current is more shallow than in sum- 
 mer, but during the early morning hours, the 
 surface relative humidity is 80 to 90 percent 
 compared with 40 to 60 percent in the dry sub- 
 siding air above. Even with radiational cooling, 
 the night temperatures throughout the marine 
 layer (from 50 to 600 m thick) still remain over 
 20 °C. This combination of temperature and hu- 
 midity creates trapping conditions for frequen- 
 cies below 300 Mc/s about 30 percent of the 
 time in February (fig. C-31). 
 
 The weather zones advance rapidly north- 
 ward [Thompson, 1965], so that by July the 
 Dakar-Fort Lamy strip is in the wet tropical 
 regime associated with the fluctuating, unstable 
 Intertropical Convergence Zone (ITC). The 
 marine current of the westerlies becomes much 
 deeper, but the ducting layers are shallower 
 and can exist only intermittently between the 
 turbulent, showery periods common to the re- 
 gion. Figures C-22, C-24, C-26, and C-28 indi- 
 cate that more than 30 percent of the ducting 
 layers are over 100 m thick for all seasons ex- 
 cept summer. 
 
 Area (2) : The coastal areas of Arabia ex- 
 perience high surface humidities all year from 
 monsoonal and sea-breeze effects, but during 
 May and August these values are reinforced by 
 temperatures above 25 °C in a marine layer 
 which may extend up to a height of 800 to 900 
 m before it meets the overrunning dry north- 
 easterlies [Tunnell, 1964]. The percentage oc- 
 currence of ducts at Bahrain seems much high- 
 er than at Aden because all observations at 
 Bahrain were taken at 0300 LST (when the 
 surface humidity is at its maximum of 75 to 90 
 percent) , whereas the Aden observations, taken 
 twice a day, include as many observations at 
 1500 LST (when the relative humidity value is 
 much less) as at 0300 LST. For instance, 50 of 
 the 66 ducts recorded in August at Aden were 
 from early morning observations. However, the 
 fact that ducting gradients at Bahrain trap fre- 
 quencies below 300 Mc/s over 75 percent of the 
 time as compared to 5 percent at Aden (fig. 
 C-37) is due to another factor: the thickness of 
 the moist marine layer, when ducting is present 
 
WORLD MAPS OF EXTREME A/-GRADIENTS 15 
 
 at Bahrain, is greater than 300 m over half the 
 time. 
 
 Area (3) : A moist surface layer is also found 
 in the monsoonal areas. Its temperature is 25 
 to 30 °C and, during occurrences of ducting, the 
 surface relative humidity ranges from 85 to 100 
 percent, but the trapping incidence is much less 
 than in either area (1) or (2). The surface 
 layer is shallower and its gradient is less intense 
 because the humidity decrease between it and 
 the air mass directly above it is only 10 to 20 
 percent. Because brief periods of stable weather 
 occur even between surges of the summer mon- 
 soon, the ducting incidence remains over 10 per- 
 cent for all of area (3). 
 
 Area (U) '■ Along the western coast of North 
 America, from Southern California to Central 
 Mexico, the most important month for unusual 
 radio propagation due to surface conditions is 
 February, when frequencies below 300 Mc/s are 
 trapped 10 percent of the time. During the pe- 
 riod studied, 30 percent of the superrefractive 
 gradients were at least 300 m thick in all 4 
 months. Closer examination of the ducting 
 structure in Mazatlan reveals that if near-sur- 
 face layers (bases of 100-300 m) were included, 
 the percentage of occurrence would be increased 
 by 20 to 40 percent for all months except Au- 
 gust. During February, May, and November 
 the surface temperature of 20 to 30 °C remains 
 nearly constant through the ducting layer, but 
 the relative humidity decreases from a surface 
 value of 70 to 80 percent to values ranging from 
 20 to 40 percent. The dry air in the upper layer 
 results from subsidence in the eastern margin 
 of the Pacific high pressure cell, which shifts 
 northwestward in August, thus decreasing the 
 ducting incidence in Mazatlan but increasing it 
 in lower California and the Hawaiian Islands 
 (figs. C-21, C-23, C-25, C-27). 
 
 Area (5) : The center of most intense ducting 
 in the Caribbean Sea and Gulf of Mexico changes 
 with the seasons (figs. C-41 through C-56) . The 
 smallest percentage of superrefractive ground- 
 based gradients is found in February, with the 
 stronger gradients concentrated near the east 
 coast of Central America. By May the super- 
 refractive area has shifted eastward into the 
 Caribbean and northward into Florida. In Au- 
 gust it includes parts of the eastern U.S. but is 
 still most intense in the Swan Island area, and 
 in November the area encompasses all of the 
 Caribbean. The ground-based superrefractive 
 layers are thicker than 100 m approximately 
 70 percent of the year, but the ducting layers are 
 never intense enough to exceed the 1-percent 
 trapping level for 300 Mc/s. 
 
 Area (6) : The cause of superrefraction in the 
 western Mediterranean and northwest Africa 
 is very similar to that in area (4) . During the 
 summer season, subsidence along the eastern 
 
 edge of the Atlantic high-pressure cell superim- 
 poses a dry layer over the marine surface layer ; 
 during the winter season, the major subsidence 
 area shifts southward, the temperatures 
 throughout the surface layer are 5 to 10° C low- 
 er, and the percentage of superrefraction and 
 trapping incidence decreases. 
 
 Area (7) : During the long Antarctic night, in- 
 tense radiation from the snow-covered ground 
 keeps the surface temperature much lower than 
 that in the air several hundred meters above. 
 This temperature inversion of 10 to 25°C is the 
 cause of all the ducting gradients in May and 
 August, which trap frequencies <1000 Mc/s at 
 least 40 percent of the time (see appendix E). 
 
 5.4. Discussion of Cumulative Distributions 
 of Ground-Based Gradients 
 
 Data from 22 representative stations were 
 selected as a sample of the kinds of ground- 
 based gradient distributions from the surface 
 to 100 m which occur in various climates and 
 locations throughout the world. 
 
 Interesting similarities which exist among 
 the gradient distributions imply that the re- 
 fractivity climate of any station may be related 
 more to the season or month of the year than to 
 any particular latitudinal location. For in- 
 stance, consider the interesting relationships 
 between Bangui (a tropical station), Bordeaux 
 (temperate), and Amundsen-Scott (arctic). 
 The gradient distribution at Amundsen-Scott 
 in February resembles that of Bordeaux in 
 February, but its August distribution slope re- 
 sembles that of Bangui in May. However, Ban- 
 gui's distribution slope and range in August 
 resembles Bordeaux in May. Amundsen-Scott 
 forms another interesting climatic triad with 
 Saigon and Long Beach. In August the distri- 
 bution slope and range of Amundsen-Scott is 
 very similar to that of Saigon (a tropical sta- 
 tion), but the negative gradient intensity is 
 about 100 iV-units greater at all percentage lev- 
 els. However, Saigon in May, before the mon- 
 soon, resembles Long Beach in February. 
 
 It was expected that a pronounced diurnal 
 effect would exist in the gradient structure near 
 the surface, so two stations, Aden, Arabia, and 
 Nicosia, Cyprus, where data were available at 
 two thermally opposite times of day — 2 and 3 
 a.m. and 2 and 3 p.m. (0000 and 1200 GMT 6 ) — 
 were studied. Figures C-57 and C-71 in appen- 
 dix C show the diurnal differences in the cumu- 
 lative distribution of initial gradients from 
 to 100 m for these two stations for the 4 months 
 studied. 
 
 Superrefractive conditions normally accom- 
 pany nocturnal inversions. At Nicosia this 
 
 6 GMT (Greenwich Mean Time) is the same as UT (Universal 
 Time). 
 
16 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 proves to be the case for all seasons, with a very- 
 definite maximum in August, when anticyclonic 
 upper air circulation intensifies the humidity 
 decrease aloft and radiational cooling lowers the 
 surface temperature 15 °C below that found 
 during the day. 
 
 Aden, a coastal station with less change in its 
 diurnal temperature cycle than Nicosia (an in- 
 terior valley station on a fairly large island), 
 exhibits superrefraction day and night for all 
 
 seasons. During August and November initial 
 gradients have a wide range of values, with the 
 largest variation occurring at 0000 GMT, but 
 in February and May the nocturnal stability 
 apparently is seldom destroyed by convective 
 mixing, and the 1200 GMT (1500 LST) initial 
 ref ractivity gradient may exceed the 0000 GMT 
 (0300 LST) gradient. However, the early 
 morning inversion is usually more significant 
 from a radio-meteorological viewpoint because 
 
 5 i 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 ADEN - MAY 
 
 OOOO GMT, H w =l.97, H|=9.95 
 
 1200 GMT, H W =2.08,H,=9.47 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 \\ 
 \\ 
 
 1 
 
 \ 
 
 \ 
 
 
 
 
 
 
 
 
 
 
 
 
 \ 1 
 
 \l 
 
 
 s V 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 >«. 
 
 — 
 
 
 
 
 
 
 
 
 
 -10 
 
 ■50 -60 -70 
 
 N-GRADIENT 
 
 -110 
 
 -120 
 
 Figure 5. Five-year mean vertical refractive gradient profile: Aden. 
 
 the refractivity gradient is much more intense 
 from 250 m to 750 m, thus affecting more radio 
 frequencies. This can be noted in figure 5, rep- 
 resenting a 5-year mean of the vertical gradient 
 observed from to 4.5 km during May. 
 
 Figure 6 presents the same data for Nicosia 
 during a 5-year August period. Because scale 
 heights are also a measure of stability and strat- 
 ification, figures 5 and 6 not only give the differ- 
 ences in the mean total N-gradient values in the 
 lower atmosphere, but also the wet (H w ) and 
 dry (.Hi) scale heights. Five-year mean values 
 of N s at Aden in May were found to be 382 
 
 (surface wet term of 123) at 0000 GMT and 
 374 (surface wet term of 119) at 1200 GMT. 
 The corresponding values at Nicosia in August 
 were 341 (wet term of 83) and 310 (wet term 
 of 62). 
 
 The percentage of occurrence of subref raction 
 (A7km>0) is larger at night (0000 GMT) in 
 February and November at both locations. 
 However, this diurnal trend is much more pro- 
 nounced at Nicosia, particularly in November 
 when the percentage of night subrefraction is 
 over 30 percent larger than the daytime per- 
 centage of occurrence. 
 
WORLD MAPS OF EXTREME N-GRADIENTS 17 
 
 5.5. Reliability and Limitations of 
 Ground-Based Refractivity Gradient Data 
 
 Because ground-based gradients are so sen- 
 sitive to local effects, such as terrain and land- 
 water relationships, it was impossible to contour 
 figures C-l through C-56 for individual small 
 areas. For instance, although Madrid (on the 
 high interior plateau of Spain) experiences 
 little ducting during the year, it is surrounded 
 
 by areas of high ducting incidence, and no at- 
 tempt was made to delineate this small region 
 of nonducting. Also, refractivity gradients cal- 
 culated from radiosonde observation levels sep- 
 arated by less than 20 m may be seriously affect- 
 ed by instrumental errors, so ground-based lay- 
 ers less than 20 m thick were not included in 
 the analysis. Consequently, very shallow duct- 
 ing (such as that found at certain times over 
 oceans, under dense jungle canopies, and in 
 
 X 
 
 o 
 
 LU 
 
 X 
 
 -40 
 
 -50 -60 -70 
 
 N-GRADIENT 
 
 90 
 
 ■110 
 
 -120 
 
 FIGURE 6. Five-year mean vertical refractive gradient profile: Nicosia. 
 
 mountain valleys) is not included in the con- 
 toured data; however, such layers may be in- 
 tense enough to create trapping conditions for 
 frequencies down to 600 Mc/s [Jeske, 1964; 
 Baynton et al., 1965 ; Behn and Duffee, 1965]. 
 
 The time of day represented by the available 
 observational data must also be considered for 
 any variable which has a definite diurnal trend. 
 Therefore, for a true comparison of worldwide 
 gradient behavior, it would be desirable to use 
 comparable data recorded at least twice a day 
 at standard local or sun-referenced time. How- 
 
 ever, because simultaneous data are needed for 
 the preparation of synoptic maps, all stations 
 in the U.S.A. and many in the European coun- 
 tries schedule radiosonde observations at 0000 
 GMT and 1200 GMT. Many stations in other 
 parts of the world take only one observation per 
 day (usually at either 0000 GMT or 1200 GMT, 
 but there are exceptions, e.g., 0600 GMT at 
 Abidjan, Dakar, and Niamey). Even if all sta- 
 tions had a common GMT hour for taking ob- 
 servations, the diurnal problem would still exist 
 because the local time for any designated GMT 
 
18 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 time would be distributed throughout the day as 
 one traversed the globe. For instance, the fol- 
 lowing stations (in tropical areas where the 
 occurrence of either subrefractive or superre- 
 fractive layers is especially dependent upon the 
 time of day) were used in this report: 
 
 Station 
 
 Local time GMT 
 
 *Aden, Federation of South 
 
 0300 
 
 0000 
 
 Arabia 
 
 
 
 * Curacao, Netherlands 
 
 2000 
 
 0000 
 
 Antilles 
 
 
 
 Fort Lamy, Republic of Chad 
 
 0100 
 
 0000 
 
 *Hilo, Hawaii 
 
 1400 
 
 0000 
 
 Lae, Territory of New Guinea 
 
 1000 
 
 0000 
 
 Majuro Island, Marshall 
 
 1200 
 
 0000 
 
 Islands 
 
 
 
 Singapore 
 
 0700 
 
 0000 
 
 Those stations marked with an asterisk also 
 take observations at 1200 GMT. However, when 
 evaluating the apparently low level of duct oc- 
 currence at some locations (e.g., Majuro) and 
 high occurrence at others (e.g., Fort Lamy), 
 and when checking the subrefraction occurrence 
 at warm, dry continental locations, such as Ni- 
 amey (where no midday observation is taken), 
 the local time of the radiosonde ascent should be 
 considered. 
 
 In addition to the spatial and temporal limita- 
 tions imposed by the use of available radiosonde 
 data, there are instrument recording limitations 
 (see sec. 2) which must be considered when 
 evaluating iV-gradients. Although the alternat- 
 ing sequence system of observing the humidity 
 and temperature can put a lower limit on the 
 thickness and thus mask the true gradient of 
 atmospheric layers which can be detected by 
 radiosonde, the response of the radiosonde tem- 
 perature and humidity elements is a more se- 
 
 rious problem in the measurement of the in- 
 tensity and number of superref ractive gradients 
 at or near the surface. For example, in typical 
 ducting situations during May in a tropical 
 (Saigon) and in a temperate climate (Bor- 
 deaux), correction of both humidity and tem- 
 perature sensor time lags as suggested by Bean 
 and Dutton [1961] would intensify gradients of 
 —293 N/km (Saigon) and —212 N/km (Bor- 
 deaux) to —377 N/km and —362 N/km, re- 
 spectively. This type of correction also would 
 have increased the percentage occurrence of 
 superrefractive and ducting gradients in the 
 majority of cases. Such extensive recalcula- 
 tions were not possible in this study, but the 
 possibility that more intensive gradients may 
 occur in larger percentages at some locations 
 (particularly in temperate, humid climates) 
 should be kept in mind when applying values 
 obtained from any of the figures in appendix C. 
 Another limitation which applies primarily to 
 the detection of subrefractive layers (figs. C-l 
 through C-12) in hot, dry regions is the high 
 electrical resistance of the lithium chloride hu- 
 midity element at very low humidities which 
 causes open-circuit signals ("motorboating"). 
 At stations such as Aoulef, Algeria, where the 
 daytime surface temperature often exceeds 
 30 °C, the relative humidity may be below the 
 motorboating boundary at all levels from sur- 
 face upward, and all relative humidity values 
 (except the surface) are estimated, usually in 
 values which are equal to, or less than, the sur- 
 face value. However, it is quite probable in 
 these highly convective conditions that the abso- 
 lute humidity remains constant with height, 
 instead of rapidly decreasing (as the estimated 
 relative humidity values would indicate) . If it 
 did remain constant, fairly persistent subre- 
 fractive gradients would be found in such areas 
 during the hours of most intense solar heating. 
 
6. World Maps of Mean Tropopause Altitudes 
 
 Maps have been prepared of the mean tropo- 
 pause altitudes which were calculated in the 
 course of obtaining the mean iV-profiles for the 
 112 station sample, as discussed previously. The 
 maps, for the 4 "seasonal" months, are shown 
 in appendix D. The zone of maximum tropo- 
 pause altitudes for each month seems to corre- 
 spond quite well with the mean position of the 
 Intertropical Convergence Zone. 
 
 As stated earlier, the criterion for determin- 
 ing the tropopause altitude for each radiosonde 
 ascent was the altitude of the base of the first 
 layer or layers which had a total thickness of 
 at least 2 km and a temperature lapse rate of 
 less than 2°C/km. The mean tropopause alti- 
 tude for each station and for each month was 
 
 determined by a simple average of all of the 
 individual values for the profiles in the sample 
 (usually of 5 years' length). The reliability or 
 consistency of these maps is difficult to assess, 
 since the results of determining tropopause alti- 
 tudes depend to a great extent on the criteria 
 used for selection of the first stratospheric layer. 
 The criterion used here is the one in most com- 
 mon usage [U.S. Weather Bureau, 1964], but 
 other criteria may be applicable where the re- 
 sults are intended for use in specific atmospheric 
 problems. These maps supplement tropopause 
 data presented in other reports and atlases 
 [For example, Willett, 1944 ; U.S. Navy, 1955- 
 59 ; Smith, 1963 ; Smith et al., 1963 ; Kantor and 
 Cole, 1965]. 
 
 19 
 
7. Appraisal of Results 
 
 7.1. Accuracy of N(z) Maps 
 
 The general accuracy of the 5 - year mean 
 values used in the N(z) study was checked by 
 computing the standard deviation of the year- 
 to-year monthly means and dividing by the 
 square root of 5. This should be a good estimate 
 of the rms (standard) error of the 5-year mean 
 values as compared to the true long-term mean 
 (assuming there are no trends in the data). 
 Table 1 shows the estimated standard error of 
 the 5-year mean N s values for 40 stations, ar- 
 ranged by climatic classification. The percent- 
 age errors should be similar for the N (z) pa- 
 rameters (with the possible exception of scale 
 heights) at various altitudes. The combined 
 (rms) standard error of 5-year mean N s for the 
 40 stations for February and August was 2.37 
 A/-units, or about 0.7 percent of the mean N s . 
 It is significant that even the standard 30-year 
 period recommended (e.g., by the WMO) for 
 standard climatological normals would have a 
 nominal standard error of about ± 1.0 A/-unit 
 (2.37 divided by \/6), or about 0.3 percent of 
 mean N s values. 7 The 30-year means would thus 
 
 Table 1. Standard errors of 5-year mean values of monthly 
 mean Ns for 1^0 stations. 
 
 
 
 
 
 12-month 
 
 12-month 
 
 
 
 
 
 estimate 
 
 rms as 
 
 
 Number 
 
 Febru- 
 
 August* 
 
 (rms of 
 
 percent- 
 
 Climatic type 
 
 of 
 
 ary* 
 
 (/V-units) 
 
 Feb. and 
 
 age of 
 
 
 stations 
 
 (N-units) 
 
 
 Aug.) 
 (TV-units) 
 
 mean 
 
 Arctic 
 
 2 
 
 1.6 
 
 0.6 
 
 1.2 
 
 0.4 
 
 Subarctic 
 
 2 
 
 1.2 
 
 2.8 
 
 2.1 
 
 0.7 
 
 Marine west 
 
 
 
 
 
 
 coast 
 
 4 
 
 1.5 
 
 1.9 
 
 1.7 
 
 0.5 
 
 Marine (ships) 
 
 4 
 
 2.2 
 
 1.5 
 
 1.9 
 
 0.6 
 
 Continental 
 
 
 
 
 
 
 (cool) 
 
 2 
 
 1.0 
 
 2.9 
 
 2.2 
 
 0.7 
 
 Continental 
 
 
 
 
 
 
 (warm) and 
 
 
 
 
 
 
 subtropical 
 
 3 
 
 2.2 
 
 1.6 
 
 1.9 
 
 0.6 
 
 Semiarid cool, 
 
 
 
 
 
 
 high altitude 
 
 2 
 
 1.4 
 
 3.5 
 
 2.7 
 
 1.0 
 
 Arid and semi- 
 
 
 
 
 
 
 arid tropical 
 
 8 
 
 2.4 
 
 3.8 
 
 3.2 
 
 0.9 
 
 Monsoon 
 
 3 
 
 3.2 
 
 1.3 
 
 2.5 
 
 0.7 
 
 Equatorial 
 
 6 
 
 (seaso 
 appli< 
 
 ns not 
 :able) 
 
 2.7 
 
 0.7 
 
 All (rms of 
 
 
 
 
 
 
 above) 
 
 40 
 
 — 
 
 — 
 
 2.37 
 
 0.7 
 
 *For stations in the Southern Hemisphere, months were 
 reversed (February was combined with August for northern 
 stations, etc.). 
 
 1 Thirty-year means are used because there are long-term trends 
 in most climatological series ; thus a standard period is desirable for 
 comparison between stations. 
 
 have an advantage of only about 50 percent in 
 rms error, as opposed to the 5-year means 
 actually used. 
 
 The overall accuracy of the three-part expo- 
 nential model was checked in two ways. First, a 
 che ck w as made of the accuracy of recovering 
 the AA7 values using the three-part exponential. 
 Here the value of AN was calculated, using the 
 wet- and dry-term tropospheric exponentials, 
 and th e va lue obtained was compared with the 
 actual AA7 from the mean A7-profile. Figure 7 
 shows the results of such a comparison for 95 
 of the 112 stations in the original sample for 
 which coincident data of s ever al types were 
 available. The true value of AN from the mean 
 A^-profile is the dependent variable, and the 
 value recovered from the wet and dry expo- 
 nentials is the inde pend ent variable. The rms 
 error in recovering aA7 was 9.2 N-units ; how- 
 ever, if those stations (points shown as crosses 
 on fig. 7) which are in areas where the three- 
 part exponential model is of questionable valid- 
 ity (as shown in fig. A-30) are eliminated from 
 the sample, the rms error is reduced to 6.4 
 AZ-units. The regression line shown in figure 7 
 is for this reduced sample. The deviation of the 
 regression line from the 45° line (labeled "per- 
 fect agreement" in fig. 7 : zero intercept and 
 unity slope) is significant at the 5-percent level ; 
 thus it would appear that this is not the best 
 usage for t he three-part exponential model. Use 
 of the AA7 maps in appendix B is recommended 
 rather than the N (z) maps, for this purpose. 
 
 The second check was to use the N(z) maps 
 to recover the values of N(z) for some of the 
 actual station locations, at different heights 
 above the surface, and compare these with the 
 actual values of mean N (z) . This would be a 
 check not only on the three-part exponential 
 model but also upon the contouring process. 
 Table 2 shows the results of such an error an- 
 alysis. 
 
 Thirty-two of the original 112 stations were 
 selected on an areal basis, and the correspond- 
 ing three-part exponential model was construct- 
 ed for each of these stations, for all 4 months, 
 using the maps in appendix A. These exponen- 
 tial models were then used to calculate N (z) for 
 three heights (3, 8, 16 km) for each month, and 
 the results were compared with the actual mean 
 A/-profiles. The mean and maximum values of 
 the absolute errors thus derived are shown in 
 table 2. Stations and seasons in this sample 
 
 20 
 
APPRAISAL OF RESULTS 21 
 
 which are characterized by a high (tropical- 
 type) tropopause showed larger errors at 16 
 km than at 8 km, the reverse of the usual trend 
 
 in table 2. It is apparent from inspection of 
 table 2 that errors in recovering N (z) at alti- 
 tudes of 3 km or more are likely to be small. 
 
 POINTS AFFECTED BY LOW-LEVEL SUBSIDENCE 
 
 REGRESSION LINE 
 
 AN M • 14 64 + 6437 AN t ± 6 40 
 
 r = 795 
 
 50 60 70 80 90 100 
 
 AN S FROM EXPONENTIAL MODEL 
 
 Figure 7. Correlation of AN; monthly mean N-profiles versus monthly 
 mean exponential model. 
 
 Table 2. Absolute errors in recovering mean N from map 
 contours for 32 stations (in N-units) . 
 
 Month 
 
 3 km 
 
 X km 
 
 16 km 
 
 Mean 
 
 Max 
 
 Mean 
 
 Max 
 
 Mean 
 
 Max 
 
 Feb. 
 May 
 Aug. 
 Nov. 
 Year 
 
 1.0 
 1.3 
 1.4 
 2.0 
 1.4 
 
 2.1 
 3.2 
 2.1 
 4.0 
 4.0 
 
 1.6 
 
 1.4 
 1.9 
 1.3 
 1.6 
 
 3.6 
 3.3 
 
 3.7 
 3.0 
 3.7 
 
 0.6 
 0.9 
 0.8 
 1.2 
 0.9 
 
 1.7 
 2.3 
 1.6 
 3.0 
 3.0 
 
 The total variance, o- T 2 , in using the maps of 
 N(z) given in appendix A can be estimated in 
 terms of the following error model : 
 
 <r T 2 as o- 5 2 + ff M 2 f (7) 
 
 where ff 5 2 is the variance of 5-year mean values 
 (as compared to long-term means), o- M 2 is the 
 variance of errors in mapping N(z). Random 
 
 instrumental errors are included in *•?. The 
 value of °" 5 can be estimated at about 2.5 AZ-units 
 (table 1), while °m may be estimated at 1.5 x 
 P.E., where the probable error, P.E., is given 
 approximately by the mean absolute errors in 
 table 2. These would yield 2.1 N at 3 km, 2.4 N 
 at 8 km, and 1.4 N at 16 km, for <*m. A reason- 
 able estimate for °m at the surface (0 km) 
 would be 1.0 iV-unit. These estimates may be 
 combined to yield approximate a r values, as 
 shown in table 3. Minimum and maximum val- 
 ues were obtained by permutations of the. values 
 in tables 1 and 2. The standard errors of the 
 5-year means have been assumed to be a con- 
 stant percent of mean N(z), independent of 
 altitude. 
 
22 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table 3. Approximate total standard errors, o'T, for N(z) 
 maps in appendix A. 
 
 Average 
 
 Minimum 
 
 Maximum 
 
 3 km 
 
 km 
 
 16 km 
 
 In terms of N-units 
 
 2.6 
 
 1.9 
 
 1.8 
 
 1.4 
 
 1.3 
 
 1.3 
 
 4.3 
 
 2.8 
 
 2.2 
 
 As percent of mean N(z) 
 
 1.0 
 0.6 
 
 1.3 
 
 Average 
 
 0.8 
 
 1.1 
 
 1.5 
 
 2.5 
 
 Minimum 
 
 0.4 
 
 0.7 
 
 1.1 
 
 1.6 
 
 Maximum 
 
 1.3 
 
 1.6 
 
 1.9 
 
 3.3 
 
 Mean N(z) 
 
 320 
 
 175 
 
 117 
 
 38 
 
 It is probable that the percent errors given in 
 table 3 do not increase materially above 16 km ; 
 asymptotic values of 3, 2, and 4 percent for 
 average, minimum, and maximum relative 
 standard errors are probably good estimates for 
 altitudes from 20 to 30 km, while above 35 km 
 the standard errors are probably no more than 
 0.2 N-units. 
 
 There are also undoubtedly some bias errors 
 involved in the N (z) maps, although they are 
 probably quite small. Sources of these bias er- 
 rors would include the equation for A7 itself 
 (Smith-Weintraub formula) , the radiosonde in- 
 
 perfect agreement- 
 
 FIGURB 8. Correlation of AN: monthly mean N-profiles versus monthly 
 mean weather summary data. 
 
 strument (where the sensor lag would seem to 
 assure a slight positive bias for all upper-air N 
 values, even over long-term means) , and certain 
 peculiarities in the mapping and curve-fitting 
 procedures which might produce bias for some 
 values of z and not others (e.g., the bias due to 
 the sharp "knee" of the -D,. and D 2 exponential 
 terms at the A7-tropopause as compared with 
 
 the smooth transition of real mean AZ-profiles; 
 note example in fig. 2) . It should be noted that 
 this last type of bias error was (automatically) 
 included in the mean absolute errors given in 
 table 2, since there was no easy way of separat- 
 ing this type of error from the others. Hence 
 table 3 includes an allowance for this particular 
 source of bias error. 
 
APPRAISAL OF RESULTS 23 
 
 7.2 Accuracy of AN Maps 
 
 As a check on the method used to calculate the 
 
 AN values from which the maps in appendix B 
 were derived, the AN values from the weather 
 summary data were obtained for all 90 of the 
 stations which were also contained in the mean 
 N-profile sample ; the AN values for the months 
 of February, May, August, and November were 
 then compared with the corresponding values 
 from the actual mean N-profiles. However, the 
 period of record involved in the mean N-profile 
 study was not, in general, even partially coin- 
 cident with the 1960-64 period used for the AN 
 study. Thus it was expected that the variance, 
 V 2 , o f the differences between the weather sum- 
 mary AN values (from which the maps given in 
 appendix B were obtained) and the AN values 
 from the mean N-profiles would have two com- 
 ponents, 
 
 <r D 2 = o-r 2 + <V, (8) 
 
 where a R - is the variance of the real difference 
 in the two 5-year mean values of AN (because 
 they are obtained from different time periods), 
 and a E~ is the variance of the differences which 
 are caused by the interpolation er rors inherent 
 in the method used to obtain the AN from the 
 weather summary data. Since the real differ- 
 ences (represented by °r) would be expected to 
 have a near-zero mean for a worldwide data 
 sample, a regression analysis of the two types 
 of AN values should reveal any bias which had 
 been p rod uced by the interpolation method used 
 in the AN study. Figure 8 shows the results of 
 such a comparison, with AN from the m ean 
 N-profiles as the dependent variable, and AN 
 from the weather summary data as the inde- 
 pendent variable. There is no statistically sig- 
 nificant bias shown, since the regression line is 
 almost identical with the "perfect agreement" 
 line (zero intercept and slope of unity). 
 
 An evaluation of the relative sizes of ^r and °e 
 was made by calculating the rms difference be- 
 tween the two types of AN values for a restrict- 
 ed sample containing only those stations where 
 the mandatory pressure level used to calculate 
 the AN values from the weather summary data 
 was within ± 100 m of 1 km above the surface ; 
 the rms difference thus calculated was 3.9 N- 
 units. Since any interpolation errors would be 
 expected to be quite small for this restricted 
 sample, it was concluded that the 3.9 N-unit 
 rms represented essentially the value of ff R as 
 given in (8). 
 
 The value of <^ D as given in figure 8 is 5.2 
 N-units ; thus by (8) the value of ° E is probably 
 on the order of 3.5 N-units. This should be a 
 good approximation to that part of the overall 
 rms error in the AN maps which is assignable 
 
 to the interpolation method used on the weather 
 summary data. 
 
 The overall accuracy of the AN maps given in 
 figures B-2 through B-13 depends on several 
 factors : the accuracy of the interpolation meth- 
 od, the variability of the 5-year mean period as 
 compared with a standard WMO 30-year mean 
 period, and the heterogeneous nature of the 
 local observation times included in the data 
 sample. The weather summary data were most- 
 ly derived from observations taken at 0000 
 GMT, although many stations in different parts 
 of the world supplied data taken at 1100 or 1200 
 GMT, while others supplied data averaged at 
 two, or in a few cases four, times per day. In 
 this study no attempt was made to correct for 
 diurnal variations imposed by the fixed observa- 
 tion times of the various stations ; hence diurnal 
 variability must be added to the sources of pos- 
 sible error in the maps. It is reasonable to 
 assume that the actual (unknown) standard 
 error of the maps is not independent of the true 
 value of monthly mean AN desired, but that it 
 is more likely a certain percentage of the true 
 value. Since the overall correlation between the 
 contoured and true values is probably quite 
 high, it is plausible to estimate the standard 
 error of the maps as a percentage of the con- 
 toured values. On figure 8 it is found that an 
 allowance of ± 10 percent from the perfect- 
 agreement line (in the vertical) will exclude 
 only 23 percent of the 360 data points (40 above 
 the limits, and 44 below the limits) ; for a nor- 
 mal distribution, 32.5 percent of the points 
 should be excluded by the standard error limits. 
 Therefore, allowing for some added error from 
 diurnal variability, it seems reasonable to esti- 
 mate the overall standard error of the maps of 
 AN as approximately 10 percent of the con- 
 toured values. 
 
 This is equivalent to assuming an error model, 
 
 = a -r + a E 2 
 
 (9) 
 
 where the terms have the same meaning as 
 those in (7) and (8), with * 6 2 = ° R 2 (possibly 
 as low as V£ a R 2 ) in (8), and where V is the 
 variance assignable to diurnal variations in AN. 
 The value of <r t should be on the order of 2 to 4 
 N-units, which is an estimate based on inspec- 
 tion of the CCIR maps [CCIR, 1965] . 
 
 There is doubtless some bias error in the AN 
 maps; the discussion given for bias errors in 
 the N (z) maps is mostly applicable to the AN 
 maps. Here the bias due to the radiosonde is 
 relatively larger, because of the differencing 
 used to obtain AN values. This mean bia s error 
 may be as high as 1 percent of the AN values, 
 but data adequate for checking on this possi- 
 bility are not available. 
 
24 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 7.3. Accuracy of Gradient Maps at widely differing local times. In line with the 
 It is very difficult to assess the probable errors results of the AN error analysis, an overall 
 in the maps of the different kinds of initial error of about 15 percent of the contoured val- 
 gradients given in appendix C. The primary ues seems reasonable, but may be higher or 
 reason for this is that no data are available ex- lower, depending on the area being considered, 
 cept those used to prepare the maps. It is likely The maps in appendix C are probably best suit- 
 that the most serious source of discrepancies ed for the depiction of climatic tendencies of 
 will arise because of the admixture of data taken subref raction and superref raction. 
 
8. Conclusions 
 
 It seems clear that the most significant con- important as obtaining larger samples. The 
 
 elusion which can be drawn from this study effects of such an analysis on the results given 
 
 (pertaining to future requirements) is that in this atlas would probably be slight in the case 
 
 careful selection of data by local time of obser- of the N(z) maps, somewhat larger in the case 
 
 vation, and the segregation of these data into of the AN maps, and might well have a profound 
 
 types, e.g., nighttime and midafternoon, prior effect on the ground-based gradient maps of 
 
 to analysis or mapping, is probably equally as appendix C. 
 
 25 
 
9. References 
 
 Air Ministry, Meteorological Office (1961), Tables of 
 Temperature, Relative Humidity and Precipitation for 
 the World, vols. I-VI (Her Majesty's Stationery Office, 
 London). 
 
 Baynton, H. W., H. L. Hamilton, Jr., P. E. Sherr, and 
 J. J. B. Worth (1965), Radio climatology of a tropical 
 rain forest, J. Geophys. Res. 70, No. 2, 504-508. 
 
 Bean, B. R. (1954), Prolonged space-wave fadeouts in 
 1046 Mc observed in Cheyenne Mountain propagation 
 program, Proc. IRE 42, No. 5, 848-853. 
 
 Bean, B. R. (1959), Climatology of ground-based radio 
 ducts, J. Res. NBS 63D (Radio Prop.), No. 1, 29-34. 
 
 Bean, B. R. (1961), Concerning the bi-exponential nature 
 of tropospheric radio refractive index, Beitr. Physik 
 Atmosphare 34, Nos. 1/2, 81-91. 
 
 Bean, B. R. (1962), The radio refractive index of air, 
 Proc. IRE 50, No. 3, 260-273. 
 
 Bean, B. R., and B. A. Cahoon (1957), A note on the 
 climatic variation of absolute humidity, Bull. Am. 
 Meteorol. Soc. 38, No. 7, 395-398. 
 
 Bean, B. R., and B. A. Cahoon (1961a), Limitations of 
 radiosonde punch-card records for radio-meteorologi- 
 cal studies, J. Geophys. Res. 66, No. 1, 328-331. 
 
 Bean, B. R., and B. A. Cahoon (1961b), Correlation of 
 monthly median transmission loss and refractive in- 
 dex profile characteristics, J. Res. NBS 65D (Radio 
 Prop.), No. 1, 67-74. 
 
 Bean, B. R., and E. J. Dutton (1961), Concerning radio- 
 sondes, lag constants, and refractive index profiles, 
 J. Geophys. Res. 66, No. 11, 3717-3722. 
 
 Bean, B. R., and F. M. Meaney (1955), Some applications 
 of the monthly median refractivity gradient in tropo- 
 spheric propagation, Proc. IRE 43, No. 10, 1419-1431. 
 
 Bean, B. R., and G. D. Thayer (1959), On models of the 
 atmospheric refractive index, Proc. IRE 47, No. 5, 
 740-755. 
 
 Bean, B. R., G. D. Thayer, and B. A. Cahoon (1960a), 
 Methods of predicting the atmospheric bending of 
 radio rays, J. Res. NBS 64D (Radio Prop.), No. 5, 
 487-492. 
 
 Bean, B. R., J. D. Horn, and A. M. Ozanich, Jr. (1960b), 
 Climatic charts and data of the radio refractive index 
 for the United States and the world, NBS Monograph 
 No. 22. 
 
 Behn, R. C, and R. A. Duffee (1965), The structure of 
 the atmosphere in and above tropical forests, Rept. 
 No. BAT-171-8, Battelle Memorial Institute, Colum- 
 bus, Ohio. 
 
 Bunker, A. F. (1953), On the determination of moisture 
 gradients from radiosonde records, Bull. Am. Mete- 
 orol. Soc. 34, 406-409. 
 
 CCIR (International Radio Consultative Committee) 
 (1963), Influence of the atmosphere on wave propa- 
 gation, Rept. 233, Documents of the 10th Plenary 
 Assembly, vol. 2 (Geneva). 
 
 CCIR (International Radio Consultative Committee) 
 (1965), Propagation over the surface of the earth and 
 through the non-ionized regions of the atmosphere, 
 Rept. G.l.d(V), Conclusions of the interim meetings 
 of Study Group V (Geneva). 
 
 Dodd, A. V. (1965), Dew point distribution in the con- 
 tiguous United States, Monthly Weather Rev. 93, 
 No. 2, 113-122. 
 
 Dutton, E. J., and B. R. Bean (1965), The bi-exponential 
 nature of tropospheric gaseous absorption of radio 
 waves, Radio Sci. J. Res. NBS 69D, No. 6, 885-892. 
 
 du Castel, F. (1961), Propagation Tropospherique et 
 Faisceaux Hertziens Transhorizon, 140-141. (Editions 
 Chiron, Paris.) 
 
 Hutchings, J. W. (1961), Water vapor transfer over the 
 Australian continent, J. Meteorol. 18, 615-634. 
 
 Jeske, H. (1964), Transhorizon-transmission and height 
 gain measurements above the sea with waves in the 
 range of 1.8 cm to 187 cm under special consideration 
 of meteorological influences, Proc. 1964 World Conf. 
 on Radio Meteorology, Am. Meteorol. Soc, Boston, 
 Mass. 458-463. 
 
 Kantor, A. J., and A. E. Cole (1965), Monthly atmos- 
 pheric structure, surface to 80 km, J. Appl. Meteorol. 
 4, No. 2, 228-237. 
 
 Kerr, D. E. (1951), Propagation of Short Radio Waves, 
 MIT Radiation Lab. Series, vol. 13. (McGraw-Hill 
 Book Co., Inc., New York, N. Y.). 
 
 Knoll, D. W. (1941), Climatology Asiatic Station, Hy- 
 drographic Office, U. S. Navy, Washington, D. C. 
 
 List, R. J. (1958), Smithsonian Meterological Tables, 
 204. (Smithsonian Inst., Washington, D. C). 
 
 Michaelis, J., and E. Gossard (1958), Distribution of 
 refractive layers over the North Pacific and Arctic, 
 NEL Rept. No. 841, U. S. Navy Electronics Lab., San 
 Diego, Calif. 
 
 Misme, P., B. R. Bean, and G. D. Thayer (1960), Models 
 of the atmospheric radio refractive index, Proc. IRE 
 48, No. 8, 1498-1501. 
 
 Misme, P. (1964), A meteorological parameter for radio- 
 climatological purposes, Radio Sci. J. Res. NBS 68D, 
 No. 7, 851-855. 
 
 Reitan, C. H. (1963), Surface dew point and water 
 vapor aloft, J. Appl. Meteorol. 2, No. 6, 776-779. 
 
 Rydgren, B. (1963), Proposal for a Swedish radio stand- 
 ard atmosphere, Rapport A518, Forsvarets, Forsk- 
 ningsanstalt, Stockholm. 
 
 Saxton, J. A. (1951), Propagation of metre waves be- 
 yond the normal horizon, Proc. Inst. Elec. Engrs. 
 (London) 98, Pt. Ill, 360-369. 
 
 Serra, A. (1955), Atlas Climatologico do Brasil, vol. 1. 
 (Conselho Nacional de Geografia e Service de Meteor- 
 ologia, Rio de Janeiro.) 
 
 Smith, E. K., and S. Weintraub (1953), The constants in 
 the equation for atmospheric refractive index at radio 
 frequencies, Proc. IRE 41, 1035-1037. 
 
 Smith, J. W. (1963), The vertical temperature distribu- 
 tion and the layer of minimum temperature, J. Appl. 
 Meteorol. 2, No. 5, 655-667. 
 
 Smith, O. E., W. M. McMurray, and H. L. Crutcher 
 (1963), Cross sections of temperature, pressure, and 
 density near the 80th meridian west, National Aero- 
 nautics and Space Administration, Washington, D. C. 
 
 Thompson, B. W. (1965), The Climate of Africa (Oxford 
 University Press, Nairobi). 
 
 Trewartha, G. T. (1961), The Earth's Problem Climates 
 (Univ. of Wisconsin Press, Madison, Wise). 
 
 Tunnell, G. A. (1964), Periodic and random fluctuations 
 of the wind at Aden, Meteorol. Mag. 93, No. 1100, 
 70-82. 
 
 UNESCO (1958), Arid Zone Research X, Climatology, 
 Reviews of Research, 159 (Imprimerie Firmin-Didot, 
 Paris). 
 
 U. S. Navy, Chief of Naval Operations (1955-1959), 
 Marine Climatic Atlas of the World, vols. 1-5, NAV- 
 AER 50-1C-528 through 50-1C-532, U. S. Govt. Print- 
 ing Office, Washington, D. C. 
 
 26 
 
REFERENCES 27 
 
 U. S. Weather Bureau (1964), Manual of Radiosonde Wagner, N. K. (1961), The effect of the time constant of 
 Observations, Circular P, 7th ed., U. S. Govt. Printing radiosonde sensors on the measurement of tempera- 
 Office, Washington, D. C. ture and humidity discontinuities in the atmosphere, 
 
 Wagner, N. K. (1960), An analysis of radiosonde effects Bull. Am. Meteorol. Soc. 42, No. 5, 317-321. 
 
 on the measured frequency of occurrence of ducting Willett, H. C. (1944), Descriptive Meteorology (Aca- 
 
 layers, J. Geophys. Res. 65, 2077-2085. demic Press, Inc., New York, N. Y.). 
 
10. Appendix A. World Maps of N(z) Parameters 
 
 Data from the weather stations listed alphabetically in table A-l were used to prepare 
 the maps in this appendix. Each station is preceded by a number to identify its location 
 on figure A-l and followed by a listing of surface refractivity values at the 1, 5, 50, 95, and 
 99 percent cumulative distribution levels for the 4 months of February, May, August, and 
 November. 
 
 The N(z) parameters (all referenced to sea level) which are necessary to calculate N 
 at any altitude, z, in kilometers, are D , W , H u H 2 , H w , and z t . These are given in figures 
 A-2 through A-25. The z t chart for any particular month will determine which of the dry- 
 term curves will be used. If the desired altitude (above sea level) of N (z) is below the z t 
 value at the specified location, use the tropospheric equation 
 
 N (z) = D exp j - ^r J + W exp J - jj^ j . (A-l) 
 
 If the desired altitude is above the z t value, use 
 
 I z t (z - z t ) ) ( z ) 
 
 N (z) = D exp I ~ jy- - V — ^— j- + Wo exp ■ - ^- > . (A-2) 
 
 All three scale heights are required for equation (A-2), whereas only two, H x and H w , 
 appear in the tropospheric equation. If the surface altitude of the location is greater than 
 
 1 km, it is suggested that the "standard atmosphere" value of 3 km be substituted for H w 
 (see sec. 3.3). 
 
 To illustrate the step-by-step procedure for determining refractivity from the N (z) 
 parameters, the following example (assuming heights of 2 km and 20 km above the surface 
 at a location 200 m above sea level, at latitude 15°N and longitude 0°, in August) is given : 
 
 (a) Refractivity at 2 km above the surface: 
 
 (1) At the assumed location, interpolate linearly between contours on figures A-19 
 to obtain z t (13.8 km) to see whether the altitude above sea level, z (2.2 km) , is above or 
 below the z t value. It is below, so equation (A-l) should be used. 
 
 (2) The map values at 15°N and 0° of the parameters needed to calculate (A-l) 
 are: 
 
 D = 267.5 iV-units (fig. A-14) 
 
 Wo = 105.0 xV-units (fig. A-15) 
 
 H t = 9.35 km (fig. A-16) 
 
 H w = 2.19 km (fig. A-18) 
 
 (3) If these values are substituted in (A-l), N(z) is found to be 249.9 iV-units at 
 
 2 km above the surface. (Probable errors due to contouring and data restrictions would 
 suggest the use of only three significant figures, i.e., 250 iV-units.) 
 
 (b) Refractivity at 20 km above surface: 
 
 (1) Check to see whether the assumed altitude (z = 20.2 km above sea level) is 
 above or below the z t value. Since it is above, (A-2) should be used. 
 
 (2) All the parameters are needed for this calculation. In addition to the four 
 values listed in calculation (a) above, these are required: 
 
 H 2 = 5.90 km (fig. A-l 7) 
 
 Zt =13.8 km (fig. A-19) 
 
 29 
 
30 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
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32 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 
 
 30 60 90 120 150 180 
 
 60 
 
 70 
 
 60 
 
 70 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure A-l. Location o/N(z) data stations. 
 
 (3) If these values are substituted in (A-2), N (z) is found to be 20.7 iV-units at 
 20 km above the surface. 
 
 Figures A-26 through A-29 are seasonal maps of the standard prediction error of 
 the exponential fits to the mean wet-term profiles used in the N (z) parameter maps. An 
 rms error in the wet term of more than 5 A7-units was considered to be a reasonable 
 limiting criterion for locations where the N(z) model should be used with caution, if at 
 all. Figure A-30 delineates these areas; the cross-hatched areas indicate that the error 
 was in excess of 5 iV-units for 2 or more of the seasonal months (February, May, August, 
 and November) and the single-hatching depicts areas where only 1 of the 4 months 
 showed such large errors. Further discussion of the uncertainty in these areas can be 
 found in section 3.4. 
 
APPENDIX A 33 
 
 Figure A-2. Mean sea-level dry term, D : February. 
 
 180 150 120 90 60 30 30 60 90 120 150 180 
 
 Figure A-3. Mean sea-level wet term, W : February. 
 
34 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 2 8.0 7 8 7,8 M | ^-^ 
 
 180 150 120 90 60 
 
 30 60 90 120 150 180 
 
 Figure A-4. Dry-term tropospheric scale height in km. Hi: February. 
 
 Figure A-5. Dry-term stratospheric scale height in km, Ho: February. 
 
APPENDIX A 35 
 
 180 150 120 90 60 
 
 30 60 90 120 150 
 
 180 150 120 90 60 
 
 30 60 
 
 120 150 180 
 
 Figure A-6. Wet-term scale height in km, H w : February. 
 
 Figure A-7. Mean density tropopause altitude in km, z t : February. 
 
36 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure A-8. Mean sea-level dry term, D : May. 
 
 Figure A-9. Mean sea-level wet term, W : May. 
 
APPENDIX A 37 
 
 180 150 120 90 60 30 30 60 90 120 150 180 
 
 70 
 
 180 150 120 90 60 30 30 60 90 120 150 180 
 
 Figure A-10. Dry-term tropospheric scale height in km, Hi: May. 
 
 Figure A-ll. Dry-term stratospheric scale height in km, Ho: May. 
 
38 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure A-12. Wet-term scale height in km, H w : May. 
 
 180 150 120 90 60 30 30 60 90 120 150 180 
 
 Figure A-13. Mean density tropopause altitude in km, z t : May. 
 
APPENDIX A 39 
 
 180 150 120 
 
 801 
 
 90 60 
 
 30 60 
 
 120 150 
 
 180 150 
 
 30 60 90 120 150 180 
 
 Figure A-14. Mean sea-level dry term, D : August. 
 
 180 150 120 90 
 
 30 60 90 120 150 180 
 
 Figure A-15. Mean sea-level wet term, W : August. 
 
40 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure A-16. Dry-term tropospheric scale height in km, Hi: August. 
 
 Figure A-17. Dry-term stratospheric scale height in km, H2: August. 
 
APPENDIX A 41 
 
 Figure A-18. Wet-term scale height in km,, H w : August. 
 
 180 150 120 90 
 
 60 90 
 
 150 180 
 
 Figure A-1.9. Mean density tropopause altitude in km, z t : August. 
 
42 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 120 150 
 
 Figure A-20. Mean sea-level dry term, D : November. 
 
 Figure A-21. Mean sea-level wet term, W : November. 
 
APPENDIX A 43 
 
 Figure A-2'2. Dry-term tropospheric scale height in km, Hi: November. 
 
 Figure A-23. Dry-term stratospheric scale height in km, H 2 : November. 
 
44 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 Figure A-24. Wet-term scale height in km, H w : November. 
 
 Figure A-25. Mean density tropopause altitude in km, z t : November. 
 
APPENDIX A 45 
 
 180 150 
 
 180 150 120 90 60 30 
 
 60 90 
 
 150 180 
 
 Figure A-26. Standard prediction error of the exponential fit to the mean wet-term profile: February. 
 
 90 120 150 180 
 
 180 150 120 90 60 30 
 
 Figure A-27. Standard prediction error of the exponential fit to the mean wet-term profile: May. 
 
46 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 60 90 120 150 180 
 
 Figure A-28. Standard prediction error of the exponential fit to the mean wet-term profile: August. 
 
 30 60 90 120 150 !80 
 
 50 120 90 60 30 30 60 90 120 150 180 
 
 Figure A-29. Standard prediction error of the exponential fit to the mean wet-term profile: November. 
 
APPENDIX A 47 
 
 150 120 
 
 90 120 
 
 60 
 
 70 
 
 I I rms WET TERM ERROR >5N FOR 1 MONTH 
 K-'^l rms WET TERM ERROR >5N FOR 2 TO 1 MONTHS 
 
 60 
 
 70 
 
 180 150 
 
 120 
 
 90 
 
 60 
 
 30 
 
 30 
 
 60 90 
 
 120 
 
 150 180 
 
 Figure A-30. Areas of doubtful applicability of three-part exponential model of N(z) for z < 6 km. 
 
11. Appendix B. World Maps of aN. 
 
 The weather stations from which data were obtained for this study are shown in 
 figure B-l. Their locations, elevations and the 5-year mean surface refractivity for each 
 month of the year are alphabetically listed in table B-l. 
 
 The monthly AN values between the surface and 1 km above the surface are pre- 
 sented in figures B-2 through B-13. If the AN at a specific location is desired for a cer- 
 tain month and year for which a monthly mean surface refractivity value, N s , is avail- 
 able, the following relationships may be used : 
 
 AN = b (W.-W.) + aN, (B-l) 
 
 where 
 
 N s = N exp -°' 1( ^ ; z — elevation above sea level in km. 
 
 World maps of N (the yearly sea-level value of refractivity), b (the slope of the 
 
 regression line (B-l) ), and aN (the mean annual value of the refractivity difference be- 
 tween surface and 1 km) are presented in figures B-14 through B-16. 
 
 If the AN value were required at a station with an elevation of 300 m and a location 
 of 30°N and 30°E, here is the procedure which would be used. Available surface wea- 
 ther reports indicate that the mean N s was 314 for a recent month for which a value of 
 AN is needed. Therefore, at the assumed location, these values are interpolated linearly 
 from the figures : 
 
 Wo = 320 (fig. B-14) 
 
 W = 48 N-units (fig. B-15) 
 
 b = 0.60 (fig. B-16) 
 
 NT = 320 exp_- 01(0 - 3) = 311 
 Using the value of 314 for N s , aN is found to be 50 N-units. 
 
 In some areas of the world (e.g., where the assumption of an exponential distribution 
 of the wet term is largely invalid ; see sec. 3.4) , the use of the regression method to predict 
 AN has definite limitations. To delineate these locations, figures B-17, B-18, B-19, and 
 B-20 are presented. The first two figures are world maps of the correlation coefficient and 
 the standard error of estimate of the regression line of AN versus N s for the 60 months of 
 
 station data, and figure B-19 gives the percentage of this standard error to the AN value. 
 Areas with correlation coefficients < 0.5 (fig. B-17), standard errors > 5 N-units (fig. 
 
 B-18), and standard errors > 12 percent of AN (fig. B-19) are shaded, but the use of 
 equation (B-l) for any location in these shaded areas may still be valid if: 
 
 (a) a low correlation coefficient occurs with a small standard error (typical of sta- 
 tions with a small seasonal range of variability in both N s and AN) , or 
 
 (b) a large error is found with a good correlation (typical of stations with distinct 
 wet-dry seasons). 
 
 However, if the coefficient is less than 0.7 (reducing the variance of AN to ~ 50 per- 
 cent) and the standard error is greater than 10 percent of AN (as discussed in sec. 7), it 
 would be reasonable to assume that the yearly dependence of AN upon N s is not sufficient to 
 justify the regression prediction method. Areas represented by these criteria are shaded 
 in figure B-20. 
 
 48 
 
Table B-l. Mean surface reft 'activity. 
 
 APPENDIX B 49 
 
 Station 
 
 Abidjan, Ivory Coast 
 
 Adelaide, Australia 
 
 Aden, Arabia 
 
 Albrook (Balboa), Panama C.Z 
 
 Albuquerque, N. Mex 
 
 Aldan, U.S.S.R 
 
 Alert, Northwest Territories 
 
 Alexander Bay, South Africa 
 
 Alger/Maison, Algeria 
 
 Alice Springs, Australia 
 
 Allahabad, India 
 
 Alma Ata, U.S.S.R 
 
 Amundsen-Scott, Antarctica 
 
 Anadyr, U.S.S.R 
 
 Anchorage, Alaska 
 
 Ankara, Turkey 
 
 Antofagasta, Chile 
 
 Aoulef, Algeria 
 
 Argentia, Newfoundland 
 
 Arkhangelsk, U.S.S.R 
 
 Ashkabad, U.S.S.R 
 
 Aswan, United Arab Republic 
 
 Athens, Ga 
 
 Athinai, Greece 
 
 Auckland, New Zealand. 
 
 Bahia Blanca, Argentina 
 
 Bahrain Island 
 
 Baker Lake, Northwest Territories. . 
 
 Bangkok, Thailand 
 
 Bangui, Central African Republic. . . . 
 
 Barrow, Alaska 
 
 Beer Ya Aqov, Israel 
 
 B-Elan, U.S.S.R 
 
 Beni Abbes /Colomb, Algeria 
 
 Benina, Libya 
 
 Beograd, Yugoslavia 
 
 Bismarck, N. Dak 
 
 Bjornova Island 
 
 Blagoveshchensk, U.S.S.R 
 
 Bloemfontein, South Africa 
 
 Boise, Idaho 
 
 Bombay, India 
 
 Bordeaux, France 
 
 Brest, France 
 
 Brisbane, Australia 
 
 Broken Hill, Zambia 
 
 Brownsville, Tex 
 
 Bruxelles, Belgium 
 
 Bukhta Tikhaya, U.S.S.R 
 
 Bukhta Tiksi, U.S.S.R 
 
 Byrd Station, Antarctica 
 
 Cairo, United Arab Republic 
 
 Calcutta, India 
 
 Camaguey, Cuba 
 
 Canton Island 
 
 Cape Hatteras, N. C 
 
 Caribou, Maine 
 
 Charleville, Australia 
 
 Chatham Island 
 
 Chiangmai, Thailand 
 
 Chita, U.S.S.R 
 
 Christchurch, New Zealand 
 
 Clark Field, the Philippines 
 
 Cloncurry, Australia 
 
 Cocos Island 
 
 Columbia, Mo 
 
 Coppermine, Northwest Territories. . 
 Coral Harbour, Northwest Territories 
 
 Cordoba, Argentina 
 
 Curacao Island 
 
 Dakar, Senegal 
 
 Dar Es Salaam, Tanzania 
 
 Darwin, Australia 
 
 Denver, Colo 
 
 D.F. Malan (Capetown), 
 
 South Africa 
 
 Dijarbakir, Turkey 
 
 Djakarta, Indonesia 
 
 Dodge City, Kans 
 
 Douala, Cameroon 
 
 Durban, South Africa 
 
 Elevation 
 (meters) 
 
 16 
 
 11 
 
 4 
 
 9 
 
 1620 
 
 680 
 62 
 22 
 28 
 
 546 
 
 98 
 
 851 
 
 2800 
 
 62 
 
 40 
 
 902 
 
 122 
 
 290 
 
 17 
 
 13 
 
 230 
 196 
 
 246 
 
 1(17 
 49 
 
 72 
 2 
 9 
 
 16 
 385 
 
 4 
 
 49 
 
 23 
 
 498 
 
 125 
 
 139 
 506 
 
 14 
 
 137 
 
 1422 
 
 871 
 11 
 48 
 
 103 
 41 
 
 1206 
 
 6 
 
 100 
 
 6 
 
 8 
 
 1500 
 
 68 
 
 6 
 
 122 
 
 3 
 
 3 
 
 191 
 
 299 
 
 49 
 
 313 
 
 671 
 
 8 
 
 170 
 
 188 
 
 5 
 
 239 
 9 
 
 59 
 479 
 
 16 
 
 22 
 
 58 
 
 27 
 
 1625 
 
 49 
 
 652 
 
 8 
 
 791 
 
 13 
 
 14 
 
 Latitude 
 
 05 15N 
 
 34 56S 
 
 12 50N 
 OH 58N 
 
 35 03N 
 
 58 37N 
 82 30N 
 
 28 34S 
 
 36 43N 
 23 48S 
 
 25 27N 
 
 43 14N 
 90 00S 
 64 47N 
 61 ION 
 
 39 57N 
 23 28S 
 
 26 58N 
 
 47 18N 
 64 35N 
 
 37 58N 
 23 58N 
 33 57N 
 
 37 58N 
 
 36 51S 
 
 38 44S 
 
 26 16N 
 64 18N 
 
 13 44N 
 04 23N 
 
 71 18N 
 32 00N 
 46 57N 
 30 08N 
 
 32 06N 
 
 44 48N 
 46 46N 
 74 31N 
 50 16N 
 
 29 07S 
 
 43 34N 
 
 18 54N 
 
 44 51N 
 
 48 27N 
 
 27 28S 
 
 14 27S 
 
 25 55N 
 50 48N 
 80 19N 
 71 35N 
 
 80 00S 
 
 30 08N 
 22 39N 
 21 25N 
 02 46S 
 
 35 16N 
 46 52N 
 
 26 25S 
 
 45 58S 
 18 47N 
 
 52 05N 
 43 32S 
 
 15 08N 
 20 40S 
 12 05S 
 
 38 58N 
 67 49N 
 64 12N 
 
 31 19S 
 12 UN 
 
 14 44N 
 
 06 52S 
 12 26S 
 
 39 46N 
 
 33 55S 
 
 37 55N 
 06 US 
 37 46N 
 04 01N 
 29 58S 
 
 Longitude 
 
 03 56W 
 
 138 35E 
 
 45 01E 
 
 79 33W 
 
 106 37W 
 
 125 22E 
 62 20W 
 
 16 32E 
 
 03 14E 
 133 53E 
 
 81 44E 
 
 76 56E 
 
 00 00 
 177 34E 
 149 59W 
 
 32 53E 
 
 70 26W 
 
 01 05E 
 54 00W 
 40 30E 
 
 58 20E 
 32 47E 
 83 19W 
 23 43E 
 174 46E 
 
 62 11W 
 
 50 37 E 
 
 96 00W 
 100 30E 
 
 18 34E 
 
 156 47W 
 
 34 54E 
 
 142 43E 
 
 02 10W 
 20 16E 
 
 20 28E 
 100 45W 
 
 19 01E 
 
 127 30E 
 26 HE 
 
 116 13W 
 
 72 49 E 
 00 42W 
 
 04 25W 
 153 02E 
 
 28 28E 
 
 97 28W 
 04 'J IK 
 52 48E 
 
 128 55E 
 
 120 00W 
 31 24E 
 88 27E 
 
 77 52W 
 
 171 43W 
 
 75 33W 
 
 68 01W 
 
 146 17E 
 
 176 33W 
 
 98 59E 
 
 113 29E 
 
 172 37E 
 120 35E 
 140 30E 
 
 96 53E 
 
 92 22W 
 115 15W 
 83 22W 
 64 13W 
 68 59W 
 
 17 30W 
 
 39 16E 
 130 52E 
 104 53W 
 
 18 36E 
 
 40 12E 
 106 50E 
 
 99 58W 
 09 43 E 
 30 57E 
 
 Jan. 
 
 383 
 316 
 365 
 366 
 254 
 
 297 
 326 
 342 
 326 
 283 
 
 327 
 284 
 221 
 314 
 307 
 
 284 
 338 
 294 
 
 .'ill 
 312 
 
 307 
 299 
 308 
 316 
 339 
 
 320 
 338 
 329 
 366 
 348 
 
 323 
 324 
 312 
 294 
 323 
 
 310 
 296 
 310 
 
 318 
 
 283 
 
 284 
 347 
 320 
 
 319 
 351 
 
 319 
 
 337 
 314 
 326 
 332 
 
 253 
 314 
 337 
 351 
 374 
 
 319 
 307 
 325 
 330 
 338 
 
 300 
 334 
 345 
 338 
 373 
 
 305 
 327 
 324 
 328 
 372 
 
 342 
 376 
 380 
 251 
 
 337 
 
 293* 
 382 
 285 
 382 
 365 
 
 Feb. 
 
 389 
 321 
 364 
 360 
 251 
 
 296 
 327 
 34 I 
 323 
 
 287 
 
 312 
 
 283 
 229 
 
 317 
 307 
 
 283 
 341 
 288 
 310 
 312 
 
 305 
 287 
 308 
 
 :si4 
 345 
 
 323 
 339 
 
 333 
 377 
 347 
 
 325 
 
 322 
 
 312 
 289 
 322 
 
 311 
 296 
 310 
 311 
 
 284 
 
 284 
 352 
 323 
 316 
 357 
 
 322 
 341 
 313 
 320 
 327 
 
 257 
 312 
 338 
 353 
 372 
 
 323 
 305 
 335 
 339 
 332 
 
 294 
 335 
 
 344 
 
 :s:s!> 
 380 
 
 305 
 329 
 324 
 331 
 368 
 
 342 
 376 
 382 
 249 
 
 341 
 
 292 
 383 
 286 
 382 
 367 
 
 Mar. 
 
 389 
 321 
 371 
 365 
 248 
 
 289 
 
 :i:so 
 340 
 327 
 288 
 
 303 
 285 
 243 
 319 
 305 
 
 283 
 337 
 283 
 310 
 311 
 
 305 
 280 
 312 
 316 
 
 341 
 
 328 
 342 
 326 
 385 
 359 
 
 325 
 323 
 311 
 283 
 319 
 
 308 
 
 310 
 304 
 
 288 
 
 279 
 362 
 322 
 318 
 351 
 
 315 
 346 
 315 
 321 
 324 
 
 259 
 313 
 346 
 357 
 377 
 
 324 
 303 
 329 
 336 
 336 
 
 287 
 337 
 345 
 333 
 372 
 
 306 
 
 328 
 
 322 
 
 332* 
 
 372 
 
 348 
 382 
 387 
 250 
 
 337 
 
 293 
 
 382* 
 287 
 383 
 365 
 
 Apr. 
 
 387 
 321 
 380 
 368 
 245 
 
 284 
 32 1 
 336 
 329 
 290 
 
 291 
 287 
 246 
 321 
 306 
 
 284 
 333 
 280 
 313 
 
 .-ill 
 
 311 
 280 
 321 
 317 
 339 
 
 320 
 348 
 316 
 393 
 362 
 
 315 
 327 
 311 
 275 
 324 
 
 312 
 289 
 312 
 299 
 
 277 
 
 280 
 371 
 323 
 321 
 343 
 
 309 
 359 
 317 
 316 
 317 
 
 261 
 313 
 366 
 363 
 381 
 
 337 
 305 
 324 
 330 
 349 
 
 281 
 334 
 351 
 311 
 378 
 
 312 
 318 
 
 312 
 32V 
 376 
 
 351 
 379 
 372 
 249 
 
 333 
 
 297 
 383 
 291 
 382 
 356 
 
 May 
 
 387 
 322 
 385 
 375 
 253 
 
 283 
 312 
 330 
 337 
 290 
 
 299 
 293 
 246 
 310 
 308 
 
 290 
 330 
 279 
 320 
 
 air, 
 
 305 
 278 
 335 
 325 
 339 
 
 319 
 361 
 
 312 
 393 
 361 
 
 313 
 332 
 314 
 274 
 324 
 
 324 
 
 296 
 8 1 5 
 304 
 270 
 
 286 
 380 
 330 
 326 
 328 
 
 293 
 366 
 324 
 313 
 312 
 
 267 
 318 
 381 
 370 
 377 
 
 349 
 310 
 316 
 327 
 368 
 
 279 
 326 
 362 
 310 
 379 
 
 327 
 313 
 310 
 318 
 380 
 
 358 
 370 
 359 
 257 
 
 333 
 
 298 
 382 
 306 
 382 
 345 
 
 June 
 
 382 
 323 
 386 
 375 
 251 
 
 292 
 313 
 327 
 346 
 287 
 
 338 
 294 
 245 
 312 
 318 
 
 292 
 327 
 277 
 323 
 324 
 
 305 
 276 
 352 
 325 
 331 
 
 315 
 365 
 314 
 391 
 363 
 
 316 
 339 
 326 
 276 
 338 
 
 331 
 306 
 316 
 325 
 264 
 
 285 
 386 
 337 
 332 
 323 
 
 285 
 377 
 333 
 312 
 316 
 
 266 
 329 
 389 
 
 377 
 377 
 
 364 
 325 
 314 
 323 
 371 
 
 297 
 324 
 367 
 305 
 
 376 
 
 343 
 318 
 313 
 305 
 380 
 
 367 
 362 
 338 
 259 
 
 330 
 
 289 
 376 
 316 
 380 
 333 
 
 July 
 
 377 
 322 
 379 
 372 
 267 
 
 305 
 313 
 326 
 354 
 289 
 
 385 
 299 
 246 
 325 
 324 
 
 290 
 325 
 
 274 
 335 
 
 331 
 
 303, 
 
 285 
 
 360 
 326 
 327 
 
 316 
 381 
 
 31X 
 390 
 361 
 
 318 
 351 
 339 
 269 
 350 
 
 335 
 313 
 320 
 336 
 265 
 
 283 
 389 
 343 
 338 
 319 
 
 282 
 375 
 338 
 314 
 319* 
 
 263 
 341 
 394 
 378 
 379 
 
 376 
 332 
 309 
 322 
 
 375 
 
 310 
 
 323 
 366 
 
 307 
 374 
 
 353 
 318 
 317 
 303 
 382 
 
 371 
 359 
 344 
 264 
 
 329 
 
 289 
 
 367* 
 
 322 
 
 379 
 
 338 
 
 Aug. 
 
 373 
 319 
 
 378 
 381 
 274 
 
 304 
 315 
 325 
 355 
 281 
 
 391 
 
 295 
 247 
 321 
 324 
 
 286 
 327 
 278 
 339 
 333 
 
 306 
 288 
 361 
 323 
 329 
 
 312 
 
 386 
 320 
 391 
 362 
 
 319 
 355 
 
 3 11 
 273 
 
 349 
 
 331 
 308 
 
 320 
 339 
 259 
 
 281 
 388 
 342 
 337 
 320 
 
 278 
 377 
 337 
 315 
 
 320 
 
 267 
 347 
 395 
 379 
 
 376 
 
 374 
 330 
 302 
 324 
 377 
 
 308 
 323 
 369 
 299 
 
 3 72 
 
 352 
 322 
 319 
 298 
 384 
 
 377 
 357 
 338 
 269 
 
 327 
 
 282 
 366 
 3 IS 
 379 
 336 
 
 Sept. 
 
 380 
 318 
 376 
 382 
 259 
 
 293 
 310 
 328 
 354 
 
 2X2 
 
 379 
 286 
 244 
 313 
 316 
 
 287 
 326 
 
 287 
 33 1 
 324 
 
 300 
 291 
 
 3 17 
 326 
 328 
 
 317 
 382 
 314 
 393 
 361 
 
 315 
 
 347 
 331 
 
 284 
 343 
 
 320 
 299 
 317 
 318 
 264 
 
 278 
 384 
 343 
 336 
 325 
 
 279 
 370 
 334 
 313 
 314 
 
 262 
 338 
 394 
 379 
 373 
 
 366 
 323 
 303 
 
 322 
 376 
 
 293 
 324 
 366 
 295 
 372 
 
 330 
 316 
 
 311 
 300 
 384 
 
 381 
 361 
 360 
 
 256 
 
 328 
 
 276 
 368 
 
 308 
 380 
 
 313 
 
 Oct. 
 
 384 
 313 
 372 
 379 
 256 
 
 287 
 313 
 329 
 
 311 
 284 
 
 353 
 285 
 236 
 308 
 306 
 
 288 
 328 
 291 
 320 
 316 
 
 305 
 294 
 325 
 327 
 326 
 
 321 
 370 
 312 
 
 390 
 363 
 
 311 
 337 
 317 
 
 291 
 339 
 
 318 
 293 
 312 
 305 
 267 
 
 283 
 375 
 332 
 
 331 
 326 
 
 282 
 357 
 328 
 311 
 311 
 
 259 
 334 
 379 
 376 
 373 
 
 347 
 313 
 304 
 327 
 
 369 
 
 288 
 320 
 363 
 
 300 
 370 
 
 316 
 312 
 309 
 314 
 382 
 
 379 
 366 
 373 
 252 
 
 330 
 
 284 
 375 
 294 
 379 
 349 
 
 Nov. 
 
 387 
 313 
 361, 
 378 
 251 
 
 291 
 320 
 334 
 333 
 
 285 
 
 325 
 
 285 
 226 
 311 
 307 
 
 286 
 333 
 294 
 318 
 314 
 
 305 
 299 
 
 316 
 329 
 329 
 
 320 
 353 
 317 
 
 374 
 362 
 
 318 
 324 
 312 
 
 291 
 331 
 
 317 
 294 
 311 
 
 307 
 
 2 ',9 
 
 285 
 364 
 324 
 323 
 337 
 
 305 
 345 
 321 
 
 314 
 322 
 
 254 
 331 
 346 
 365 
 373 
 
 332 
 308 
 303 
 329 
 358 
 
 291 
 321 
 357 
 308 
 369 
 
 306 
 318 
 313 
 318 
 379 
 
 360 
 
 372 
 
 374 
 252 
 
 330 
 
 297 
 380 
 285 
 381 
 357 
 
 Dec. 
 
 383 
 314 
 369 
 375 
 251 
 
 297 
 324 
 338 
 328 
 284 
 
 324 
 
 284 
 220 
 314 
 307 
 
 287 
 334 
 299 
 312 
 312 
 
 309 
 305 
 308 
 322 
 
 331 
 
 319 
 341 
 325 
 363 
 354 
 
 324 
 323 
 311 
 296 
 326 
 
 312 
 295 
 
 310 
 314 
 285 
 
 285 
 355 
 321 
 324 
 347 
 
 317 
 339 
 318 
 319 
 327 
 
 253 
 322 
 
 341 
 360 
 372 
 
 326 
 306 
 310 
 332 
 345 
 
 295 
 
 327 
 349 
 321 
 369 
 
 306 
 323 
 317 
 327 
 376 
 
 342 
 375 
 385 
 251 
 
 33! 
 
 294 
 380 
 
 2.-4 
 382 
 360 
 
50 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table B-l. (Continued) 
 
 Station 
 
 Elevation 
 (meters) 
 
 Latitude 
 
 Longitude 
 
 Jan. 
 
 Feb. 
 
 Mar. 
 
 Apr. 
 
 May 
 
 June 
 
 July 
 
 Aug. 
 
 Sept. 
 
 Oct. 
 
 Nov. 
 
 113 31W 
 
 290 
 
 287 
 
 287 
 
 286 
 
 287 
 
 298 
 
 307 
 
 308 
 
 296 
 
 288 
 
 288 
 
 52 52W 
 
 311 
 
 311 
 
 310 
 
 309 
 
 310 
 
 314 
 
 318 
 
 318 
 
 311 
 
 309 
 
 307 
 
 23 55E 
 
 314 
 
 312 
 
 312 
 
 314 
 
 322 
 
 330 
 
 337 
 
 343 
 
 336 
 
 329 
 
 322 
 
 106 24W 
 
 266 
 
 261 
 
 258 
 
 255 
 
 260 
 
 266 
 
 284 
 
 289 
 
 272 
 
 271 
 
 263 
 
 114 51W 
 
 249 
 
 249 
 
 248 
 
 247 
 
 251 
 
 249 
 
 248 
 
 249 
 
 248 
 
 249 
 
 249 
 
 32 27E 
 
 320 
 
 321 
 
 323 
 
 325 
 
 326 
 
 323 
 
 321 
 
 320 
 
 322 
 
 320 
 
 318 
 
 85 56W 
 
 332 
 
 335 
 
 338 
 
 325 
 
 313 
 
 315 
 
 316 
 
 316 
 
 314 
 
 317 
 
 327 
 
 58 32W 
 
 340 
 
 349 
 
 344 
 
 338 
 
 331 
 
 326 
 
 325 
 
 326 
 
 328 
 
 333 
 
 335 
 
 147 52W 
 
 314 
 
 309 
 
 304 
 
 301 
 
 304 
 
 315 
 
 322 
 
 322 
 
 311 
 
 305 
 
 308 
 
 128 06E 
 
 311 
 
 319 
 
 319 
 
 313 
 
 315 
 
 313 
 
 313 
 
 316 
 
 317 
 
 309 
 
 300 
 
 15 02E 
 
 288 
 
 279 
 
 282 
 
 298 
 
 316 
 
 337 
 
 353 
 
 360 
 
 358 
 
 336 
 
 297 
 
 122 35W 
 
 306 
 
 302 
 
 296 
 
 294 
 
 295 
 
 307 
 
 314 
 
 313 
 
 305 
 
 298 
 
 299 
 
 111 58W 
 
 315 
 
 311 
 
 306 
 
 302 
 
 301 
 
 307 
 
 316 
 
 317 
 
 310 
 
 305 
 
 305 
 
 11 37W 
 
 302 
 
 301 
 
 300 
 
 304 
 
 305 
 
 311 
 
 308 
 
 313 
 
 312 
 
 310 
 
 309 
 
 16 54W 
 
 329 
 
 329 
 
 324 
 
 329 
 
 336 
 
 345 
 
 352 
 
 352 
 
 349 
 
 344 
 
 335 
 
 91 45E 
 
 336 
 
 332 
 
 333 
 
 348 
 
 368 
 
 385 
 
 392 
 
 394 
 
 388 
 
 377 
 
 356 
 
 128 18E 
 
 292 
 
 293 
 
 287 
 
 283 
 
 294 
 
 288 
 
 287 
 
 284 
 
 279 
 
 286 
 
 281 
 
 60 25W 
 
 310 
 
 310 
 
 309 
 
 308 
 
 309 
 
 315 
 
 324 
 
 324 
 
 315 
 
 310 
 
 308 
 
 09 54W 
 
 334 
 
 334 
 
 332 
 
 328 
 
 328 
 
 324 
 
 324 
 
 324 
 
 322 
 
 324 
 
 326 
 
 111 21W 
 
 272 
 
 271 
 
 270 
 
 269 
 
 271 
 
 277 
 
 278 
 
 273 
 
 271 
 
 270 
 
 270 
 
 88 08W 
 
 306 
 
 305 
 
 306 
 
 308 
 
 316 
 
 329 
 
 338 
 
 342 
 
 327 
 
 316 
 
 306 
 
 51 55E 
 
 315 
 
 314 
 
 315 
 
 314* 
 
 315 
 
 331* 
 
 325 
 
 326 
 
 320 
 
 320* 
 
 317 
 
 43 34E 
 
 320 
 
 318 
 
 317 
 
 317 
 
 311 
 
 302 
 
 303 
 
 306 
 
 310 
 
 309 
 
 321 
 
 24 58E 
 
 309 
 
 311 
 
 311 
 
 312 
 
 316 
 
 325 
 
 333 
 
 334 
 
 327 
 
 319 
 
 314 
 
 155 04W 
 
 350 
 
 349 
 
 349 
 
 353 
 
 358 
 
 359 
 
 361 
 
 367 
 
 362 
 
 361 
 
 358 
 
 147 20E 
 
 319 
 
 323 
 
 323 
 
 317 
 
 315 
 
 313 
 
 314 
 
 314 
 
 314 
 
 312 
 
 312 
 
 114 10E 
 
 331 
 
 334 
 
 348 
 
 363 
 
 378 
 
 385 
 
 391 
 
 391 
 
 383 
 
 360 
 
 348 
 
 93 23W 
 
 301 
 
 300 
 
 297 
 
 296 
 
 300 
 
 313 
 
 323 
 
 323 
 
 311 
 
 303 
 
 298 
 
 29 05E 
 
 317 
 
 317 
 
 318 
 
 323 
 
 333 
 
 342 
 
 352 
 
 354 
 
 342 
 
 333 
 
 329 
 
 27 10E 
 
 317 
 
 316 
 
 314 
 
 319 
 
 326 
 
 332 
 
 334 
 
 332 
 
 324 
 
 328 
 
 325 
 
 73 01E 
 
 301 
 
 290 
 
 292 
 
 284 
 
 297 
 
 339 
 
 355 
 
 368 
 
 359 
 
 310 
 
 297 
 
 169 31W 
 
 363 
 
 361 
 
 365 
 
 371 
 
 366 
 
 376 
 
 375 
 
 375 
 
 378 
 
 376 
 
 371 
 
 66 59E 
 
 324 
 
 341 
 
 355 
 
 370 
 
 384 
 
 394 
 
 391 
 
 391 
 
 387 
 
 368 
 
 350 
 
 73 08E 
 
 296 
 
 297 
 
 295 
 
 295 
 
 296 
 
 304 
 
 312* 
 
 311 
 
 296 
 
 294 
 
 296 
 
 23 53E 
 
 310 
 
 311 
 
 310 
 
 313 
 
 322 
 
 327 
 
 337 
 
 336 
 
 327 
 
 320 
 
 316 
 
 22 37W 
 
 309 
 
 310 
 
 311 
 
 313 
 
 317 
 
 321 
 
 325 
 
 323 
 
 322 
 
 316 
 
 313 
 
 135 10E 
 
 316 
 
 311 
 
 306 
 
 306 
 
 310 
 
 329 
 
 346 
 
 346 
 
 328 
 
 310 
 
 307 
 
 36 17E 
 
 309 
 
 308 
 
 308 
 
 308 
 
 311 
 
 321 
 
 325 
 
 325 
 
 317 
 
 317 
 
 314 
 
 32 33E 
 
 287 
 
 284 
 
 283 
 
 285 
 
 286 
 
 305 
 
 328 
 
 341 
 
 329 
 
 302 
 
 296 
 
 102 28E 
 
 331 
 
 328 
 
 320* 
 
 316 
 
 312 
 
 314* 
 
 321 
 
 317 
 
 313 
 
 313 
 
 325 
 
 108 07E 
 
 318 
 
 312 
 
 306 
 
 299 
 
 301 
 
 314 
 
 324 
 
 324 
 
 311 
 
 304 
 
 306 
 
 12 40E 
 
 314 
 
 314 
 
 315 
 
 316 
 
 319 
 
 325 
 
 333 
 
 333 
 
 329 
 
 324 
 
 321 
 
 82 54E 
 
 316 
 
 313 
 
 311 
 
 309 
 
 308 
 
 317 
 
 333 
 
 330 
 
 320 
 
 311 
 
 312 
 
 134 29E 
 
 385 
 
 383 
 
 383 
 
 387 
 
 391 
 
 385 
 
 388 
 
 387 
 
 388 
 
 386 
 
 388 
 
 92 53 E 
 
 309 
 
 305 
 
 304 
 
 301 
 
 300 
 
 310 
 
 327 
 
 327 
 
 315 
 
 305 
 
 304 
 
 63 37E 
 
 310 
 
 309 
 
 308 
 
 305 
 
 304 
 
 321* 
 
 328 
 
 320 
 
 311 
 
 307 
 
 307 
 
 30 30E 
 
 308 
 
 307 
 
 306 
 
 307 
 
 313 
 
 320 
 
 324 
 
 328 
 
 319 
 
 314 
 
 314 
 
 08 22W 
 
 320 
 
 321 
 
 323 
 
 324 
 
 331 
 
 334 
 
 340 
 
 342 
 
 341 
 
 334 
 
 324 
 
 147 00E 
 
 378 
 
 377 
 
 379 
 
 381 
 
 382 
 
 377 
 
 377 
 
 377 
 
 377 
 
 376 
 
 380 
 
 03 20E 
 
 377 
 
 382 
 
 382 
 
 384 
 
 385 
 
 378 
 
 373 
 
 372 
 
 379 
 
 382 
 
 381 
 
 93 09W 
 
 325 
 
 328 
 
 330 
 
 346 
 
 362 
 
 375 
 
 382 
 
 380 
 
 369 
 
 350 
 
 333 
 
 115 09W 
 
 283 
 
 279 
 
 274 
 
 269 
 
 267 
 
 264 
 
 279 
 
 284 
 
 271 
 
 275 
 
 278 
 
 30 LsE 
 
 312 
 
 312 
 
 312 
 
 312 
 
 318 
 
 327 
 
 334 
 
 334 
 
 327 
 
 320 
 
 315 
 
 15 19E 
 
 369 
 
 368 
 
 368 
 
 367 
 
 368 
 
 355 
 
 345 
 
 346 
 
 352 
 
 360 
 
 364 
 
 01 11W 
 
 314 
 
 315 
 
 315 
 
 316 
 
 320 
 
 324 
 
 329 
 
 330 
 
 329 
 
 324 
 
 318 
 
 77 02W 
 
 354 
 
 357 
 
 356 
 
 350 
 
 343 
 
 339 
 
 336 
 
 338 
 
 336 
 
 340 
 
 342 
 
 14 07E 
 
 311 
 
 313 
 
 312 
 
 313 
 
 319 
 
 325 
 
 336 
 
 332 
 
 326 
 
 322 
 
 317 
 
 09 08W 
 
 325 
 
 328 
 
 328 
 
 324 
 
 329 
 
 330 
 
 335 
 
 336 
 
 341 
 
 330 
 
 326 
 
 32 34E 
 
 371 
 
 370 
 
 366 
 
 361 
 
 348 
 
 341 
 
 339 
 
 341 
 
 345 
 
 356 
 
 357 
 
 13 13E 
 
 375 
 
 374 
 
 377 
 
 380 
 
 369 
 
 356 
 
 351 
 
 352 
 
 359 
 
 367 
 
 375 
 
 23 57E 
 
 302 
 
 306 
 
 301 
 
 307 
 
 315 
 
 322 
 
 330 
 
 327 
 
 318 
 
 312 
 
 309 
 
 158 57E 
 
 320 
 
 319 
 
 319 
 
 317 
 
 316 
 
 314 
 
 315 
 
 316 
 
 315 
 
 314 
 
 312 
 
 80 HE 
 
 364 
 
 363 
 
 368 
 
 379 
 
 379 
 
 367 
 
 369 
 
 376 
 
 380 
 
 384 
 
 379 
 
 03 41W 
 
 296 
 
 296 
 
 296 
 
 291 
 
 300 
 
 304 
 
 298 
 
 299 
 
 305 
 
 304 
 
 300 
 
 171 23E 
 
 383 
 
 378 
 
 381 
 
 385 
 
 387 
 
 381 
 
 383 
 
 383 
 
 383 
 
 383 
 
 384 
 
 31 39E 
 
 298 
 
 292 
 
 299 
 
 320 
 
 337 
 
 353 
 
 358 
 
 362 
 
 363 
 
 358 
 
 329 
 
 52 44E 
 
 311* 
 
 315 
 
 312 
 
 312 
 
 311 
 
 316 
 
 322* 
 
 321* 
 
 — 
 
 312* 
 
 314* 
 
 67 39W 
 
 339 
 
 335 
 
 336 
 
 345 
 
 354 
 
 352 
 
 353 
 
 353 
 
 354 
 
 355 
 
 350 
 
 37 52E 
 
 317 
 
 317 
 
 319 
 
 316 
 
 313 
 
 313 
 
 313 
 
 314 
 
 313 
 
 314 
 
 314 
 
 23 25E 
 
 323 
 
 324 
 
 319 
 
 301 
 
 284 
 
 279 
 
 275 
 
 271 
 
 274 
 
 282 
 
 308 
 
 62 53E 
 
 299 
 
 298 
 
 300 
 
 303 
 
 305 
 
 306 
 
 306 
 
 306 
 
 303 
 
 300 
 
 298 
 
 106 26W 
 
 346 
 
 339 
 
 342 
 
 351 
 
 360 
 
 374 
 
 379 
 
 380 
 
 384 
 
 377 
 
 354 
 
 166 40E 
 
 302 
 
 301 
 
 310 
 
 307 
 
 310 
 
 311 
 
 312 
 
 315 
 
 308 
 
 304 
 
 299 
 
 122 52W 
 
 305 
 
 303 
 
 301 
 
 300 
 
 304 
 
 307 
 
 306 
 
 305 
 
 306 
 
 307 
 
 307 
 
 144 58E 
 
 328 
 
 328 
 
 330 
 
 326 
 
 321 
 
 324 
 
 322 
 
 319 
 
 320 
 
 319 
 
 324 
 
 89 31W 
 
 350 
 
 349 
 
 354 
 
 363 
 
 369 
 
 376 
 
 379 
 
 378 
 
 381 
 
 370 
 
 357 
 
 27 13E 
 
 319 
 
 319 
 
 321 
 
 323 
 
 336 
 
 348 
 
 361 
 
 361 
 
 345 
 
 340 
 
 334 
 
 80 17W 
 
 341 
 
 343 
 
 347 
 
 357 
 
 367 
 
 375 
 
 379 
 
 379 
 
 380 
 
 362 
 
 352 
 
 09 17E 
 
 313 
 
 315 
 
 315 
 
 319 
 
 330 
 
 340 
 
 346 
 
 348 
 
 340 
 
 329 
 
 319 
 
 37 37E 
 
 307 
 
 306 
 
 305 
 
 306 
 
 314 
 
 322 
 
 332 
 
 327 
 
 318 
 
 313 
 
 310 
 
 Dec. 
 
 Edmonton, Alberta 
 
 Egedesminde, Greenland 
 
 El Adem, Libya 
 
 El Paso, Tex 
 
 Ely, Nev 
 
 Entebbe, Uganda 
 
 Eureka, Northwest Territories 
 
 Ezeiza, Argentina 
 
 Fairbanks, Alaska 
 
 Forrest, Australia 
 
 Ft. Lamy, Chad 
 
 Ft. Nelson, British Columbia 
 
 Ft. Smith, Northwest Territories 
 
 Ft. Trinquet, Mauritania 
 
 Funchal, Madeira 
 
 Gauhati, India 
 
 Giles, Australia 
 
 Goose Bay, Labrador 
 
 Gough Island 
 
 Great Falls, Mont 
 
 Green Bav, Wise 
 
 Guryev, U.S.S.R 
 
 Habbaniya, Iraq 
 
 Helsinki, Finland 
 
 Hilo, Hawaii 
 
 Hobart, Tasmania, Australia 
 
 Hong Kong 
 
 International Falls, Minn 
 
 Istanbul, Turkey 
 
 Izmir, Turkey 
 
 Jodhpur, India 
 
 Johnston Island 
 
 Karachi, West Pakistan 
 
 Karaganda, U.S.S.R 
 
 Kaunas, U.S.S.R 
 
 Keflavik, Iceland 
 
 Khabarovsk, U.S.S.R 
 
 Kharkov, U.S.S.R 
 
 Khartoum, Sudan 
 
 Khatanga, U.S.S.R 
 
 Kirensk, U.S.S.R 
 
 Kobenhavn, Denmark 
 
 Kolpashev, U.S.S.R 
 
 Koror, Palau Islands 
 
 Krasnoiarsk, U.S.S.R 
 
 Kustanay, U.S.S.R 
 
 Kyev, U.S.S.R 
 
 La Coruna, Spain 
 
 Lae, New Guinea 
 
 Lagos, Nigeria 
 
 Lake Charles, La 
 
 Las Vegas, Nev 
 
 Leningrad, U.S.S.R 
 
 Leopoldville (Kinshasa), Democratic 
 
 Republic of the Congo 
 
 Lerwick, United Kingdom 
 
 Lima, Peru 
 
 Lindenberg, East Germany 
 
 Lisboa, Portugal 
 
 Lourenco Marques, Portuguese East Africa 
 Luanda, Portuguese West Africa 
 
 Lvov, U.S.S.R 
 
 Macquarie Island 
 
 Madras, India 
 
 Madrid, Spain 
 
 Majuro Island 
 
 Malakal, Sudan 
 
 Malye-Karmakuly, U.S.S.R 
 
 Maracay, Venezuela 
 
 Marion Island 
 
 Maun, South Africa 
 
 Mawson, Antarctica 
 
 Mazatlan, Mexico 
 
 McMurdo Sound, Antarctica 
 
 Medford, Oreg 
 
 Melbourne, Australia 
 
 Merida, Mexico 
 
 Mersa Matruh, United Arab Republic. . . 
 
 Miami, Fla 
 
 Milano, Italy 
 
 Moscow, U.S.S.R 
 
 676 
 
 48 
 
 157 
 
 1194 
 
 1908 
 
 1146 
 
 2 
 
 20 
 
 138 
 
 160 
 
 300 
 
 375 
 203 
 359 
 110 
 
 51 
 
 514 
 
 44 
 
 40 
 
 1115 
 
 210 
 
 -21 
 45 
 58 
 11 
 
 54 
 
 66 
 
 360 
 
 40 
 
 25 
 
 224 
 5 
 
 4 
 
 555 
 
 75 
 
 50 
 
 72 
 
 153 
 
 385 
 
 24 
 
 258 
 
 6 
 
 76 
 
 29 
 
 194 
 
 171 
 182 
 
 57 
 8 
 
 40 
 
 5 
 
 664 
 
 4 
 
 290 
 
 82 
 
 is;, 
 
 105 
 
 103 
 
 44 
 
 70 
 
 329 
 
 6 
 
 16 
 
 657 
 
 3 
 
 389 
 16 
 
 442 
 26 
 
 945 
 
 14 
 78 
 45 
 
 ■10!, 
 44 
 
 22 
 
 25 
 
 4 
 
 120 
 
 156 
 
 53 34N 
 68 42N 
 31 51N 
 
 31 48N 
 
 39 17N 
 
 00 03N 
 80 00N 
 34 50S 
 64 49N 
 30 51S 
 
 12 08N 
 58 50N 
 60 01N 
 
 25 14N 
 
 32 38N 
 
 26 UN 
 
 25 02S 
 
 53 19N 
 
 40 19S 
 47 30N 
 
 42 29N 
 
 47 07N 
 
 33 22N 
 60 19N 
 19 44N 
 
 42 53S 
 
 22 18N 
 
 48 34N 
 40 58N 
 38 24N 
 
 26 18N 
 16 44N 
 
 24 48N 
 
 49 48N 
 
 54 53N 
 
 63 59N 
 
 48 31N 
 
 49 56N 
 15 36N 
 
 71 59N 
 
 57 46N 
 
 55 38N 
 
 58 18N 
 
 07 20N 
 
 56 00N 
 
 53 13N 
 
 50 27N 
 42 23N 
 06 44S 
 
 06 35N 
 
 30 13N 
 
 36 05N 
 
 59 58N 
 
 04 19S 
 
 60 08N 
 
 12 06S 
 52 13N 
 38 46N 
 
 25 55S 
 
 08 49S 
 
 49 49N 
 
 54 30S 
 
 13 00N 
 40 24N 
 
 07 05N 
 
 09 33 N 
 
 72 23N 
 
 10 15N 
 46 53S 
 
 19 59S 
 
 67 36S 
 
 23 UN 
 77 51S 
 42 23N 
 
 37 49S 
 
 20 58N 
 
 31 20N 
 25 49N 
 45 28N 
 
 55 49N 
 
 287 
 306 
 317 
 262 
 249 
 
 319 
 
 333 
 338 
 314 
 310 
 
 294 
 303 
 311 
 303 
 330 
 
 344 
 289- 
 309 
 332 
 269 
 
 306 
 
 316* 
 
 322 
 
 311 
 
 356 
 
 319 
 334 
 299 
 321 
 322 
 
 303 
 
 366 
 330 
 297 
 312 
 
 306 
 313 
 309 
 293 
 328 
 
 314 
 316 
 314 
 387 
 310 
 
 310 
 
 308 
 
 320* 
 
 379 
 
 379 
 
 329 
 280 
 313 
 
 367 
 313 
 
 349 
 313 
 327 
 362 
 374 
 
 304 
 320 
 366 
 301 
 383 
 
 304 
 
 314* 
 
 344 
 
 313 
 
 321 
 
 299 
 340 
 299 
 307 
 322 
 
 353 
 323 
 346 
 316 
 
 307 
 
Table B-l. {Continued) 
 
 APPENDIX B 51 
 
 Station 
 
 Elevation 
 (meters) 
 
 Latitude 
 
 Longitude 
 
 Jan. 
 
 Feb. 
 
 Mar. 
 
 Apr. 
 
 May 
 
 June 
 
 July 
 
 Aug. 
 
 Sept. 
 
 Oct. 
 
 Nov. 
 
 331 
 
 330 
 
 332 
 
 322 
 
 314 
 
 314 
 
 316 
 
 317 
 
 312 
 
 313 
 
 323 
 
 309 
 
 309 
 
 308 
 
 308 
 
 310 
 
 315 
 
 324 
 
 325 
 
 317 
 
 313 
 
 311 
 
 328 
 
 326 
 
 326 
 
 318 
 
 314 
 
 315 
 
 317 
 
 316 
 
 314 
 
 312 
 
 320 
 
 319 
 
 321 
 
 318 
 
 314 
 
 313 
 
 316 
 
 321 
 
 327 
 
 314* 
 
 314 
 
 315 
 
 322 
 
 326 
 
 325 
 
 318 
 
 313 
 
 317 
 
 318 
 
 318 
 
 316 
 
 312 
 
 315 
 
 319 
 
 .300 
 
 297 
 
 305 
 
 308 
 
 343 
 
 370 
 
 369 
 
 361 
 
 341 
 
 321 
 
 277 
 
 278 
 
 277 
 
 289 
 
 289 
 
 282 
 
 281 
 
 280 
 
 277 
 
 276 
 
 284 
 
 380 
 
 382 
 
 381 
 
 379 
 
 373 
 
 367 
 
 364 
 
 362 
 
 367 
 
 367 
 
 371 
 
 311 
 
 312 
 
 312 
 
 318 
 
 327 
 
 340 
 
 355 
 
 354 
 
 347 
 
 330 
 
 320 
 
 313 
 
 313 
 
 313 
 
 312 
 
 313 
 
 315 
 
 325 
 
 325 
 
 318 
 
 313 
 
 313 
 
 309 
 
 311 
 
 311 
 
 324 
 
 338 
 
 351 
 
 363 
 
 361 
 
 340 
 
 326 
 
 313 
 
 309 
 
 308 
 
 307 
 
 308 
 
 307 
 
 307 
 
 309 
 
 306 
 
 307 
 
 307 
 
 307 
 
 296 
 
 303 
 
 305* 
 
 310* 
 
 304 
 
 300 
 
 304 
 
 302 
 
 299 
 
 300 
 
 297 
 
 313 
 
 311 
 
 308 
 
 299 
 
 306 
 
 334 
 
 373 
 
 379 
 
 362 
 
 332 
 
 318 
 
 286 
 
 281 
 
 285 
 
 308 
 
 331 
 
 351 
 
 362 
 
 369 
 
 369 
 
 343 
 
 308 
 
 317 
 
 315 
 
 313 
 
 314 
 
 315 
 
 325 
 
 326 
 
 331 
 
 325 
 
 318 
 
 320 
 
 298 
 
 296 
 
 293 
 
 291 
 
 293 
 
 299 
 
 311 
 
 307 
 
 301 
 
 296 
 
 293 
 
 313 
 
 315 
 
 315 
 
 313 
 
 313 
 
 320 
 
 326 
 
 326 
 
 318 
 
 311 
 
 312 
 
 352 
 
 357 
 
 349 
 
 348 
 
 336 
 
 336 
 
 331 
 
 334 
 
 335 
 
 337 
 
 340 
 
 322 
 
 321 
 
 317 
 
 310 
 
 308 
 
 314 
 
 327 
 
 325 
 
 315 
 
 309 
 
 315 
 
 282 
 
 281 
 
 281 
 
 282 
 
 295 
 
 307 
 
 317 
 
 314 
 
 294 
 
 289 
 
 283 
 
 302 
 
 303 
 
 303 
 
 309 
 
 309 
 
 311 
 
 313 
 
 313 
 
 309 
 
 305 
 
 301 
 
 340 
 
 339 
 
 336 
 
 335 
 
 332 
 
 324 
 
 327 
 
 328 
 
 325 
 
 328 
 
 330 
 
 323 
 
 324 
 
 324 
 
 326 
 
 329 
 
 334 
 
 337 
 
 337 
 
 335 
 
 330 
 
 323 
 
 312 
 
 313 
 
 312 
 
 315 
 
 327 
 
 334 
 
 337 
 
 336 
 
 326 
 
 323 
 
 321 
 
 316 
 
 314 
 
 311 
 
 309 
 
 312 
 
 320 
 
 332 
 
 337 
 
 321 
 
 307 
 
 310 
 
 315 
 
 312 
 
 311 
 
 309 
 
 305 
 
 316 
 
 331 
 
 328 
 
 316 
 
 307 
 
 311 
 
 344 
 
 350 
 
 355 
 
 332 
 
 327 
 
 330 
 
 321 
 
 317 
 
 322 
 
 316 
 
 325 
 
 299 
 
 299 
 
 299 
 
 299 
 
 301 
 
 310 
 
 313 
 
 314 
 
 310 
 
 304 
 
 303 
 
 325 
 
 327 
 
 325 
 
 317 
 
 312 
 
 315 
 
 316 
 
 317 
 
 316 
 
 313 
 
 319 
 
 324 
 
 323 
 
 318 
 
 315 
 
 313 
 
 315* 
 
 320 
 
 320 
 
 314 
 
 311 
 
 316 
 
 375 
 
 376 
 
 378 
 
 377 
 
 375 
 
 368 
 
 366 
 
 362 
 
 363 
 
 368 
 
 373 
 
 330 
 
 330 
 
 333 
 
 330 
 
 327 
 
 326 
 
 325 
 
 323 
 
 323 
 
 324 
 
 320 
 
 303 
 
 301 
 
 309* 
 
 312 
 
 304 
 
 305 
 
 347* 
 
 365* 
 
 373* 
 
 318 
 
 311 
 
 303 
 
 304 
 
 305 
 
 305 
 
 309 
 
 320 
 
 326 
 
 331 
 
 320 
 
 311 
 
 306* 
 
 379 
 
 380 
 
 380 
 
 384 
 
 385 
 
 386 
 
 384 
 
 384 
 
 384 
 
 384 
 
 384 
 
 364 
 
 365 
 
 369 
 
 371 
 
 385 
 
 384 
 
 382 
 
 386 
 
 384 
 
 382 
 
 378 
 
 351 
 
 350 
 
 350 
 
 339 
 
 331 
 
 328 
 
 329 
 
 328 
 
 332 
 
 335 
 
 339 
 
 319 
 
 318 
 
 317 
 
 312 
 
 313 
 
 315 
 
 321 
 
 322 
 
 317 
 
 313 
 
 310 
 
 301 
 
 300 
 
 298 
 
 287 
 
 275 
 
 270 
 
 270 
 
 266 
 
 276 
 
 282 
 
 294 
 
 336 
 
 337 
 
 331 
 
 328 
 
 326 
 
 328 
 
 326 
 
 324 
 
 325 
 
 326 
 
 326 
 
 263 
 
 258 
 
 267 
 
 269 
 
 262 
 
 276 
 
 282 
 
 276 
 
 270 
 
 261 
 
 256 
 
 368 
 
 361 
 
 363 
 
 369 
 
 370 
 
 374 
 
 377 
 
 379 
 
 380 
 
 377 
 
 374 
 
 349 
 
 352 
 
 353 
 
 346 
 
 342 
 
 334 
 
 331 
 
 331 
 
 333 
 
 335 
 
 338 
 
 356 
 
 362 
 
 362 
 
 353 
 
 346 
 
 332 
 
 329 
 
 332 
 
 337 
 
 345 
 
 342 
 
 325 
 
 328 
 
 330 
 
 319 
 
 311 
 
 313 
 
 315 
 
 315 
 
 310 
 
 310 
 
 319 
 
 314 
 
 317 
 
 316 
 
 319 
 
 328 
 
 334 
 
 338 
 
 339 
 
 335 
 
 326 
 
 321 
 
 363 
 
 362 
 
 369 
 
 375 
 
 384 
 
 386 
 
 384 
 
 386 
 
 384 
 
 383 
 
 373 
 
 313 
 
 311 
 
 311 
 
 312 
 
 314 
 
 320 
 
 326 
 
 326 
 
 322 
 
 315 
 
 311 
 
 302 
 
 305 
 
 295 
 
 288 
 
 279 
 
 277 
 
 271 
 
 270 
 
 274 
 
 275 
 
 288 
 
 270 
 
 268 
 
 265 
 
 264 
 
 268 
 
 267 
 
 270 
 
 272 
 
 265 
 
 268 
 
 270 
 
 316 
 
 312 
 
 311 
 
 309 
 
 310 
 
 321 
 
 330 
 
 329 
 
 318 
 
 311 
 
 314 
 
 311 
 
 313 
 
 316 
 
 320 
 
 330 
 
 343 
 
 346 
 
 347 
 
 337 
 
 329 
 
 322 
 
 320 
 
 325 
 
 325 
 
 328 
 
 332 
 
 339 
 
 346 
 
 350 
 
 346 
 
 337 
 
 323 
 
 358 
 
 358 
 
 359 
 
 363 
 
 371 
 
 376 
 
 378 
 
 378 
 
 378 
 
 374 
 
 370 
 
 309 
 
 309 
 
 310 
 
 310 
 
 318 
 
 332 
 
 349 
 
 353 
 
 337 
 
 323 
 
 313 
 
 304 
 
 306 
 
 303 
 
 305 
 
 307 
 
 315 
 
 322 
 
 318 
 
 314 
 
 307 
 
 305 
 
 307 
 
 F06 
 
 304 
 
 305 
 
 309 
 
 325 
 
 333 
 
 332 
 
 323 
 
 315 
 
 307 
 
 308 
 
 308 
 
 311 
 
 315 
 
 318 
 
 321 
 
 327 
 
 327 
 
 324 
 
 316 
 
 310 
 
 375 
 
 377 
 
 382 
 
 385 
 
 387 
 
 384 
 
 383 
 
 382 
 
 382 
 
 382 
 
 380 
 
 306 
 
 306 
 
 306 
 
 304 
 
 305 
 
 309 
 
 319 
 
 320 
 
 313 
 
 307 
 
 307 
 
 317 
 
 316 
 
 318 
 
 316 
 
 313 
 
 312 
 
 313 
 
 311 
 
 312 
 
 312 
 
 314 
 
 311 
 
 310 
 
 310 
 
 311 
 
 311 
 
 318 
 
 328 
 
 329 
 
 324 
 
 321 
 
 315 
 
 303 
 
 303 
 
 304 
 
 306 
 
 313 
 
 319 
 
 322 
 
 325 
 
 319 
 
 312 
 
 306 
 
 306 
 
 303 
 
 302 
 
 300 
 
 302 
 
 314 
 
 324 
 
 319 
 
 309 
 
 303 
 
 305 
 
 368 
 
 364 
 
 371 
 
 377 
 
 382 
 
 386 
 
 387 
 
 387 
 
 388 
 
 383 
 
 375 
 
 311 
 
 309 
 
 308 
 
 308 
 
 310 
 
 317 
 
 325 
 
 324 
 
 318 
 
 312 
 
 311 
 
 249 
 
 247 
 
 242 
 
 247 
 
 252 
 
 263 
 
 265 
 
 266 
 
 265 
 
 258 
 
 254 
 
 338 
 
 342 
 
 349 
 
 357 
 
 372 
 
 381 
 
 384 
 
 384 
 
 380 
 
 364 
 
 357 
 
 246 
 
 248 
 
 244 
 
 243 
 
 243 
 
 252 
 
 251 
 
 252 
 
 255 
 
 251 
 
 252 
 
 312 
 
 311 
 
 312 
 
 307 
 
 299 
 
 295 
 
 291 
 
 289 
 
 290 
 
 294 
 
 305 
 
 296 
 
 295 
 
 298 
 
 304 
 
 308 
 
 301 
 
 302 
 
 304 
 
 297 
 
 297 
 
 300 
 
 318 
 
 321 
 
 317 
 
 321 
 
 328 
 
 332 
 
 336 
 
 340 
 
 336 
 
 328 
 
 324 
 
 299 
 
 297 
 
 298 
 
 305 
 
 314 
 
 320 
 
 331 
 
 330 
 
 321 
 
 311* 
 
 308 
 
 310 
 
 305 
 
 302 
 
 301 
 
 304 
 
 316 
 
 326 
 
 326 
 
 312 
 
 305 
 
 303 
 
 365* 
 
 366 
 
 372* 
 
 382* 
 
 385* 
 
 383* 
 
 385* 
 
 390* 
 
 389* 
 
 386* 
 
 376* 
 
 376 
 
 378 
 
 371 
 
 363 
 
 351 
 
 339 
 
 333 
 
 336 
 
 339 
 
 356 
 
 361 
 
 362 
 
 364 
 
 370 
 
 380 
 
 385 
 
 382 
 
 382 
 
 380 
 
 379 
 
 380 
 
 378 
 
 307 
 
 309 
 
 311 
 
 310 
 
 316 
 
 321 
 
 328 
 
 328 
 
 321 
 
 313 
 
 312 
 
 334 
 
 324 
 
 307* 
 
 304 
 
 301* 
 
 313 
 
 324 
 
 318 
 
 310 
 
 306 
 
 316* 
 
 Dec. 
 
 Mould Bay, Northwest Territories. . . 
 
 Murmansk, U.S.S.R 
 
 Mys Cheliuskin, U.S.S.R 
 
 Mys Kamenny, U.S.S.R 
 
 Mys Schmidt, U.S.S.R 
 
 Nagphur, India 
 
 Nairobi, Kenya 
 
 Nandi, Fiji Islands 
 
 Nantucket, Mass 
 
 Naryan-Mar, U.S.S.R 
 
 Nashville, Tenn 
 
 Naval Orcades Island 
 
 Neuquen, Argentina 
 
 New Delhi, India 
 
 Niamey, Niger ' 
 
 Nicosia, Cyprus 
 
 Nitchequon, Quebec 
 
 Nome, Alaska 
 
 Norfolk Island 
 
 Norman Wells, Northwest Territories 
 
 North Platte, Nebr 
 
 Norway Base, Antarctica 
 
 Nouvelle Amsterdam Island 
 
 Oakland, Calif 
 
 Odessa, U.S.S.R 
 
 Okhotsk, U.S.S.R 
 
 Omsk, U.S.S.R 
 
 Onslow, Australia 
 
 Ostersund, Sweden 
 
 Ostrov Chetyrekhstolbovoy, U.S.S.R. . 
 
 Ostrov Dikson, U.S.S.R 
 
 Papeete, Tahiti Island 
 
 Perth, Australia 
 
 Peshawar, West Pakistan 
 
 Petropavlovsk Kamcatskij, U.S.S.R... 
 
 Ponape, Caroline Islands 
 
 Port Blair, India 
 
 Port Elizabeth, South Africa 
 
 Port Harrison, Quebec 
 
 Pretoria, South Africa 
 
 Puerto Montt, Chile 
 
 Quetta/Samungli, West Pakistan 
 
 Raizet, Guadaloupe Island 
 
 Raoul Island 
 
 Resistencia, Argentina 
 
 Resolute Bay, Northwest Territories. 
 
 Roma, Italy 
 
 Saigon, Viet Nam 
 
 Saint Paul, Alaska 
 
 Salisbury, Rhodesia 
 
 Salt Lake City, Utah 
 
 Samarovo, U.S.S.R 
 
 Samsun, Turkey 
 
 San Diego, Calif 
 
 San Juan, P. R 
 
 Sapporo, Japan 
 
 Saratov, U.S.S.R 
 
 Sault Ste. Marie, Mich 
 
 Shemya, Alaska 
 
 Singapore 
 
 Sodankyla, Finland 
 
 Stanley, Falkland Islands 
 
 Stockholm, Sweden 
 
 Stuttgart, Germany 
 
 Sverdlovsk, U.S.S.R 
 
 Swan Island 
 
 Syktyvkar, U.S.S.R 
 
 Tacubaya, Mexico 
 
 Taipei, Taiwan 
 
 Tamanrasset, Algeria 
 
 Tananarive, Malagasy Republic 
 
 Tashkent, U.S.S.R 
 
 Tatoosh Island, Wash 
 
 Tbilisi, U.S.S.R 
 
 The Pas, Manitoba 
 
 Tourane, Viet Nam 
 
 Townsville, Australia 
 
 Trivandrum, India 
 
 Tromso, Norway 
 
 Tura, U.S.S.R 
 
 15 
 50 
 13 
 
 7 
 7 
 
 310 
 
 1798 
 
 16 
 
 14 
 
 7 
 
 184 
 
 4 
 
 270 
 
 216 
 
 226 
 
 218 
 515 
 
 14 
 110 
 
 64 
 
 850 
 
 50 
 
 28 
 
 6 
 
 Ii4 
 
 7 
 
 94 
 
 4 
 
 809 
 
 6 
 
 20 
 
 2 
 
 60 
 
 359 
 7 
 
 37 
 
 79 
 
 til 
 
 20 
 
 1368 
 
 3 
 1601 
 
 8 
 49 
 52 
 
 64 
 
 181 
 
 10 
 
 6 
 1480 
 
 1288 
 
 37 
 
 44 
 
 9 
 
 19 
 
 is 
 185 
 221 
 
 37 
 18 
 
 179 
 53 
 52 
 
 316 
 
 284 
 
 10 
 
 96 
 2306 
 
 8 
 1378 
 
 1310 
 
 478 
 26 
 
 404 
 272 
 
 7 
 4 
 
 64 
 9 
 
 147 
 
 76 14N 
 68 58N 
 
 77 43N 
 68 28N 
 
 68 55N 
 
 21 06N 
 01 18S 
 17 45S 
 41 15N 
 67 39N 
 
 36 07N 
 60 45S 
 38 57S 
 
 28 35N 
 13 29N 
 
 35 09N 
 
 53 12N 
 
 64 30N 
 
 29 03S 
 
 65 18N 
 
 41 08N 
 70 20S 
 
 37 50S 
 37 44N 
 46 29N 
 
 59 22N 
 
 54 56N 
 
 21 40S 
 
 63 UN 
 70 38N 
 
 73 30N 
 17 33S 
 
 31 57S 
 34 01N 
 52 58N 
 
 06 58N 
 11 40N 
 33 59S 
 
 58 27N 
 25 45S 
 
 41 28S 
 
 30 15N 
 
 16 16N 
 29 15S 
 27 28S 
 
 74 43N 
 41 48N 
 10 49N 
 57 09N 
 
 17 56S 
 
 40 46N 
 
 60 58N 
 
 41 17N 
 
 32 44N 
 
 18 26N 
 
 43 03N 
 
 51 34N 
 46 28N 
 
 52 43N 
 01 21N 
 
 67 22N 
 51 42S 
 
 59 2 IN 
 48 50N 
 56 50N 
 
 17 24N 
 
 61 40N 
 
 19 24N 
 25 02N 
 
 22 48N 
 
 18 54S 
 41 20N 
 48 23N 
 41 43N 
 
 53 58N 
 
 16 02N 
 
 19 15S 
 08 29N 
 
 69 42N 
 
 64 16N 
 
 119 20W 
 
 33 03E 
 
 104 17E 
 
 73 36E 
 
 179 29W 
 
 79 07E 
 36 45E 
 
 177 27E 
 70 04W 
 53 01E 
 
 86 41W 
 44 43W 
 
 68 07W 
 77 12E 
 02 10E 
 
 33 17E 
 
 70 35W 
 165 26W 
 167 56E 
 126 51W 
 
 100 42W 
 02 00W 
 
 77 34E 
 122 12W 
 
 30 38E 
 
 143 12E 
 73 24E 
 
 115 07E 
 14 37E 
 
 162 24E 
 
 80 14E 
 149 37W 
 115 49E 
 
 71 35E 
 158 45E 
 
 158 13E 
 92 43E 
 
 25 36E 
 
 78 08W 
 28 14E 
 
 72 56W 
 66 53E 
 61 31W 
 
 177 55W 
 58 59W 
 
 94 59W 
 
 12 36E 
 
 106 40E 
 
 170 13W 
 
 31 05E 
 
 111 58W 
 
 69 04E 
 36 20E 
 
 117 10W 
 66 00W 
 
 141 20E 
 
 46 00E 
 84 22W 
 
 174 06E 
 103 54E 
 
 26 39E 
 57 52W 
 
 18 04E 
 09 12 E 
 60 38E 
 
 83 56W 
 50 51 E 
 99 12W 
 121 31E 
 05 32E 
 
 47 32E 
 69 18E 
 
 124 44W 
 44 48E 
 
 101 06W 
 
 108 HE 
 
 146 46E 
 
 76 57E 
 
 19 01E 
 100 14E 
 
 328 
 309 
 321 
 
 322 
 320* 
 
 317 
 286 
 373 
 313 
 315 
 
 312 
 307 
 296 
 313 
 294 
 
 319 
 295 
 
 315 
 348 
 318 
 
 281 
 302 
 339 
 321 
 315 
 
 314 
 313 
 
 337 
 298 
 324 
 
 320 
 375 
 
 324 
 
 304 
 303 
 
 378 
 367 
 346 
 314 
 299 
 
 33 1 
 259 
 369 
 
 346 
 
 354 
 
 320 
 318 
 370 
 312 
 
 300 
 
 271 
 315 
 313 
 317 
 
 365 
 
 309 
 305 
 306 
 307 
 381 
 
 306 
 314 
 312 
 
 302 
 304 
 
 371 
 
 311 
 
 251 
 343 
 
 250 
 
 311 
 
 300 
 321 
 303 
 305 
 
 363* 
 866 
 
 368 
 308 
 329 
 
52 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table B-l. (Continued) 
 
 Station 
 
 Elevation 
 (meters) 
 
 Latitude 
 
 Longitude 
 
 Jan. 
 
 Feb. 
 
 Mar. 
 
 Apr. 
 
 May 
 
 June 
 
 July 
 
 Aug. 
 
 Sept. 
 
 Oct. 
 
 Nov. 
 
 Dec. 
 
 Turukhansk, U.S.S.R 
 
 Ushuaia, Argentina 
 
 Valentia, United Kingdom 
 
 Valparaiso, Chile 
 
 Vera Cruz, Mexico 
 
 Verkhoyansk, U.S.S.R 
 
 37 
 6 
 14 
 41 
 16 
 
 135 
 
 3 
 
 138 
 
 118 
 
 7 
 
 4 
 
 20 
 
 698 
 
 203 
 
 12 
 
 1728 
 
 t 
 t 
 t 
 t 
 t 
 
 t 
 t 
 t 
 t 
 t 
 
 t 
 
 t 
 
 65 47N 
 54 48S 
 
 51 56N 
 
 33 OlS 
 19 12N 
 
 67 33N 
 17 43N 
 
 43 07N 
 
 59 17N 
 
 37 23N 
 
 19 17N 
 
 38 51N 
 
 60 43N 
 48 15N 
 
 66 15S 
 
 22 34S 
 
 62 OON 
 56 30N 
 
 52 45N 
 
 44 OON 
 35 OON 
 
 59 OON 
 52 30N 
 
 45 OON 
 66 OON 
 30 OON 
 
 50 OON 
 
 34 OON 
 
 87 57E 
 68 19W 
 10 15W 
 71 39W 
 96 08W 
 
 133 23E 
 83 14E 
 
 131 54E 
 39 52E 
 
 136 54E 
 
 166 39E 
 
 77 02W 
 
 135 04W 
 
 16 22E 
 110 35E 
 
 17 06E 
 
 33 00W 
 51 00W 
 35 30W 
 41 00W 
 48 00W 
 
 19 00W 
 
 20 00W 
 16 00 W 
 02 OOE 
 
 140 00W 
 
 145 OOW 
 164 OOE 
 
 324 
 311 
 320 
 345 
 361 
 
 345 
 357 
 308 
 309 
 314 
 
 356 
 310 
 291 
 306 
 301 
 
 263 
 
 307 
 310 
 317 
 327 
 339 
 
 315 
 322 
 329 
 312 
 340 
 
 316 
 328 
 
 318 
 312 
 319 
 346 
 367 
 
 341 
 356 
 305 
 308 
 314 
 
 359 
 311 
 287 
 307 
 302 
 
 269 
 
 312 
 310 
 315 
 323 
 336 
 
 315 
 319 
 322 
 314 
 339 
 
 318 
 331 
 
 312 
 307 
 321 
 343 
 370 
 
 320 
 369 
 304 
 307 
 316 
 
 363 
 309 
 284 
 307 
 300 
 
 267 
 
 312 
 312 
 315 
 324 
 337 
 
 316 
 321 
 327 
 315 
 335 
 
 318 
 335 
 
 308 
 308 
 322 
 334 
 380 
 
 307 
 391 
 307 
 308 
 322 
 
 367 
 320 
 282 
 309 
 303 
 
 265 
 
 315 
 312 
 321 
 328 
 341 
 
 319 
 323 
 329 
 316 
 338 
 
 317 
 340 
 
 305 
 t08 
 325 
 332 
 382 
 
 301 
 392 
 314 
 312 
 332 
 
 371 
 328 
 282 
 316 
 303 
 
 248 
 
 317 
 318 
 322 
 334 
 351 
 
 321 
 324 
 335 
 319 
 340 
 
 324 
 350 
 
 311 
 310 
 332 
 333 
 
 386 
 
 308 
 391 
 329 
 325 
 348 
 
 378 
 342 
 287 
 325 
 306 
 
 247 
 
 323 
 321 
 328 
 340 
 366 
 
 327 
 333 
 342 
 321 
 344 
 
 328 
 359 
 
 323 
 310 
 337 
 331 
 386 
 
 316 
 384 
 347 
 335 
 369 
 
 380 
 354 
 292 
 332 
 307 
 
 245 
 
 326 
 326 
 332 
 356 
 374 
 
 330 
 337 
 348 
 327 
 349 
 
 333 
 379 
 
 324 
 308 
 336 
 332 
 
 387 
 
 314 
 386 
 350 
 330 
 371 
 
 384 
 352 
 293 
 333 
 303 
 
 241 
 
 324 
 325 
 333 
 360 
 374 
 
 328 
 337 
 348 
 327 
 351 
 
 336 
 381 
 
 316 
 309 
 335 
 332 
 383 
 
 307 
 389 
 332 
 319 
 355 
 
 383 
 343 
 289 
 322 
 303 
 
 237 
 
 320 
 320 
 330 
 348 
 368 
 
 324 
 333 
 345 
 326 
 350 
 
 335 
 369 
 
 310 
 305 
 329 
 334 
 379 
 
 310 
 380 
 311 
 314 
 336 
 
 380 
 328 
 284 
 317 
 301 
 
 240 
 
 314 
 315 
 323 
 336 
 357 
 
 320 
 327 
 338 
 318 
 
 348 
 
 325 
 366 
 
 316 
 305 
 324 
 335 
 368 
 
 331 
 358 
 304 
 311 
 324 
 
 373 
 316 
 284 
 312 
 299 
 
 256 
 
 311 
 311 
 319 
 330 
 349 
 
 318 
 326 
 330 
 318 
 345 
 
 319 
 355 
 
 324 
 303 
 320 
 338 
 361 
 
 345 
 
 Vishakhapatnam, India 
 
 Vladivostok U.S.S.R 
 
 Vologda, U.S.S.R 
 
 353 
 306 
 309 
 318 
 
 
 368 
 
 Washington, D. C 
 
 Wien/Hohe-Warte, Austria 
 
 Wilkes Stn., Antarctica 
 
 313 
 289 
 308 
 302 
 
 250 
 
 Ship A 
 
 306 
 
 Ship B 
 
 309 
 
 Ship C 
 
 Ship D. . 
 
 315 
 330 
 
 Ship E 
 
 346 
 
 Ship I 
 
 Ship J 
 
 Ship K 
 
 Ship M 
 
 Ship N 
 
 315 
 319 
 332 
 312 
 342 
 
 Ship P 
 
 317 
 
 Ship V 
 
 337 
 
 
 
 * Less than 3 years of data. 
 I No elevation given. 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 60 90 120 150 180 
 
 Figure B-l. Location of AN data stations. 
 
APPENDIX B 53 
 
 90 60 
 
 60 90 120 150 
 
 Figure B-2. Monthly mean AN: January. 
 
 Figure B-3. Monthly mean AN: February. 
 
54 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure B-4. Monthly mean AN: March. 
 
 Figure B-5. Monthly mean AN: April. 
 
APPENDIX B 55 
 
 Figure B-6. Monthly mean AN: May. 
 
 180 150 120 90 
 
 60 90 
 
 150 180 
 
 Figure B-7. Monthly mean AN: June. 
 
56 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 
 
 90 120 150 180 
 
 180 150 120 90 60 30 
 
 90 120 150 180 
 
 Figure B-8. Monthly mean AN: July. 
 
 Figure B-9. Monthly mean AN: August. 
 
APPENDIX B 57 
 
 180 
 80|— 
 
 180 150 120 90 
 
 Figure B-10. Monthly mean AN: September. 
 
 180 150 120 90 
 
 90 120 150 180 
 
 Figure B-ll. Monthly mean AN: October. 
 
58 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure B-12. Monthly mean AN: November 
 
 180 150 
 
 ISO 150 120 90 
 
 150 180 
 
 Figure B-13. Monthly mean AN: December. 
 
APPENDIX B 59 
 
 180 150 120 90 60 
 
 60 90 120 150 180 
 
 180 150 
 
 Figure B-14. Annual mean of sea-level refr activity, N„. 
 
 180 150 120 90 60 30 
 
 30 
 
 90 120 150 
 
 Figure B-15. Annual mean of refractivity gradient between surface and 1 km, .AN. 
 
60 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure B-16. Slope of regression line of AN versus N B , b. 
 
 Figure B-17. Correlation coefficient of AN versus N a 
 
APPENDIX B 61 
 
 180 150 120 90 
 
 90 120 150 180 
 
 180 150 120 90 60 30 
 
 60 90 120 150 
 
 Figure B-18. Standard prediction error of the regression line of AN versus N s . 
 
 Figure B-19. Standard prediction error of the regression line of AN versus N 3 as a percent of AN. 
 
62 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure B-20. Areas of doubtful applicability of using N s to predict AN. 
 
12. Appendix C. World Maps and Cumulative Distribution Charts 
 of Gradients of Ground-Based Atmospheric Layers 
 
 Initial gradient data, obtained (see sec. 5) for 99 of the 112 stations listed in table 
 A-l, are presented in groups of seasonal world maps which illustrate various aspects of 
 the percentage distribution of gradients in ground-based layers. The specific map groups 
 are given below. 
 
 Figures C-l through C-4: Percent of time gradient ^ (N/km) . 
 
 Figures C-5 through C-12 : Gradient exceeded 10 and 2 percent of the time for 100-m 
 layer. 
 
 Figures C-13 through C-20 : Percent of time gradient ^ -100 (N/km) and percent 
 of superrefractive layers > 100 m thick. 
 
 Figures C-21 through C-28: Percent of time gradient ^= -157 (N/km) and percent 
 of ducting layers > 100 m thick. 
 
 Figures C-29 through C-40: Percentage of time trapping frequency is below 3000 
 Mc/s, below 1000 Mc/s, and below 300 Mc/s. 
 
 Figures C-41 through C-56: Lapse rate of refractivity (N/km) exceeded 25, 10, 5, 
 and 2 percent of the time for 100-m layer. 
 
 Cumulative probability distribution charts were prepared for 22 climatically diverse 
 locations for the months of February, May, August, and November (figs. C-57 through 
 C-78) . The alphabetical listing of these stations in table C-l includes seasonal median and 
 minimum trapping frequency values when these were available. Distribution data for two 
 separate times of day at Aden and Nicosia are shown in figures C-57 and C-71. The nega- 
 tive gradient of 50 iV-units/km, which is generally considered to be a good normal value 
 for ground-based layers, has been indicated on each of the distributions by a dashed line to 
 provide a common reference for the vertical scale. The circled value on the distribution 
 line represents the mean ground-based gradient (of any layer thickness greater than 
 20 m) for each month. 
 
 63 
 
64 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 
 
 801 
 
 60 
 
 -60 
 
 70 1 — 
 180 
 
 J70 
 
 150 
 
 120 90 60 30 
 
 30 
 
 60 
 
 90 120 150 180 
 
 Figure C-l. Percent of time gradient > (N/km): February. 
 
 90 120 150 180 
 
 Figure C-2. Percent of time gradient > (N/km): May. 
 
APPENDIX C 65 
 
 180 150 
 
 60 90 
 
 150 180 
 
 Figure C-3. Percent of time gradient > (N/fcm): August. 
 
 180 150 
 
 90 60 
 
 180 150 120 90 60 30 
 
 60 90 120 150 
 
 Figure C-4. Percent of time gradient > (N/km): November. 
 
66 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 
 
 60 90 
 
 150 180 
 
 Figure C-5. Gradient (N/km) exceeded 10 percent of the time for 100-m layer: February. 
 
 180 150 
 
 90 60 
 
 180 150 120 90 
 
 60 90 120 150 
 
 Figure C-6. Gradient {N/km) exceeded 2 percent of the time for 100-m layer: February. 
 
APPENDIX C 67 
 
 180 150 120 90 
 
 180 150 120 90 
 
 60 90 120 150 
 
 FIGURE C-7. Gradient CN /km) exceeded 10 percent of the time for 100-m layer: May. 
 
 90 120 150 180 
 
 Figure C-8. Gradient (N/km) exceeded 2 -percent of the time for 100-m layer: May. 
 
68 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Figure C-9. Gradient (N/fcm) exceeded 10 percent of the time for 100-m layer: August. 
 
 180 150 120 90 
 
 90 120 150 180 
 
 180 150 120 90 60 30 
 
 150 180 
 
 Figure C-10. Gradient (N/km) exceeded 2 percent of the time for 100-m layer: August. 
 
APPENDIX C 69 
 
 180 150 120 90 60 30 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 60 90 120 150 
 
 Figure C-ll. Gradient (N/fcm) exceeded 10 percent of the time for 100-m layer: November. 
 
 180 150 
 
 90 60 
 
 180 150 
 
 Figure C-12. Gradient (N/fcm) exceeded 2 percent of the time for 100-m layer: November. 
 
70 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 801 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure C-13. Percent of time gradient < — 100 (N/fcm): February. 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure C-14. Percent of swperrefractive layers thicker than 100 m: February. 
 
APPENDIX C 71 
 
 180 150 120 90 
 
 60 90 
 
 150 180 
 
 Figure C-15. Percent of time gradient < —100 (N/fcm): May. 
 
 Figure C-16. Percent of superrefractive layers thicker than 100 m: May. 
 
72 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 
 
 60 30 
 
 60 90 
 
 150 180 
 
 Figure C-17. Percent of time gradient < —100 (N/fcra): August. 
 
 180 150 
 
 80 
 
 Figure C-18. Percent of su-perrefr active layers thicker than 100 m: August. 
 
APPENDIX C 73 
 
 Figure C-19. Percent of time gradient < —100 CN/km): November. 
 
 Figure C-20. Percent of superrefractive layers thicker than 100 m: November. 
 
74 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 Figure C-21. Percent of time gradient < — 157 CN/ km): February. 
 
 180 150 120 90 
 
 Figure C-22. Percent of ducting layers thicker than 100 m: February. 
 
APPENDIX C 75 
 
 180 150 
 
 80 
 
 Figure C-23. Percent of time gradient < —157 (N/Am): May. 
 
 180 
 80 
 
 120 90 60 30 
 
 60 90 120 150 
 
 Figure C-24. Percent of ducting layers thicker than 100 m: May. 
 
76 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 30 30 60 90 120 150 BO 
 
 60 
 
 -60 
 
 70 
 
 180 
 
 A 
 
 —170 
 180 
 
 150 120 
 
 90 
 
 60 30 
 
 Figure C-25. Percent of time gradient < — 157 (N /km): August. 
 
 Figure C-26. Percent of ducting layers thicker than 100 m: August. 
 
APPENDIX C 77 
 
 180 150 
 
 30 60 90 120 150 180 
 
 Figure C-27. Percent of time gradient < — 157 (N/ km): November. 
 
 60- 
 
 60 
 
 70 
 
 70 L 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 
 
 180 
 
 Figure C-28. Percent of ducting layers thicker than 100 m: November. 
 
78 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 FIGURE C-29. Percent of time trapping frequency < 3000 Mc/s: February. 
 
 180 150 120 90 60 
 
 30 60 90 120 150 180 
 
 90 120 150 180 
 
 Figure C-30. Percent of time trapping frequency < 1000 Mc/s: February. 
 
APPENDIX C 79 
 
 Figure C-31. Percent of time trapping frequency < 300 Mc/s: February. 
 
 Figure C-32. Percent of time trapping frequency < 3000 Mc/s: May. 
 
80 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure C-33. Percent of time trapping frequency < 1000 Mc/s: May. 
 
 180 150 120 90 
 
 30 60 90 120 150 180 
 
 60 
 
 70 
 
 -60 
 
 —170 
 180 
 
 180 150 120 90 60 30 
 
 30 60 
 
 120 150 
 
 Figure C-34. Percent of time trapping frequency < 300 Mc/s: May. 
 
APPENDIX C 81 
 
 180 150 
 
 30 30 60 90 120 150 180 
 
 60- 
 
 60 
 
 70 
 
 70 L 
 
 180 150 
 
 120 90 60 30 
 
 180 
 
 Figure C-35. Percent of time trapping frequency < 3000 Mc/s: August. 
 
 60- 
 
 60 
 
 70 
 
 70 L 
 
 180 150 
 
 120 90 60 30 
 
 30 
 
 60 90 120 150 
 
 180 
 
 Figure C-36. Percent of time trapping frequency < 1000 Mc/s: August. 
 
82 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 60 
 
 ■60 
 
 70 
 
 180 150 
 
 — 170 
 150 180 
 
 120 
 
 90 60 
 
 30 
 
 30 
 
 60 90 
 
 120 
 
 Figure C-37. Percent of time trapping frequency < 300 Mc/s: August. 
 
 60 
 
 70 
 
 -60 
 
 A 
 
 -170 
 
 180 150 120 90 60 30 
 
 30 
 
 60 90 120 150 
 
 180 
 
 Figure C-38. Percent of time trapping frequency < 3000 Mc/s: November. 
 
APPENDIX C 83 
 
 180 150 120 90 60 30 
 
 Figure C-39. Percent of time trapping frequency < 1000 Mc/s: November. 
 
 180 150 120 
 
 80| 
 
 60 
 
 60 
 
 70 
 
 70 
 
 180 150 
 
 120 90 60 30 
 
 30 60 
 
 90 
 
 120 150 180 
 
 Figure C-40. Percent of time trapping frequency < 300 Mc/s: November. 
 
84 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure C-41. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 100-m layer: February. 
 
 180 150 120 90 60 
 
 30 60 90 120 150 
 
 60 
 
 70 
 
 60 
 
 70 
 
 180 150 120 90 60 30 30 
 
 60 
 
 90 120 150 180 
 
 Figure C-42. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: February. 
 
APPENDIX C 85 
 
 180 150 
 
 180 150 120 90 
 
 60 90 120 150 
 
 Figure C-43. Lapse rate of refractivity (N/fcm) exceeded 5 percent of time for 100-m layer: February. 
 
 180 150 120 90 60 
 
 60 
 
 60 
 
 70 
 
 70 
 
 A 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 
 
 180 
 
 Figure C-44. Lapse rate of refractivity (N/fcm) exceeded 2 percent of time for 100-m layer: February. 
 
86 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 30 60 
 
 90 120 150 180 
 
 60 
 
 70 
 
 60 
 
 70 
 
 180 150 
 
 120 
 
 90 
 
 60 30 
 
 30 
 
 60 90 120 150 
 
 180 
 
 Figure C-45. Lapse rate of refractivity (N/Am) exceeded 25 percent of time for 100-m layer: May. 
 
 180 150 
 
 90 60 
 
 60 
 
 70 
 
 60 
 
 70 
 
 180 150 120 90 60 30 
 
 30 
 
 60 90 120 150 
 
 180 
 
 Figure C-46. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: May. 
 
APPENDIX C 87 
 
 Figure C-47. Lapse rate of refractivity (N/fcm) exceeded 5 percent of time for 100-m layer: May. 
 
 180 150 
 
 150 180 
 
 Figure C-48. Lapse rate of refractivity (N/fcm) exceeded 2 percent of time for 100-m layer: May. 
 
88 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 120 90 
 
 60 
 
 70 
 
 60 
 
 70 
 
 180 150 120 90 
 
 60 30 
 
 30 
 
 60 90 120 150 
 
 180 
 
 Figure C-49. Lapse rate of refractivity (N/km) exceeded 25 percent of time for 100-m layer: August. 
 
 180 150 120 90 
 
 60 30 
 
 90 120 150 180 
 
 Figure C-50. Lapse rate of refractivity (N/km) exceeded 10 percent of time for 100-m layer: August. 
 
APPENDIX C 89 
 
 60 
 
 70 
 
 f-,0 
 
 70 
 
 180 150 
 
 120 
 
 90 60 30 
 
 180 
 
 Figure C-51. Lapse rate of refractivity (N/A-m) exceeded 5 percent of time for 100-m layer: August. 
 
 180 150 
 
 Figure C-52. Lapse rate of refractivity (N/fcm) exceeded 2 percent of time for 100-m layer: August. 
 
90 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 90 120 150 180 
 
 FIGURE C-53. Lapse rate of refractivity (N/fcm) exceeded 25 percent of time for 100-m layer: November. 
 
 180 150 
 
 180 150 
 
 Figure C-54. Lapse rate of refractivity (N/fcm) exceeded 10 percent of time for 100-m layer: November. 
 
APPENDIX C 91 
 
 180 150 120 90 
 
 30 
 
 30 60 90 120 150 180 
 
 Figure C-55. Lapse rate of refractivity (N/km) exceeded 5 percent of time for 100-m layer: November. 
 
 180 150 
 
 180 150 120 90 
 
 60 90 120 150 
 
 Figure C-56. Lapse rate of refractivity (N/km) exceeded 2 percent of time for 100-m layer: November. 
 
92 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table C-l. Median and minimum trapping frequency (Mc/s) of ducting layers. 
 
 Station 
 
 Feb. 
 
 Med 
 
 Min 
 
 May 
 
 Med 
 
 Min 
 
 Aug. 
 
 Nov. 
 
 Med 
 
 Min 
 
 Med 
 
 Min 
 
 Aden, Arabia 0000 GMT 
 
 1200 GMT 
 
 Amundsen-Scott, Antarctica 
 
 Balboa (Albrook), Panama C. Z. . 
 
 Bangui, Central African Republic . 
 
 Bordeaux, France 
 
 Dakar, Senegal 
 
 Denver, Colo 
 
 Ezeiza, Argentina 
 
 Fort Smith, Northwest Territories . 
 
 Hilo, Hawaii 
 
 Long Beach, Calif 
 
 Lourenco Marques, 
 
 Portuguese East Africa 
 
 Nandi, Fiji Islands 
 
 New York, N.Y 
 
 Nicosia, Cyprus 0000 GMT . . . 
 1200 GMT. . . 
 
 Ostersund, Sweden 
 
 Perth, Australia 
 
 Saigon, Viet Nam 
 
 San Juan, P. R 
 
 Ship Station "C" 
 
 Tashkent, U.S.S.R 
 
 Vladivostok, U.S.S.R 
 
 * Less than 5 ducting layers during month. 
 
 t No ducting. 
 
 % Trapping frequencies not computed. 
 
 478 
 475 
 
 2503 
 743 
 280 
 
 1300 
 328 
 X 
 
 681 
 872 
 862 
 t 
 
 554 
 t 
 t 
 
 1143 
 
 t 
 
 t 
 4864 
 532 
 671 
 320 
 
 t 
 
 t 
 
 82 
 82 
 
 311 
 66 
 
 190 
 56 
 
 123 
 
 9 
 
 482 
 
 245 
 
 155 
 51 
 
 865 
 
 767 
 
 531 
 676 
 569 
 565 
 409 
 
 t 
 181 
 430 
 
 801 
 
 X 
 
 t 
 
 307 
 290 
 
 X 
 709 
 610 
 631 
 843 
 
 t. 
 499 
 
 40 
 
 51 
 
 266 
 
 270 
 
 247 
 
 99 
 
 43 
 
 346 
 
 125 
 
 326 
 
 41 
 
 582 
 495 
 663 
 265 
 402 
 848 
 
 t 
 
 383 
 145 
 467 
 
 X 
 
 2334 
 
 t 
 
 1 
 
 137 
 680 
 
 t 
 
 799 
 
 904 
 
 595 
 
 1028 
 
 334 
 
 584 
 
 41 
 41 
 
 272 
 
 242 
 
 176 
 
 89 
 
 162 
 
 43 
 253 
 
 44 
 122 
 
 403 
 227 
 
 50 
 
 485 
 522 
 
 t 
 
 657 
 
 369 
 
 442 
 378 
 
 X 
 
 1403 
 1345 
 1035 
 
 X 
 
 2404 
 
 X 
 
 X 
 
 398 
 1223 
 
 t 
 
 649 
 
 539 
 
 535 
 
 308 
 
 774 
 
 1253 
 
 114 
 122 
 
 314 
 
 294 
 
 186 
 
 35 
 
 496 
 
 430 
 
 264 
 53 
 
APPENDIX C 93 
 
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 y 
 
 
 ° 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 si 
 
 3 |i 
 
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 o 1 
 
 •' 1 
 
 1 
 
 
 
 
 
 
 
 
 
 j&'~' 
 
 
 
 
 Q 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 s * 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 O 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 •* / 
 
 
 
 
 
 
 < 
 
 
 
 
 
 
 
 
 
 .<$ 
 
 
 
 
 
 
 
 a * 
 
 
 
 
 
 
 
 
 _/ 
 
 
 
 
 
 
 
 
 s 
 
 
 
 
 
 
 
 
 ji 
 
 
 
 
 
 
 
 O 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 GC 
 
 
 
 
 
 
 
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13. Appendix D. World Charts of Tropopause Heights 
 
 Five-year mean tropopause heights obtained in the process of computing N(z) 
 parameters for February, May, August, and November at the 112 stations listed in table 
 A-l were plotted and contoured. Maps of these heights, given in figures D-l through D-4, 
 represent the average of all of the individual altitudes which marked the base of the first 
 layer which had a thickness of at least 2 km and a temperature lapse rate of less than 
 2°C/km (see sec. 6). 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 180 150 120 90 60 30 
 
 30 60 90 120 150 180 
 
 Figure D-l. Tropopause heights (km), based on temperature lapse rate: February. 
 
 117 
 
118 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 180 150 
 
 180 150 
 
 60 90 
 
 150 180 
 
 Figure D-2. Tropopause heights (km), based on temperature lapse rate: May. 
 
 180 150 
 
 90 60 
 
 90 120 
 
 180 150 
 
 90 120 150 180 
 
 Figure D-3. Tropopause heights (km), based on temperature lapse rate: August. 
 
APPENDIX D 119 
 
 180 150 120 90 
 
 Figure D-4. Tropopause heights (km), based on temperature lapse rate: November. 
 
14. Appendix E. Sample Listing of the Computer Output for 
 San Juan, P.R., and Amundsen-Scott, Antarctica 
 
 A sample listing of the complete computer output of the mean N-profiles for February, 
 May, August, and November at a subtropical and an arctic station is given in the first 
 section of this appendix. 
 
 For instance, at station 11636 (San Juan, P. R.) in February (table E-l) , the heading 
 gives the number of pieces of data used to compute two types of tropopause height (based 
 on two types of temperature criteria) : 
 
 (a) the mean heights where the extreme minimum temperature occurred in 320 indi- 
 vidual profiles was 17.56 km ± a standard deviation of 0.65 km, 
 
 (b) the mean height of the bottom of the lowest atmospheric layer with a thickness 
 ^ 2 km and a temperature gradient ^ — 2°C/km in 307 temperature profiles was 16.44 
 km ± 0.96 km. 
 
 This February profile also gives refractivity information at 40 height levels ranging 
 from to 30 km. Following each height level is a listing of the values of total refractivity, 
 gradient, dry and wet terms, and their respective standard deviations at that height above 
 surface. For example, at 1 km, 383 radiosonde profiles were examined, and the refractivity 
 was found to be 306.7 with a standard deviation of 8.22 iV-units ; the gradient at that level 
 was — 47.66 iV/km with a standard deviation of 17.38 iV/km ; the dry term was 242.4 ± 
 1.19 iV-units, and the wet term was 64.3 ± 8.52 iV-units. The correlation coefficients for 
 the data fit, within various height ranges, of the wet term (W) and the tropospheric (L^) 
 and stratospheric (D 2 ) dry terms to the line represented by a computed regression equation 
 are found at the bottom of each month's listing. In this example, for the dry term equation 
 (-Di), with a surface value of 271.9 and an exponential decay coefficient of — 0.1088, the 
 correlation coefficient is 0.999 and the standard deviation is 3.01 iV-units. (These figures 
 were based on 5 years of data from the surface to 15 km.) 
 
 At station 90001 (Amundsen-Scott, Antarctica) the wet-term value is so small at all 
 heights during the months studied that the regression equation from to 3 km becomes 
 meaningless. Because the South Polar region is not shown on the ground-based gradient 
 maps (figs. C-l through C-56) , a complete computer listing for Amundsen-Scott is included 
 in this appendix. This also illustrates the form of the original station data which were 
 used to plot the various values needed for the gradient maps. All gradients are in iV-units/ 
 km and all frequencies are Mc/s. The "profiles skipped" represents the number of profiles 
 which had gradients > — 100 iV/km. 
 
 120 
 
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APPENDIX E 125 
 
 Table E-3. Cumulative distribution of ground-based gradients: Arnuudseu-Scott, Antarctica 
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 0- 
 
 -50 METERS, STATION 90001, 
 
 MONTH 2 
 
 
 
 
 
 
 -20.7 
 
 0.51 
 
 -20.8 
 
 1.52 
 
 -22.3 
 
 2.53 
 
 -23.1 
 
 3.54 
 
 -24.8 
 
 4.55 
 
 -28.5 
 
 5.56 
 
 -28.7 
 
 6.57 
 
 -28.9 
 
 7.58 
 
 -30.5 
 
 8.59 
 
 -31.0 
 
 9.60 
 
 -32.8 
 
 10.61 
 
 -35.0 
 
 11.62 
 
 -37.2 
 
 12.63 
 
 -40.0 
 
 13.64 
 
 -41.6 
 
 14.65 
 
 -42.9 
 
 15.66 
 
 -43.3 
 
 16.67 
 
 -44.2 
 
 17.68 
 
 -44.2 
 
 IX. 69 
 
 -45.3 
 
 19.70 
 
 -46.9 
 
 20.71 
 
 -47.1 
 
 21.72 
 
 -47.7 
 
 22.73 
 
 -49.7 
 
 23.74 
 
 -50.1 
 
 24.75 
 
 -50.2 
 
 25.76 
 
 -50.3 
 
 26.77 
 
 -51.0 
 
 27.78 
 
 -51.3 
 
 28.79 
 
 -51.7 
 
 29.80 
 
 -52.8 
 
 30.81 
 
 -53.8 
 
 31.82 
 
 -54.0 
 
 32.83 
 
 -54.2 
 
 33.84 
 
 -56.0 
 
 34.85 
 
 -56.6 
 
 35.86 
 
 -57.5 
 
 36.87 
 
 -57.6 
 
 37.88 
 
 -61.5 
 
 38.89 
 
 -61.7 
 
 39.90 
 
 -62.4 
 
 40.91 
 
 -63.0 
 
 41.92 
 
 -63.5 
 
 42.93 
 
 -63.6 
 
 43.94 
 
 -63.9 
 
 44.95 
 
 -65.4 
 
 45.96 
 
 -67.6 
 
 46.97 
 
 -67.7 
 
 47.98 
 
 -67.8 
 
 48.99 
 
 -68.3 
 
 50.00 
 
 -68.4 
 
 51.01 
 
 -69.1 
 
 52.02 
 
 -70.7 
 
 53.03 
 
 -71.6 
 
 54.04 
 
 -72.5 
 
 55.05 
 
 -73.5 
 
 56.06 
 
 -73.8 
 
 57.07 
 
 -74.1 
 
 58.08 
 
 -74.5 
 
 59.09 
 
 -75.3 
 
 60.10 
 
 -75.7 
 
 61.11 
 
 -76.6 
 
 62.12 
 
 -76.7 
 
 63.13 
 
 -77.9 
 
 64.14 
 
 -79.5 
 
 65.15 
 
 -81.1 
 
 66.16 
 
 -81.9 
 
 67.17 
 
 -82.8 
 
 68.18 
 
 -83.9 
 
 69.19 
 
 -84.0 
 
 70.20 
 
 -84.4 
 
 71.21 
 
 -85.1 
 
 72.22 
 
 -85.4 
 
 73.23 
 
 -85.8 
 
 74.24 
 
 -86.1 
 
 75.25 
 
 -87.1 
 
 76.26 
 
 -89.2 
 
 77.27 
 
 -91.3 
 
 78.28 
 
 -93.7 
 
 79.29 
 
 -95.6 
 
 80.30 
 
 -97.9 
 
 81.31 
 
 -101.0 
 
 82.32 
 
 -102.4 
 
 83.33 
 
 -104.4 
 
 84.34 
 
 -107.2 
 
 85.35 
 
 -107.4 
 
 86.36 
 
 -107.5 
 
 87.37 
 
 -107.5 
 
 88.38 
 
 -109.7 
 
 89.39 
 
 -113.6 
 
 90.40 
 
 -114.6 
 
 91.41 
 
 -123.7 
 
 92.42 
 
 -125.1 
 
 93.43 
 
 -133.7 
 
 94.44 
 
 -137.2 
 
 95.45 
 
 -146.9 
 
 96.46 
 
 -154.4 
 
 97.47 
 
 -166.7 
 
 98.48 
 
 -171.8 
 
 99.49 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 0- 
 
 -50 METERS, STATION 90001, 
 
 MONTH 5 
 
 
 
 
 
 
 -33.8 
 
 0.30 
 
 -41.3 
 
 0.91 
 
 -42.4 
 
 1.52 
 
 -43.9 
 
 2.13 
 
 -45.1 
 
 2.74 
 
 -45.4 
 
 3.35 
 
 -45.6 
 
 3.96 
 
 -47.4 
 
 4.57 
 
 -48.7 
 
 5.18 
 
 -52.5 
 
 5.79 
 
 -53.3 
 
 6.40 
 
 -56.4 
 
 7.01 
 
 -57.4 
 
 7.62 
 
 -59.5 
 
 8.23 
 
 -63.1 
 
 8.84 
 
 -64.8 
 
 9.45 
 
 -65.2 
 
 10.06 
 
 -66.6 
 
 10.67 
 
 -68.1 
 
 11.28 
 
 -68.3 
 
 11.89 
 
 -69.5 
 
 12.50 
 
 -70.2 
 
 13.11 
 
 -70.8 
 
 13.72 
 
 -72.3 
 
 14.33 
 
 -73.1 
 
 14.94 
 
 -77.1 
 
 15.55 
 
 -78.2 
 
 16.16 
 
 -78.9 
 
 16.77 
 
 -80.6 
 
 17.38 
 
 -80.8 
 
 17.99 
 
 -82.0 
 
 18.60 
 
 -83.0 
 
 19.21 
 
 -83.7 
 
 19.82 
 
 -83.7 
 
 20.43 
 
 -89.5 
 
 21.04 
 
 -91.5 
 
 21.65 
 
 -92.0 
 
 22.26 
 
 -93.7 
 
 22.87 
 
 -94.1 
 
 23.48 
 
 -94.6 
 
 24.09 
 
 -97.6 
 
 24.70 
 
 -99.0 
 
 25.30 
 
 -102.3 
 
 25.91 
 
 -102.4 
 
 26.52 
 
 -105.8 
 
 27.13 
 
 -108.9 
 
 27.74 
 
 -110.4 
 
 28.35 
 
 -110.7 
 
 28.96 
 
 -111.4 
 
 29.57 
 
 -111.5 
 
 30.18 
 
 -113.9 
 
 30.79 
 
 -116.0 
 
 31.40 
 
 -116.7 
 
 32.01 
 
 -118.3 
 
 32.62 
 
 -119.7 
 
 33.23 
 
 -119.7 
 
 33.84 
 
 -120.7 
 
 34.45 
 
 -127.9 
 
 35.06 
 
 -131.9 
 
 35.67 
 
 -132.2 
 
 36.28 
 
 -132.2 
 
 36.89 
 
 -132.8 
 
 37.50 
 
 -134.4 
 
 38.11 
 
 -135.6 
 
 38.72 
 
 -137.1 
 
 39.33 
 
 -137.2 
 
 39.94 
 
 -137.4 
 
 40.55 
 
 -137.8 
 
 41.16 
 
 -138.1 
 
 11.77 
 
 -139.0 
 
 42.38 
 
 -140.7 
 
 42.99 
 
 -141.2 
 
 43.60 
 
 -144.5 
 
 44.21 
 
 -146.1 
 
 44.82 
 
 -147.3 
 
 45.43 
 
 -148.6 
 
 46.04 
 
 -150.3 
 
 46.65 
 
 -150.3 
 
 47.26 
 
 -150.4 
 
 47.87 
 
 -152.4 
 
 48.48 
 
 -153.3 
 
 49.09 
 
 -153.4 
 
 49.70 
 
 -153.6 
 
 50.30 
 
 -156.6 
 
 50.91 
 
 -157.1 
 
 51.52 
 
 -159.6 
 
 52.13 
 
 -164.0 
 
 52.74 
 
 -164.7 
 
 53.35 
 
 -165.1 
 
 53.96 
 
 — 166.6 
 
 54.57 
 
 -166.7 
 
 55.18 
 
 -167.0 
 
 55.79 
 
 -168.4 
 
 56.40 
 
 -168.7 
 
 57.01 
 
 -170.5 
 
 57.62 
 
 -171.6 
 
 58.23 
 
 -172.4 
 
 58.84 
 
 -173.5 
 
 59.45 
 
 -173.6 
 
 60.06 
 
 -174.6 
 
 60.67 
 
 -175.1 
 
 61.28 
 
 -176.7 
 
 61.89 
 
 -177.4 
 
 62.50 
 
 -178.0 
 
 63.11 
 
 -178.5 
 
 63.72 
 
 -179.6 
 
 64.33 
 
 -180.1 
 
 64.94 
 
 -180.5 
 
 65.55 
 
 -183.4 
 
 66.16 
 
 -183.6 
 
 66.77 
 
 -183.7 
 
 67.38 
 
 -186.3 
 
 67.99 
 
 -186.9 
 
 68.60 
 
 -187.3 
 
 69.21 
 
 -189.7 
 
 69.82 
 
 -193.8 
 
 70.43 
 
 -197.3 
 
 71.04 
 
 -199.0 
 
 71.65 
 
 -199.7 
 
 72.26 
 
 -204.3 
 
 72.87 
 
 -204.9 
 
 73.48 
 
 -207.6 
 
 74.09 
 
 -208.9 
 
 74.70 
 
 -209.1 
 
 75.30 
 
 -210.1 
 
 75.91 
 
 -210.9 
 
 76.52 
 
 -211.2 
 
 77.13 
 
 -214.2 
 
 77.74 
 
 -214.4 
 
 78.35 
 
 -216.3 
 
 78.96 
 
 -217.0 
 
 79.57 
 
 -222.3 
 
 80.18 
 
 -226.8 
 
 N0.79 
 
 -227.7 
 
 81.40 
 
 -229.2 
 
 82.01 
 
 -232.7 
 
 82.62 
 
 -233.4 
 
 83.23 
 
 -236.8 
 
 83.84 
 
 -238.9 
 
 84.45 
 
 -241.1 
 
 85.06 
 
 -244.1 
 
 85.67 
 
 -247.3 
 
 86.28 
 
 -247.6 
 
 86.89 
 
 -251.5 
 
 87.50 
 
 -255.0 
 
 88.11 
 
 -260.8 
 
 88.72 
 
 -263.3 
 
 89.33 
 
 -268.2 
 
 89.94 
 
 -270.8 
 
 90.55 
 
 -270.9 
 
 91.16 
 
 -275.0 
 
 91.77 
 
 -275.8 
 
 92.38 
 
 -282.8 
 
 92.99 
 
 -285.1 
 
 93.60 
 
 -297.8 
 
 94.21 
 
 -299.3 
 
 94.82 
 
 -299.8 
 
 95.43 
 
 -310.0 
 
 96.04 
 
 -311.1 
 
 96.65 
 
 -312.5 
 
 97.26 
 
 -341.2 
 
 97.87 
 
 -383.6 
 
 98.48 
 
 -429.0 
 
 99.09 
 
 -481.2 
 
 99.70 
 
 
 
 
 
 
 
 
 
 
 
 
 
 0- 
 
 -50 METERS, STATION 90001 
 
 MONTH 8 
 
 
 
 
 
 
 -37.9 
 
 0.27 
 
 -39.3 
 
 0.81 
 
 -45.5 
 
 1.35 
 
 -47.9 
 
 1.89 
 
 -48.4 
 
 2.43 
 
 -49.5 
 
 2.97 
 
 -49.6 
 
 3.51 
 
 -58.3 
 
 4.05 
 
 -59.3 
 
 4.59 
 
 -59.3 
 
 5.14 
 
 -69.3 
 
 5.68 
 
 -74.6 
 
 6.22 
 
 -74.7 
 
 6.76 
 
 -74.9 
 
 7.30 
 
 -75.1 
 
 7.84 
 
 -80.4 
 
 8.38 
 
 -80.7 
 
 8.92 
 
 -82.9 
 
 9.46 
 
 -85.7 
 
 10.00 
 
 -89.9 
 
 10.54 
 
 -90.8 
 
 11.08 
 
 -92.6 
 
 11.62 
 
 -94.9 
 
 12.16 
 
 -95.4 
 
 12.70 
 
 -96.2 
 
 13.24 
 
 -96.6 
 
 13.78 
 
 -99.0 
 
 14.32 
 
 -100.4 
 
 14.86 
 
 -100.5 
 
 15.41 
 
 -100.6 
 
 15.95 
 
 -101.7 
 
 16.49 
 
 -105.6 
 
 17.03 
 
 -106.5 
 
 17.57 
 
 -108.2 
 
 18.11 
 
 -110.1 
 
 18.65 
 
 -112.4 
 
 19.19 
 
 -112.9 
 
 19.73 
 
 -114.7 
 
 20.27 
 
 -114.9 
 
 20.81 
 
 -115.1 
 
 21.35 
 
 -115.2 
 
 21.89 
 
 -116.0 
 
 22.43 
 
 -117.5 
 
 22.97 
 
 -118.6 
 
 23.51 
 
 -121.6 
 
 24.05 
 
 -123.9 
 
 24.59 
 
 -126.3 
 
 25.14 
 
 -126.3 
 
 25.68 
 
 -129.2 
 
 26.22 
 
 -129.6 
 
 26.76 
 
 -130.8 
 
 27.30 
 
 -131.9 
 
 27.84 
 
 -132.3 
 
 28.38 
 
 -132.7 
 
 28.92 
 
 -134.3 
 
 29.46 
 
 -134.8 
 
 30.00 
 
 -135.2 
 
 30.54 
 
 -135.5 
 
 31.08 
 
 -135.9 
 
 31.62 
 
 -137.3 
 
 32.16 
 
 -137.5 
 
 32.70 
 
 -137.6 
 
 33.24 
 
 -138.0 
 
 33.78 
 
 -138.7 
 
 34.32 
 
 -139.1 
 
 34.86 
 
 -139.3 
 
 35.41 
 
 -141.7 
 
 35.95 
 
 -143.6 
 
 36.49 
 
 -143.7 
 
 37.03 
 
 -143.7 
 
 37.57 
 
 -146.0 
 
 38.11 
 
 -146.6 
 
 38.65 
 
 -147.1 
 
 39.19 
 
 -147.2 
 
 39.73 
 
 -147.5 
 
 40.27 
 
 -149.8 
 
 40.81 
 
 -150.2 
 
 41.35 
 
 -151.4 
 
 41.89 
 
 -152.6 
 
 42.43 
 
 -154.7 
 
 42.97 
 
 -156.6 
 
 43.51 
 
 -156.8 
 
 44.05 
 
 -156.9 
 
 44.59 
 
 -157.6 
 
 45.14 
 
 -161.1 
 
 45.68 
 
 -163.3 
 
 46.22 
 
 -164.4 
 
 46.76 
 
 -164.6 
 
 47.30 
 
 -166.7 
 
 47.84 
 
 -166.9 
 
 48.38 
 
 -168.1 
 
 48.92 
 
 -169.2 
 
 49.46 
 
 -170.1 
 
 50.00 
 
 -171.3 
 
 50.54 
 
 -171.5 
 
 51.08 
 
 -171.8 
 
 51.62 
 
 -173.0 
 
 52.16 
 
 -174.4 
 
 52.70 
 
 -176.0 
 
 53.24 
 
 -176.2 
 
 53.78 
 
 -176.3 
 
 54.32 
 
 -177.8 
 
 54.86 
 
 -178.4 
 
 55.41 
 
 -180.4 
 
 55.95 
 
 -180.5 
 
 56.49 
 
 -180.9 
 
 57.03 
 
 -180.9 
 
 57.57 
 
 -183.8 
 
 58.11 
 
 -183.8 
 
 58.65 
 
 -184.4 
 
 59.19 
 
 -185.9 
 
 59.73 
 
 -186.3 
 
 60.27 
 
 -186.4 
 
 60.81 
 
 -189.5 
 
 61.35 
 
 -190.2 
 
 61.89 
 
 -191.7 
 
 62.43 
 
 -191.8 
 
 62.97 
 
 -192.0 
 
 63.51 
 
 -193.0 
 
 64.05 
 
 -193.8 
 
 64.59 
 
 -197.1 
 
 65.14 
 
 -198.3 
 
 65.68 
 
 -199.3 
 
 66.22 
 
 -200.8 
 
 66.76 
 
 -203.1 
 
 67.30 
 
 -204.5 
 
 67.84 
 
 -206.0 
 
 68.38 
 
 -208.6 
 
 68.92 
 
 -208.8 
 
 69.46 
 
 -211.2 
 
 70.00 
 
 -211.8 
 
 70.54 
 
 -212.0 
 
 71.08 
 
 -212.5 
 
 71.62 
 
 -215.4 
 
 72.16 
 
 -216.1 
 
 72.70 
 
 -216.1 
 
 73.24 
 
 -217.1 
 
 73.78 
 
 -218.3 
 
 74.32 
 
 -219.3 
 
 74.86 
 
 -221.4 
 
 75.41 
 
 -221.7 
 
 75.95 
 
 
 76.49 
 
 -225.7 
 
 77.03 
 
 -227.7 
 
 77.57 
 
 -230.6 
 
 78.11 
 
 -230.7 
 
 78.65 
 
 -232.5 
 
 79.19 
 
 -238.2 
 
 79.73 
 
 -239.9 
 
 80.27 
 
 -240.9 
 
 80.81 
 
 -242.2 
 
 81.35 
 
 -242.6 
 
 81.89 
 
 -244.8 
 
 82.43 
 
 -245.4 
 
 82.97 
 
 -246.9 
 
 83.51 
 
 -249.6 
 
 84.05 
 
 -252.3 
 
 84.59 
 
 -252.9 
 
 85.14 
 
 -254.6 
 
 85.68 
 
 -262.2 
 
 86.22 
 
 -265.4 
 
 86.76 
 
 -266.3 
 
 87.30 
 
 -267.0 
 
 87.84 
 
 -267.1 
 
 88.38 
 
 -269.1 
 
 88.92 
 
 -272.4 
 
 89.46 
 
 -275.7 
 
 90.00 
 
 -277.6 
 
 90.54 
 
 -279.1 
 
 91.08 
 
 -279.2 
 
 91.62 
 
 -280.0 
 
 92.16 
 
 -289.3 
 
 92.70 
 
 -290.5 
 
 93.24 
 
 -290.7 
 
 93.78 
 
 -292.7 
 
 94.32 
 
 -293.7 
 
 94.86 
 
 -296.5 
 
 95.41 
 
 -298.7 
 
 95.95 
 
 -302.6 
 
 96.49 
 
 -305.8 
 
 97.03 
 
 -306.4 
 
 97.57 
 
 -314.4 
 
 98.11 
 
 -336.0 
 
 98.65 
 
 -346.2 
 
 99.19 
 
 -419.4 
 
 99.73 
 
 
 
 
 
 
 
 
 
126 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table E-3. (Continued) 
 
 
 
 
 
 0- 
 
 -50 METERS, STATION 90001, 
 
 MONTH 11 
 
 
 
 
 
 
 -9.1 
 
 0.31 
 
 -16.1 
 
 0.93 
 
 -18.1 
 
 1.55 
 
 -21.3 
 
 2.17 
 
 -22.5 
 
 2.80 
 
 -24.9 
 
 3.42 
 
 -25.1 
 
 4.04 
 
 -25.8 
 
 4.66 
 
 -27.3 
 
 5.28 
 
 -27.6 
 
 5.90 
 
 -27.6 
 
 6.52 
 
 -27.7 
 
 7.14 
 
 -27.8 
 
 7.76 
 
 -28.0 
 
 8.39 
 
 -28.2 
 
 9.01 
 
 -29.1 
 
 9.63 
 
 -31.7 
 
 10.25 
 
 -35.1 
 
 10.87 
 
 -37.2 
 
 11.49 
 
 -38.4 
 
 12.11 
 
 -38.6 
 
 12.73 
 
 -39.5 
 
 13.35 
 
 -39.6 
 
 13.98 
 
 -39.7 
 
 14.60 
 
 -40.4 
 
 15.22 
 
 -40.7 
 
 15.84 
 
 -42.7 
 
 16.46 
 
 -43.6 
 
 17.08 
 
 -44.3 
 
 17.70 
 
 -45.4 
 
 18.32 
 
 -45.5 
 
 18.94 
 
 -46.5 
 
 19.57 
 
 -46.5 
 
 20.19 
 
 -46.6 
 
 20.81 
 
 -46.7 
 
 21.43 
 
 -4Y.3 
 
 22.05 
 
 -48.3 
 
 22.67 
 
 -49.1 
 
 23.29 
 
 -49.3 
 
 23.91 
 
 -49.3 
 
 24.53 
 
 -49.8 
 
 25.16 
 
 -49.9 
 
 25.78 
 
 -50.3 
 
 26.40 
 
 -51.7 
 
 27.02 
 
 -53.7 
 
 27.64 
 
 -53.7 
 
 28.26 
 
 -53.9 
 
 28.88 
 
 -54.6 
 
 29.50 
 
 -55.1 
 
 30.12 
 
 -55.4 
 
 30.75 
 
 -55.7 
 
 31.37 
 
 -55.9 
 
 31.99 
 
 -56.1 
 
 32.61 
 
 -56.3 
 
 33.23 
 
 -56.4 
 
 33.85 
 
 -56.8 
 
 34.47 
 
 -58.3 
 
 35.09 
 
 -58.6 
 
 35.71 
 
 -58.7 
 
 36.34 
 
 -58.8 
 
 36.96 
 
 -59.1 
 
 37.58 
 
 -59.7 
 
 38.20 
 
 -59.9 
 
 38.82 
 
 -60.2 
 
 39.44 
 
 -60.5 
 
 40.06 
 
 -61.1 
 
 40.68 
 
 -61.4 
 
 41.30 
 
 -61.9 
 
 41.93 
 
 -62.0 
 
 42.55 
 
 -62.3 
 
 43.17 
 
 -62.8 
 
 43.79 
 
 -62.8 
 
 44.41 
 
 -63.2 
 
 45.03 
 
 -64.1 
 
 45.65 
 
 -64.5 
 
 46.27 
 
 -65.0 
 
 46.89 
 
 -66.3 
 
 47.52 
 
 -66.3 
 
 48.14 
 
 -66.5 
 
 48.76 
 
 -67.2 
 
 49.38 
 
 -67.5 
 
 50.00 
 
 -67.7 
 
 50.62 
 
 -67.8 
 
 51.24 
 
 -68.2 
 
 51.86 
 
 -68.5 
 
 52.48 
 
 -68.6 
 
 53.11 
 
 -69;2 
 
 53.73 
 
 -69.7 
 
 54.35 
 
 -70.2 
 
 54.97 
 
 -73.0 
 
 55.59 
 
 -73.2 
 
 56.21 
 
 -73.4 
 
 56.83 
 
 -73.9 
 
 57.45 
 
 -74.2 
 
 58.07 
 
 -74.3 
 
 58.70 
 
 -74.6 
 
 59.32 
 
 -76.0 
 
 59.94 
 
 -76.3 
 
 60.56 
 
 -76.8 
 
 61.18 
 
 -77.1 
 
 61.80 
 
 -77.2 
 
 62.42 
 
 -77.2 
 
 63.04 
 
 -77.4 
 
 63.66 
 
 -77.8 
 
 64.29 
 
 -78.7 
 
 64.91 
 
 -79.0 
 
 65.53 
 
 -79.0 
 
 66.15 
 
 -79.5 
 
 66.77 
 
 -79.8 
 
 67.39 
 
 -80.5 
 
 68.01 
 
 -81.6 
 
 68.63 
 
 -81.8 
 
 69.25 
 
 -82.3 
 
 69.88 
 
 -84.0 
 
 70.50 
 
 -84.5 
 
 71.12 
 
 -84.7 
 
 71.74 
 
 -85.1 
 
 72.36 
 
 -86.0 
 
 72.98 
 
 -86.0 
 
 73.60 
 
 -86.0 
 
 74.22 
 
 -86.1 
 
 74.84 
 
 -86.6 
 
 75.47 
 
 -86.8 
 
 76.09 
 
 -87.3 
 
 76.71 
 
 -87.7 
 
 77.33 
 
 -88.9 
 
 77.95 
 
 -89.8 
 
 78.57 
 
 -90.2 
 
 79.19 
 
 -91.3 
 
 79.81 
 
 -92.4 
 
 80.43 
 
 -92.6 
 
 81.06 
 
 -96.0 
 
 81.68 
 
 -97.6 
 
 82.30 
 
 -98.9 
 
 82.92 
 
 -99.2 
 
 83.54 
 
 -99.4 
 
 84.16 
 
 -100.0 
 
 84.78 
 
 -100.7 
 
 85.40 
 
 -100.8 
 
 86.02 
 
 -100.9 
 
 86.65 
 
 -102.1 
 
 87.27 
 
 -102.3 
 
 87.89 
 
 -102.9 
 
 88.51 
 
 -103.7 
 
 89.13 
 
 -105.3 
 
 89.75 
 
 -106.1 
 
 90.37 
 
 -108.4 
 
 90.99 
 
 -110.2 
 
 91.61 
 
 -111.8 
 
 92.24 
 
 -111.9 
 
 92.86 
 
 -119.1 
 
 93.48 
 
 -119.1 
 
 94.10 
 
 -119.3 
 
 94.72 
 
 -122.4 
 
 95.34 
 
 -123.8 
 
 95.96 
 
 -124.1 
 
 96.58 
 
 -128.8 
 
 97.20 
 
 -139.4 
 
 97.83 
 
 -147.0 
 
 98.45 
 
 -155.9 
 
 99.07 
 
 -161.3 
 
 99.69 
 
 
 
 
 
 0- 
 
 -100 METERS, STATION 90001 
 
 , MONTH 2 
 
 
 
 
 
 
 -20.6 
 
 0.51 
 
 -20.7 
 
 1.52 
 
 -22.1 
 
 2.53 
 
 -23.0 
 
 3.54 
 
 -24.7 
 
 4.55 
 
 -28.4 
 
 5.56 
 
 -28.6 
 
 6.57 
 
 -28.8 
 
 7.58 
 
 -30.3 
 
 8.59 
 
 -30.4 
 
 9.60 
 
 -32.7 
 
 10.61 
 
 -34.7 
 
 11.62 
 
 -37.1 
 
 12.63 
 
 -39.8 
 
 13.64 
 
 -41.1 
 
 14.65 
 
 -42.6 
 
 15.66 
 
 -43.0 
 
 16.67 
 
 -44.0 
 
 17.68 
 
 -44.0 
 
 18.69 
 
 -45.1 
 
 19.70 
 
 -46.6 
 
 2 .71 
 
 -46.7 
 
 21.72 
 
 -46.8 
 
 22.7< 
 
 -47.4 
 
 23.74 
 
 -48.9 
 
 24.75 
 
 -49.9 
 
 25.76 
 
 -50.0 
 
 26.77 
 
 -50.7 
 
 27.78 
 
 -51.0 
 
 28.79 
 
 -51.4 
 
 29.80 
 
 -52.5 
 
 30.81 
 
 -53.5 
 
 31.82 
 
 -53.6 
 
 32.83 
 
 -53.8 
 
 33.84 
 
 -55.6 
 
 34.85 
 
 -56.2 
 
 35.86 
 
 -57.1 
 
 36.87 
 
 -57.3 
 
 37.88 
 
 -61.1 
 
 38.89 
 
 -61.3 
 
 39.90 
 
 -61.3 
 
 40.91 
 
 -62.6 
 
 41.92 
 
 -63.1 
 
 42.93 
 
 -63.1 
 
 43.94 
 
 -63.5 
 
 44.95 
 
 -64.9 
 
 45.96 
 
 -67.1 
 
 46.97 
 
 -67.2 
 
 47.98 
 
 -67.3 
 
 48.99 
 
 -67.8 
 
 50.00 
 
 -67.9 
 
 51.01 
 
 -68.5 
 
 52.02 
 
 -70.2 
 
 53.03 
 
 -71.1 
 
 54.04 
 
 -71.9 
 
 55.05 
 
 -72.9 
 
 56.06 
 
 -73.2 
 
 57.07 
 
 -73.5 
 
 58.08 
 
 -73.8 
 
 59.09 
 
 -74.7 
 
 60.10 
 
 -75.0 
 
 61.11 
 
 -76.0 
 
 62.12 
 
 -76.1 
 
 63.13 
 
 -77.3 
 
 64.14 
 
 -78.8 
 
 65.15 
 
 -80.3 
 
 66.16 
 
 -81.1 
 
 67.17 
 
 -82.0 
 
 68.18 
 
 -83.1 
 
 69.19 
 
 -83.2 
 
 70.20 
 
 -83.6 
 
 71.21 
 
 -84.3 
 
 72.22 
 
 -84.6 
 
 73.23 
 
 -85.0 
 
 74.24 
 
 -85.3 
 
 75.25 
 
 -86.2 
 
 76.26 
 
 -88.3 
 
 77.27 
 
 -90.4 
 
 78.28 
 
 -92.7 
 
 79.29 
 
 -93.0 
 
 80.30 
 
 -94.6 
 
 81.31 
 
 -96.8 
 
 82.32 
 
 -99.9 
 
 83.33 
 
 -101.3 
 
 84.34 
 
 -103.2 
 
 85.35 
 
 -106.2 
 
 86.36 
 
 -106.2 
 
 87.37 
 
 -106.2 
 
 88.38 
 
 -108.4 
 
 89.39 
 
 -112.2 
 
 90.40 
 
 -112.8 
 
 91.41 
 
 -113.1 
 
 92.42 
 
 -122.1 
 
 93.43 
 
 -131.8 
 
 94.44 
 
 -135.2 
 
 95.45 
 
 -144.6 
 
 96.46 
 
 -151.9 
 
 97.47 
 
 -163.8 
 
 98.48 
 
 -168.8 
 
 99.49 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 0- 
 
 100 METERS, STATION 90001 
 
 MONTH 5 
 
 
 
 
 
 
 -42.2 
 
 0.30 
 
 -43.7 
 
 0.91 
 
 -44.9 
 
 1.52 
 
 -45.2 
 
 2.13 
 
 -45.4 
 
 2.74 
 
 -47.2 
 
 3.35 
 
 -49.2 
 
 3.96 
 
 -56.1 
 
 4.57 
 
 -56.6 
 
 5.18 
 
 -57.1 
 
 5.79 
 
 -59.1 
 
 6.40 
 
 -62.7 
 
 7.01 
 
 -64.7 
 
 7.62 
 
 -66.1 
 
 8.23 
 
 -67.6 
 
 8.84 
 
 -70.2 
 
 9.45 
 
 -70.8 
 
 10.06 
 
 -71.8 
 
 10.67 
 
 -72.5 
 
 11.28 
 
 -74.0 
 
 11.89 
 
 -74.2 
 
 12.50 
 
 -77.3 
 
 13.11 
 
 -77.6 
 
 13.72 
 
 -79.9 
 
 14.33 
 
 -80.1 
 
 14.94 
 
 -83.0 
 
 15.55 
 
 -83.6 
 
 16.16 
 
 -86.1 
 
 16.77 
 
 -87.1 
 
 17.38 
 
 -90.3 
 
 17.99 
 
 -90.6 
 
 18.60 
 
 -91.1 
 
 19.21 
 
 -92.7 
 
 19.82 
 
 -93.2 
 
 20.43 
 
 -93.7 
 
 21.04 
 
 -96.6 
 
 21.65 
 
 -97.0 
 
 22.26 
 
 -98.0 
 
 22.87 
 
 -101.2 
 
 23.48 
 
 -101.3 
 
 24.09 
 
 -104.6 
 
 24.70 
 
 -107.6 
 
 25.30 
 
 -109.1 
 
 25.91 
 
 -109.5 
 
 26.52 
 
 -109.7 
 
 27.13 
 
 -110.1 
 
 27.74 
 
 -110.2 
 
 28.35 
 
 -110.8 
 
 28.96 
 
 -112.6 
 
 29.57 
 
 -114.7 
 
 30.18 
 
 -114.7 
 
 30.79 
 
 -115.0 
 
 31.40 
 
 -115.3 
 
 32.01 
 
 -116.8 
 
 32.62 
 
 -118.2 
 
 33.23 
 
 -118.2 
 
 33.84 
 
 -119.2 
 
 34.45 
 
 -126.3 
 
 35.06 
 
 -130.0 
 
 35.67 
 
 -130.4 
 
 36.28 
 
 -130.4 
 
 36.89 
 
 -131.0 
 
 37.50' 
 
 -132.6 
 
 38.11 
 
 -134.6 
 
 38.72 
 
 -134.9 
 
 39.33 
 
 -135.2 
 
 39.94 
 
 -135.5 
 
 40.55 
 
 -135.9 
 
 41.16 
 
 -136.2 
 
 41.77 
 
 -138.7 
 
 42.38 
 
 -139.1 
 
 42.99 
 
 -141.0 
 
 43.60 
 
 -142.5 
 
 44.21 
 
 -144.0 
 
 44.82 
 
 -146.0 
 
 45.43 
 
 -146.5 
 
 46.04 
 
 -148.0 
 
 46.65 
 
 -148.1 
 
 47.26 
 
 -149.4 
 
 47.87 
 
 -150.0 
 
 48.48 
 
 -150.9 
 
 49.09 
 
 -151.2 
 
 49.70 
 
 -153.9 
 
 50.30 
 
 -154.6 
 
 50.91 
 
 -157.7 
 
 51.52 
 
 -158.8 
 
 52.13 
 
 -160.5 
 
 52.74 
 
 -161.2 
 
 53.35 
 
 -162.1 
 
 53.96 
 
 -162.3 
 
 54.57 
 
 -163.7 
 
 55.18 
 
 -163.8 
 
 55.79 
 
 -165.7 
 
 56.40 
 
 -165.8 
 
 57.01 
 
 -166.3 
 
 57.62 
 
 -168.2 
 
 58,23 
 
 -168.3 
 
 58.84 
 
 -169.6 
 
 59.45 
 
 -170.5 
 
 60.06 
 
 -170.6 
 
 60.67 
 
 -171.3 
 
 61.28 
 
 -171.5 
 
 61.89 
 
 -172.1 
 
 62.50 
 
 -173.1 
 
 63.11 
 
 -173.5 
 
 63.72 
 
 -174.2 
 
 64.33 
 
 -174.9 
 
 64.94 
 
 -175.3 
 
 65.55 
 
 -176.4 
 
 66.16 
 
 -177.2 
 
 66.77 
 
 -180.1 
 
 67.38 
 
 -180.3 
 
 67.99 
 
 -180.4 
 
 68.60 
 
 -182.8 
 
 69.21 
 
 -183.4 
 
 69.82 
 
 -183.7 
 
 70.43 
 
 -186.1 
 
 71.04 
 
 -186.2 
 
 71.65 
 
 -186.3 
 
 72.26 
 
 -193.4 
 
 72.87 
 
 -194.5 
 
 73.48 
 
 -195.1 
 
 74.09 
 
 -195.7 
 
 74.70 
 
 -196.1 
 
 75.30 
 
 -197.3 
 
 75.91 
 
 -197.4 
 
 76.52 
 
 -198.8 
 
 77.13 
 
 -200.1 
 
 77.74 
 
 -200.7 
 
 78.35 
 
 -203.3 
 
 78.96 
 
 -204.9 
 
 79.57 
 
 -205.5 
 
 80.18 
 
 -206.7 
 
 80.79 
 
 -208.9 
 
 81.40 
 
 -209.6 
 
 82.01 
 
 -209.7 
 
 82.62 
 
 -210.2 
 
 83.23 
 
 -212.0 
 
 83.84 
 
 -212.5 
 
 84.45 
 
 -217.3 
 
 85.06 
 
 -220.1 
 
 85.67 
 
 -221.1 
 
 86.28 
 
 -221.7 
 
 86.89 
 
 -222.6 
 
 87.50 
 
 -225.0 
 
 88.11 
 
 -227.5 
 
 88.72 
 
 -227.9 
 
 89.33 
 
 -229.4 
 
 89.94 
 
 -230.3 
 
 90.55 
 
 -234.0 
 
 91.16 
 
 -238.6 
 
 91.77 
 
 -239.8 
 
 92.3K 
 
 -245.3 
 
 S2.99 
 
 -248.1 
 
 93.60 
 
 -250.6 
 
 94.21 
 
 -252.3 
 
 94.82 
 
 -252.8 
 
 95.43 
 
 -263.5 
 
 96.04 
 
 -264.9 
 
 96.65 
 
 -270.7 
 
 97.26 
 
 -271.5 
 
 97.87 
 
 -277.4 
 
 98.48 
 
 -288.5 
 
 99.09 
 
 -290.9 
 
 99.70 
 
 
 
 
 
 
 
 
 
APPENDIX E 127 
 
 Table E-3. (Continued) 
 
 
 
 
 
 0- 
 
 -100 METERS, STATION 90001 
 
 , MONTH 8 
 
 
 
 
 
 
 -37.7 
 
 0.27 
 
 -39.1 
 
 0.81 
 
 -45.2 
 
 1.35 
 
 -47.7 
 
 1.89 
 
 -48.2 
 
 2.43 
 
 -49.4 
 
 2.97 
 
 -57.9 
 
 3.51 
 
 -74.0 
 
 4.05 
 
 -74.2 
 
 4.59 
 
 -74.3 
 
 5.14 
 
 -79.5 
 
 5.68 
 
 -79.7 
 
 6.22 
 
 -80.0 
 
 6.76 
 
 -82.2 
 
 7.30 
 
 -84.2 
 
 7.84 
 
 -84.9 
 
 8.38 
 
 -89.1 
 
 8.92 
 
 -89.9 
 
 9.46 
 
 -91.7 
 
 10.00 
 
 -93.9 
 
 10.54 
 
 -94.4 
 
 11.08 
 
 -95.6 
 
 11.62 
 
 -97.9 
 
 12.16 
 
 -99.4 
 
 12.70 
 
 -99.6 
 
 13.24 
 
 -99.9 
 
 13.78 
 
 -105.3 
 
 14.32 
 
 -107.0 
 
 14.86 
 
 -108.9 
 
 15.41 
 
 -109.1 
 
 15.95 
 
 -111.1 
 
 16.49 
 
 -113.3 
 
 17.03 
 
 -113.6 
 
 17.57 
 
 -113.7 
 
 18.11 
 
 -113.8 
 
 18.65 
 
 -114.6 
 
 19.19 
 
 -115.1 
 
 19.73 
 
 -116.1 
 
 20.27 
 
 -116.9 
 
 20.81 
 
 -117.2 
 
 21.35 
 
 -120.1 
 
 21.89 
 
 122.3 
 
 22.43 
 
 -124.7 
 
 22.97 
 
 -124.7 
 
 23.51 
 
 -126.8 
 
 24.05 
 
 -127.4 
 
 24.59 
 
 -127.9 
 
 25.14 
 
 -130.2 
 
 25.68 
 
 -130.4 
 
 26.22 
 
 -131.9 
 
 26.76 
 
 -132.1 
 
 27.30 
 
 -132.4 
 
 27.84 
 
 -132.9 
 
 28.38 
 
 -133.3 
 
 28.92 
 
 -133.6 
 
 29.46 
 
 -134.0 
 
 30.00 
 
 -135.4 
 
 30.54 
 
 -135.5 
 
 31.08 
 
 -135.5 
 
 31.62 
 
 -135.6 
 
 32.16 
 
 -136.7 
 
 32.70 
 
 -136.7 
 
 33.24 
 
 -137.1 
 
 33.78 
 
 -139.6 
 
 34.32 
 
 -139.8 
 
 34.86 
 
 -140.8 
 
 35.41 
 
 -141.5 
 
 35.95 
 
 -141.6 
 
 36.49 
 
 -141.7 
 
 37.03 
 
 -143.9 
 
 37.57 
 
 -145.0 
 
 38.11 
 
 -145.1 
 
 38.65 
 
 -145.3 
 
 39.19 
 
 -147.0 
 
 39.73 
 
 -147.6 
 
 40.27 
 
 -149.1 
 
 40.81 
 
 -150.3 
 
 41.35 
 
 -150.6 
 
 41.89 
 
 -152.2 
 
 42.43 
 
 -154.0 
 
 42.97 
 
 -154.5 
 
 43.51 
 
 -154.6 
 
 44.05 
 
 -156.0 
 
 44.59 
 
 -157.4 
 
 45.14 
 
 -158.1 
 
 45.68 
 
 -158.5 
 
 46.22 
 
 -161.1 
 
 46.76 
 
 -161.6 
 
 47.30 
 
 -161.8 
 
 47.84 
 
 -163.6 
 
 48.38 
 
 -164.1 
 
 48.92 
 
 -165.2 
 
 49.46 
 
 -166.3 
 
 50.00 
 
 -168.6 
 
 50.54 
 
 -169.3 
 
 51.08 
 
 -169.9 
 
 51.62 
 
 -170.7 
 
 52.16 
 
 -170.7 
 
 52.70 
 
 -170.9 
 
 53.24 
 
 -171.4 
 
 53.78 
 
 -172.9 
 
 54.32 
 
 -173.3 
 
 54.86 
 
 -175.5 
 
 55.41 
 
 -176.2 
 
 55.95 
 
 -177.2 
 
 56.49 
 
 -177.3 
 
 57.03 
 
 -177.7 
 
 57.57 
 
 -180.4 
 
 58.11 
 
 -180.5 
 
 58.65 
 
 -181.1 
 
 59.19 
 
 -182.5 
 
 59.73 
 
 -182.8 
 
 60.27 
 
 -182.9 
 
 60.81 
 
 -183.4 
 
 61.35 
 
 -183.5 
 
 61.89 
 
 -185.7 
 
 62.43 
 
 -185.9 
 
 62.97 
 
 -186.2 
 
 63.51 
 
 -186.5 
 
 64.05 
 
 -187.9 
 
 64.59 
 
 -188.4 
 
 65.14 
 
 -188.5 
 
 65.68 
 
 -189.2 
 
 66.22 
 
 -190.0 
 
 66.76 
 
 -190.4 
 
 67.30 
 
 -192.7 
 
 67.84 
 
 -192.8 
 
 68.38 
 
 -192.8 
 
 68.92 
 
 -196.1 
 
 69.46 
 
 -196.7 
 
 70.00 
 
 -198.7 
 
 70.54 
 
 -199.2 
 
 71.08 
 
 -203.3 
 
 71.62 
 
 -204.3 
 
 72.16 
 
 -204.4 
 
 72.70 
 
 -206.6 
 
 73.24 
 
 -207.4 
 
 73.78 
 
 -207.4 
 
 74.32 
 
 -207.6 
 
 74.86 
 
 -208.0 
 
 75.41 
 
 -210.6 
 
 75.95 
 
 -210.8 
 
 76.49 
 
 -211.4 
 
 77.03 
 
 -211.5 
 
 77.57 
 
 -213.7 
 
 78.11 
 
 -214.4 
 
 78.65 
 
 -215.8 
 
 79.19 
 
 -216.1 
 
 79.73 
 
 -218.3 
 
 80.27 
 
 -218.9 
 
 80.81 
 
 -219.3 
 
 81.35 
 
 -219.4 
 
 81.89 
 
 -220.0 
 
 82.43 
 
 -221.1 
 
 82.97 
 
 -221.2 
 
 83.51 
 
 -221.2 
 
 84.05 
 
 -222.5 
 
 84.59 
 
 -225.3 
 
 85.14 
 
 -231.0 
 
 85.68 
 
 -232.9 
 
 86.22 
 
 -233.5 
 
 86.76 
 
 -235.2 
 
 87.30 
 
 -235.6 
 
 87.84 
 
 -236.3 
 
 88.38 
 
 -238.5 
 
 88.92 
 
 -243.9 
 
 89.46 
 
 —244.8 
 
 90.00 
 
 -248.1 
 
 90.54 
 
 -252.9 
 
 91.08 
 
 -253.2 
 
 91.62 
 
 -255.4 
 
 92.16 
 
 -257.3 
 
 92.70 
 
 -260.0 
 
 93.24 
 
 -260.1 
 
 93.78 
 
 -260.2 
 
 94.32 
 
 -261.8 
 
 94.86 
 
 -262.9 
 
 95.41 
 
 -265.7 
 
 95.95 
 
 -271.5 
 
 96.49 
 
 -279.1 
 
 97.03 
 
 -279.7 
 
 97.57 
 
 -282.1 
 
 98.11 
 
 -293.5 
 
 98.65 
 
 -319.1 
 
 99.19 
 
 -328.5 
 
 99.73 
 
 
 
 
 
 
 
 
 
 
 
 
 
 0- 
 
 100 METERS, STATION 90001, 
 
 MONTH 11 
 
 
 
 
 
 
 -9.0 
 
 0.31 
 
 -9.9 
 
 0.93 
 
 -17.9 
 
 1.55 
 
 -21.3 
 
 2.17 
 
 -22.5 
 
 2.80 
 
 -25.0 
 
 3.42 
 
 -25.7 
 
 4.04 
 
 -27.2 
 
 4.66 
 
 -27.5 
 
 5.28 
 
 -27.6 
 
 5.90 
 
 -27.7 
 
 6.52 
 
 -27.9 
 
 7.14 
 
 -28.1 
 
 7.76 
 
 -29.0 
 
 8.39 
 
 -29.8 
 
 9.01 
 
 -31.6 
 
 9.63 
 
 -34.9 
 
 10.25 
 
 -37.0 
 
 10.87 
 
 -38.2 
 
 11.49 
 
 -38.4 
 
 12.1 
 
 -39.4 
 
 12.73 
 
 -40.3 
 
 13.35 
 
 -40.5 
 
 13.98 
 
 -42.4 
 
 14.60 
 
 -44.1 
 
 15.22 
 
 -45.2 
 
 15.84 
 
 -46.3 
 
 16.46 
 
 -46.3 
 
 17.08 
 
 -46.3 
 
 17.70 
 
 -46.4 
 
 18.32 
 
 -48.1 
 
 18.94 
 
 -48.8 
 
 19.57 
 
 -49.0 
 
 20.19 
 
 -49.0 
 
 20.81 
 
 -49.5 
 
 21.43 
 
 -49.6 
 
 22.05 
 
 -50.0 
 
 22.67 
 
 -51.0 
 
 23.29 
 
 -51.1 
 
 23.91 
 
 -51.3 
 
 24.53 
 
 -51.4 
 
 25.16 
 
 -53.4 
 
 25.78 
 
 -53.4 
 
 26.40 
 
 -53.6 
 
 27.02 
 
 -54.3 
 
 27.64 
 
 -54.6 
 
 28.26 
 
 -54.7 
 
 28.88 
 
 -55.1 
 
 29.50 
 
 -55.3 
 
 30.12 
 
 -55.8 
 
 30.75 
 
 -56.0 
 
 31.37 
 
 -56.0 
 
 31.99 
 
 -56.5 
 
 32.61 
 
 -57.3 
 
 33.23 
 
 -57.9 
 
 33.85 
 
 -58.3 
 
 34.47 
 
 -58.4 
 
 35.09 
 
 -58.7 
 
 35.71 
 
 -58.8 
 
 36.34 
 
 -59.3 
 
 36.96 
 
 -59.5 
 
 37.58 
 
 -59.8 
 
 38.20 
 
 -60.1 
 
 38.82 
 
 -60.7 
 
 39.44 
 
 -61.0 
 
 40.06 
 
 -61.5 
 
 40.68 
 
 -61.6 
 
 41.30 
 
 -61.8 
 
 41.93 
 
 -62.4 
 
 42.55 
 
 -62.4 
 
 43.17 
 
 -62.8 
 
 43.79 
 
 -63.7 
 
 44.41 
 
 -64.0 
 
 45.03 
 
 -64.5 
 
 45.65 
 
 -65.6 
 
 46.27 
 
 -65.8 
 
 46.89 
 
 -66.0 
 
 47.52 
 
 -66.7 
 
 48.14 
 
 -67.0 
 
 48.76 
 
 -67.3 
 
 49.38 
 
 -68.1 
 
 50.00 
 
 -68.7 
 
 50.62 
 
 -69.2 
 
 51.24 
 
 -69.6 
 
 51.86 
 
 -72.4 
 
 52.48 
 
 -72.8 
 
 53.11 
 
 -73.3 
 
 53.73 
 
 -73.3 
 
 54.35 
 
 -73.5 
 
 54.97 
 
 -73.6 
 
 55.59 
 
 -73.7 
 
 56.21 
 
 -74.8 
 
 56.83 
 
 -75.4 
 
 57.45 
 
 -75.7 
 
 58.07 
 
 -76.1 
 
 58.70 
 
 -76.4 
 
 59.32 
 
 -76.5 
 
 59.94 
 
 -76.6 
 
 60.56 
 
 -76.7 
 
 61.18 
 
 -77.0 
 
 61.80 
 
 -78.0 
 
 62.42 
 
 -78.1 
 
 63.04 
 
 -78.3 
 
 63.66 
 
 -78.3 
 
 64.29 
 
 -78.8 
 
 64.91 
 
 -79.2 
 
 65.53 
 
 -79.8 
 
 66.15 
 
 -80.7 
 
 66.77 
 
 -80.8 
 
 67.39 
 
 -80.9 
 
 68.01 
 
 -81.1 
 
 68.63 
 
 -81.6 
 
 69.25 
 
 -83.2 
 
 69.88 
 
 -83.2 
 
 70.50 
 
 -83.8 
 
 71.12 
 
 -84.0 
 
 71.74 
 
 -84.1 
 
 72.36 
 
 -84.2 
 
 72.98 
 
 -84.3 
 
 73.60 
 
 -85.2 
 
 74.22 
 
 -85.2 
 
 74.84 
 
 -85.2 
 
 75.47 
 
 -85.3 
 
 76.09 
 
 -85.5 
 
 76.71 
 
 -85.8 
 
 77.33 
 
 -86.0 
 
 77.95 
 
 -86.0 
 
 78.57 
 
 -86.5 
 
 79.19 
 
 -86.9 
 
 79.81 
 
 -88.0 
 
 80.43 
 
 -88.9 
 
 81.06 
 
 -89.3 
 
 81.68 
 
 -89.8 
 
 82.30 
 
 -90.4 
 
 82.92 
 
 -91.6 
 
 83.54 
 
 -95.1 
 
 84.16 
 
 -96.6 
 
 84.78 
 
 -96.8 
 
 85.40 
 
 -98.2 
 
 86.02 
 
 -98.3 
 
 86.65 
 
 -98.9 
 
 87.27 
 
 -99.6 
 
 87.89 
 
 -99.7 
 
 88.51 
 
 -100.7 
 
 89.13 
 
 -100.9 
 
 89.75 
 
 -101.2 
 
 90.37 
 
 -101.7 
 
 90.99 
 
 -102.5 
 
 91.61 
 
 -104.1 
 
 92.24 
 
 -104.9 
 
 92.86 
 
 -107.1 
 
 93.48 
 
 -107.4 
 
 94.10 
 
 -110.4 
 
 94.72 
 
 -110.6 
 
 95.34 
 
 -114.8 
 
 95.96 
 
 -117.6 
 
 96.58 
 
 -120.7 
 
 97.20 
 
 -120.8 
 
 97.83 
 
 -122.1 
 
 98.45 
 
 -132.5 
 
 99.07 
 
 -158.5 
 
 99.69 
 
 Table E-4. Analysis of ground-based superrefractive and ducting layers: Amundsen-Scott, Antarctica. 
 
 FEBRUARY 
 
 87 Profiles Skipped 
 
 99 Profiles Read 
 
 Number of Ducts 2 
 
 Number of Superrefractive Layers 10 
 
 
 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, MONTH 2 
 
 -157.500 
 
 25.00 | 
 
 -164.815 
 
 75.00 
 
 
 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES, STATION 90001, MONTH 2 
 
 0.120 
 
 25.00 | 
 
 0.108 
 
 75.00 | 
 
 
 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 2 
 
 3693.683 
 
 25.00 
 
 1191.104 
 
 75.00 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS. STATION 90001, MONTH 2 
 
 -100.000 5.00 -101.626 15.00 -103.922 25.00 -103.960 35.00 -117.391 45.00 -118.033 55.00 -124.793 65.00 
 
 -129.054 75.00 -138.182 85.00 -148.182 95.00 
 
128 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table E-4. {Continued) 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001 
 
 , MONTH 2 
 
 
 
 0.148 5.00 
 0.102 75.00 
 
 0.123 15.00 
 0.101 85.00 
 
 0.122 25.00 
 0.092 95.00 
 
 0.121 35.00 
 
 0.110 45.00 
 
 0.110 55.00 
 
 0.104 
 
 65.00 
 
 
 MAY 
 
 45 Profiles Skipped 
 164 Profiles Read 
 Number of Ducts 79 
 Number of Superrefractive Layers 40 
 
 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, 
 
 MONTH 5 
 
 
 
 
 
 
 -157.396 
 
 0.63 
 
 -158.605 
 
 1.90 
 
 -158.929 
 
 3.16 
 
 -159.596 
 
 4.43 
 
 -159.873 
 
 5.70 
 
 -160.156 
 
 6.96 
 
 -161.207 
 
 8.23 
 
 -164.045 
 
 9.49 
 
 -165.248 
 
 10.76 
 
 -165.306 
 
 12.03 
 
 -165.789 
 
 13.29 
 
 -166.667 
 
 14.56 
 
 -167.290 
 
 15.82 
 
 -167.480 
 
 17.09 
 
 -167.925 
 
 18.35 
 
 -169.595 
 
 19.62 
 
 -170.732 
 
 20.89 
 
 -172.414 
 
 22.15 
 
 -173.810 
 
 23.42 
 
 -174.737 
 
 24.68 
 
 -175.163 
 
 25.95 
 
 -175.694 
 
 27.22 
 
 -176.119 
 
 28.48 
 
 -176.774 
 
 29.75 
 
 -176.800 
 
 31.01 
 
 -180.952 
 
 32.28 
 
 -182.906 
 
 33.54 
 
 -183.511 
 
 34.81 
 
 -187.629 
 
 36.08 
 
 -187.850 
 
 37.34 
 
 -188.095 
 
 38.61 
 
 -192.035 
 
 39.87 
 
 -192.105 
 
 41.14 
 
 -192.241 
 
 42.41 
 
 -196.241 
 
 43.67 
 
 -196.522 
 
 44.94 
 
 -198.742 
 
 46.20 
 
 -200.962 
 
 47.47 
 
 -202.778 
 
 48.73 
 
 -202.857 
 
 50.00 
 
 -203.175 
 
 51.27 
 
 -205.263 
 
 52.53 
 
 -206.542 
 
 53.80 
 
 -210.204 
 
 55.06 
 
 -210.959 
 
 56.33 
 
 -214.019 
 
 57.59 
 
 -219.231 
 
 58.86 
 
 -220.192 
 
 60.13 
 
 -221.429 
 
 61.39 
 
 -224.107 
 
 62.66 
 
 -224.762 
 
 63.92 
 
 -231.633 
 
 65.19 
 
 -231.818 
 
 66.46 
 
 -232.824 
 
 67.72 
 
 -233.333 
 
 68.99 
 
 -237.903 
 
 70.25 
 
 -239.583 
 
 71.52 
 
 -240.230 
 
 72.78 
 
 -244.706 
 
 74.05 
 
 -246.808 
 
 75.32 
 
 -250.000 
 
 76.58 
 
 -259.375 
 
 77.85 
 
 -263.265 
 
 79.11 
 
 -266.279 
 
 80.38 
 
 -266.327 
 
 81.65 
 
 -272.500 
 
 82.91 
 
 -275.000 
 
 84.18 
 
 -276.389 
 
 85.44 
 
 -284.615 
 
 86.71 
 
 -285.981 
 
 87.97 
 
 -298.947 
 
 89.24 
 
 -302.410 
 
 90.51 
 
 -306.383 
 
 91.77 
 
 -351.282 
 
 93.04 
 
 -375.000 
 
 94.30 
 
 -377.586 
 
 95.57 
 
 -405.128 
 
 96.84 
 
 -422.414 
 
 98.10 
 
 -789.655 
 
 99.37 
 
 
 
 
 
 
 
 
 
 
 
 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES 
 
 , STATION 90001, MONTH 5 
 
 
 
 
 
 
 0.215 
 
 0.63 
 
 0.196 
 
 1.90 
 
 0.188 
 
 3.16 
 
 0.178 
 
 4.43 
 
 0.171 
 
 5.70 
 
 0.169 
 
 6.96 
 
 0.164 
 
 8.23 
 
 0.159 
 
 9.49 
 
 0.157 
 
 10.76 
 
 0.155 
 
 12.03 
 
 0.153 
 
 13.29 
 
 0.152 
 
 14.56 
 
 0.148 
 
 15.82 
 
 0.144 
 
 17.09 
 
 0.141 
 
 18.35 
 
 0.134 
 
 19.62 
 
 0.133 
 
 20.89 
 
 0.131 
 
 22.15 
 
 0.128 
 
 23.42 
 
 0.126 
 
 24.68 
 
 0.126 
 
 25.95 
 
 0.126 
 
 27.22 
 
 0.125 
 
 28.48 
 
 0.124 
 
 29.75 
 
 0.123 
 
 31.01 
 
 0.120 
 
 32.28 
 
 0.117 
 
 33.54 
 
 0.116 
 
 34.81 
 
 0.116 
 
 36.08 
 
 0.116 
 
 37.34 
 
 0.115 
 
 38.61 
 
 0.113 
 
 39.87 
 
 0.112 
 
 41.14 
 
 0.112 
 
 42.41 
 
 0.108 
 
 43.67 
 
 0.107 
 
 44.94 
 
 0.107 
 
 46.20 
 
 0.107 
 
 47.47 
 
 0.107 
 
 48.73 
 
 0.107 
 
 50.00 
 
 0.106 
 
 51.27 
 
 0.105 
 
 52.53 
 
 0.105 
 
 53.80 
 
 0.105 
 
 55.06 
 
 0.104 
 
 56.33 
 
 0.104 
 
 57.59 
 
 0.104 
 
 58.86 
 
 0.099 
 
 60.13 
 
 0.098 
 
 61.39 
 
 0.098 
 
 62.66 
 
 (.098 
 
 63.92 
 
 0.098 
 
 65.19 
 
 0.097 
 
 66.46 
 
 0.096 
 
 67.72 
 
 0.096 
 
 68.99 
 
 0.095 
 
 70.25 
 
 0.095 
 
 71.52 
 
 0.094 
 
 72.78 
 
 0.087 
 
 74.05 
 
 0.086 
 
 75.32 
 
 0.085 
 
 76.58 
 
 0.083 
 
 77.85 
 
 0.078 
 
 79.11 
 
 0.076 
 
 80.38 
 
 0.076 
 
 81.65 
 
 0.075 
 
 82.91 
 
 0.073 
 
 84.18 
 
 0.072 
 
 85.44 
 
 0.066 
 
 86.71 
 
 0.060 
 
 87.97 
 
 0.058 
 
 89.24 
 
 0.058 
 
 90.51 
 
 0.049 
 
 91.77 
 
 0.048 
 
 93.04 
 
 0.047 
 
 94.30 
 
 0.039 
 
 95.57 
 
 0.039 
 
 96.84 
 
 0.029 
 
 98.10 
 
 0.028 
 
 99.37 
 
 
 
 
 
 
 
 
 
 
 
 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 5 
 
 2429.613 
 
 0.63 
 
 2325.397 
 
 1.90 
 
 2227.842 
 
 3.16 
 
 1903.914 
 
 4.43 
 
 1718.862 
 
 5.70 
 
 1450.565 
 
 6.96 
 
 1439.236 
 
 8.23 
 
 1108.889 
 
 9.49 
 
 1107.595 
 
 10.76 
 
 1063.192 
 
 12.03 
 
 1054.213 
 
 13.29 
 
 1040.684 
 
 14.56 
 
 1037.927 
 
 15.82 
 
 980.129 
 
 17.09 
 
 961.749 
 
 18.35 
 
 957.395 
 
 19.62 
 
 954.692 
 
 20.89 
 
 913.759 
 
 22.15 
 
 847.641 
 
 23.42 
 
 838.695 
 
 24.68 
 
 820.140 
 
 25.95 
 
 816.260 
 
 27.22 
 
 810.394 
 
 28.48 
 
 777.463 
 
 29.75 
 
 764.293 
 
 31.01 
 
 712.759 
 
 32.28 
 
 710.191 
 
 33.54 
 
 662.327 
 
 34.81 
 
 646.673 
 
 36.08 
 
 610.797 
 
 37.34 
 
 603.274 
 
 38.61 
 
 592.484 
 
 39.87 
 
 586.278 
 
 41.14 
 
 582.761 
 
 42.41 
 
 573.161 
 
 43.67 
 
 563.175 
 
 44.94 
 
 553.068 
 
 46.20 
 
 538.194 
 
 47.47 
 
 534.227 
 
 48.73 
 
 530.990 
 
 50.00 
 
 528.221 
 
 51.27 
 
 522.541 
 
 52.53 
 
 515.639 
 
 53.80 
 
 512.172 
 
 55.06 
 
 507.723 
 
 56.33 
 
 506.381 
 
 57.59 
 
 502.051 
 
 58.86 
 
 494.731 
 
 60.13 
 
 484.498 
 
 61.39 
 
 483.106 
 
 62.66 
 
 482.604 
 
 63.92 
 
 482.287 
 
 65.19 
 
 481.507 
 
 66.46 
 
 476.108 
 
 67.72 
 
 472.742 
 
 68.99 
 
 465.027 
 
 70.25 
 
 450.911 
 
 71.52 
 
 449.615 
 
 72.78 
 
 449.164 
 
 74.05 
 
 448.447 
 
 75.32 
 
 445.739 
 
 76.58 
 
 439.734 
 
 77.85 - 
 
 437.183 
 
 79.11 
 
 435.225 
 
 80.38 
 
 424.335 
 
 81.65 
 
 412.328 
 
 82.91 
 
 394.994 
 
 84.18 
 
 390.938 
 
 85.44 
 
 390.912 
 
 86.71 
 
 387.054 
 
 87.97 
 
 370.600 
 
 89.24 
 
 340.803 
 
 90.51 
 
 338.249 
 
 91.77 
 
 302.637 
 
 93.04 
 
 299.088 
 
 94.30 
 
 290.004 
 
 95.57 
 
 287.878 
 
 96.84 
 
 282.840 
 
 98.10 
 
 266.106 
 
 99.37 
 
 
 
 
 
 
 
 
 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 5 
 
 -103.333 
 
 1.25 
 
 -103.347 
 
 3.75 
 
 -103.791 
 
 6.25 
 
 -106.587 
 
 8.75 
 
 -106.667 
 
 11.25 
 
 -111.515 
 
 13.75 
 
 -111.892 
 
 16.25 
 
 -112.632 
 
 18.75 
 
 -113.333 
 
 21.25 
 
 -114.465 
 
 23.75 
 
 -115.741 
 
 26.25 
 
 -120.084 
 
 28.75 
 
 -123.478 
 
 31.25 
 
 -125.253 
 
 33.75 
 
 -126.087 
 
 36.25 
 
 -126.214 
 
 38.75 
 
 -127.700 
 
 41.25 
 
 -127.835 
 
 43.75 
 
 -128.144 
 
 46.25 
 
 -130.000 
 
 48.75 
 
 -130.337 
 
 51.25 
 
 -130.469 
 
 53.75 
 
 -133.110 
 
 56.25 
 
 -133.588 
 
 58.75 
 
 -134.375 
 
 61.25 
 
 -135.714 
 
 63.75 
 
 -138.967 
 
 66.25 
 
 -139.655 
 
 68.75 
 
 -139.662 
 
 71.25 
 
 -140.845 
 
 73.75 
 
 -141.799 
 
 76.25 
 
 -142.938 
 
 78.75 
 
 -143.158 
 
 81.25 
 
 -147.183 
 
 83.75 
 
 -147.619 
 
 86.25 
 
 -148.980 
 
 88.75 
 
 -150.649 
 
 91.25 
 
 -155.208 
 
 93.75 
 
 -155.556 
 
 96.25 
 
 -155.797 
 
 98.75 
 
 
 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 5 
 
 0.300 
 
 1.25 
 
 0.299 
 
 3.75 
 
 0.240 
 
 6.25 
 
 0.239 
 
 8.75 
 
 0.239 
 
 11.25 
 
 0.237 
 
 13.75 
 
 0.213 
 
 16.25 
 
 0.213 
 
 18.75 
 
 0.213 
 
 21.25 
 
 0.211 
 
 23.75 
 
 0.206 
 
 26.25 
 
 0.198 
 
 28.75 
 
 0.189 
 
 31.25 
 
 0.185 
 
 33.75 
 
 0.184 
 
 36.25 
 
 0.178 
 
 38.75 
 
 0.177 
 
 41.25 
 
 0.174 
 
 43.75 
 
 0.167 
 
 46.25 
 
 0.167 
 
 48.75 
 
 0.165 
 
 51.25 
 
 0.159 
 
 53.75 
 
 0.154 
 
 56.25 
 
 0.147 
 
 58.75 
 
 0.142 
 
 61.25 
 
 0.138 
 
 63.75 
 
 0.131 
 
 66.25 
 
 0.128 
 
 68.75 
 
 0.126 
 
 71.25 
 
 0.120 
 
 73.75 
 
 0.115 
 
 76.25 
 
 0.108 
 
 78.75 
 
 0.098 
 
 81.25 
 
 0.097 
 
 83.75 
 
 0.096 
 
 86.25 
 
 0.096 
 
 88.75 
 
 0.095 
 
 91.25 
 
 0.095 
 
 93.75 
 
 0.090 
 
 96.25 
 
 0.080 
 
 98.75 
 
 
 
 
 
 THICKNESS AND GRADIENT OF SUPERREFRACTIVE LAYERS OVER 300 METERS THICK 
 
 0.30000 
 
 -106.66667 
 
APPENDIX E 129 
 
 Table E-4. (Continued) 
 
 AUGUST 
 
 31 Profiles Skipped 
 185 Profiles Read 
 Number of Ducts 104 
 Number of Superrefractive Layers 50 
 
 CUMULATIVE DISTRIBUTION OF DUCTING GRADIENTS, STATION 90001, 
 
 MONTH 8 
 
 
 
 
 
 
 -157.059 
 
 0.48 
 
 -157.639 
 
 1.44 
 
 -157.843 
 
 2.40 
 
 -158.268 
 
 3.37 
 
 -159.236 
 
 4.33 
 
 -160.759 
 
 5.20 
 
 -161.881 
 
 6.25 
 
 -162.667 
 
 7.21 
 
 -163.265 
 
 8.17 
 
 — 16(1.355 
 
 9.13 
 
 -167.262 
 
 10.10 
 
 -167.532 
 
 11.06 
 
 -168.033 
 
 12.02 
 
 -170.149 
 
 12.98 
 
 -170.248 
 
 13.94 
 
 -170.408 
 
 14.90 
 
 -172.308 
 
 15.8? 
 
 -172.727 
 
 16.83 
 
 -172.727 
 
 17.79 
 
 -173.404 
 
 18.75 
 
 -173.984 
 
 19.71 
 
 -175.510 
 
 20.67 
 
 -175.949 
 
 21.63 
 
 -176.056 
 
 22.60 
 
 -176.111 
 
 23.56 
 
 -176.190 
 
 24.52 
 
 -176.786 
 
 25.48 
 
 -177.165 
 
 26.44 
 
 -177.922 
 
 27.40 
 
 -181.250 
 
 28.37 
 
 -182.292 
 
 29.33 
 
 -182.895 
 
 30.29 
 
 -182.993 
 
 31.25 
 
 -184.127 
 
 32.21 
 
 -184.536 
 
 33.17 
 
 -185.185 
 
 34.13 
 
 -185.315 
 
 35.10 
 
 -186.170 
 
 36.06 
 
 -190.816 
 
 37.02 
 
 -191.919 
 
 37.98 
 
 -194.286 
 
 38.94 
 
 -196.939 
 
 39.90 
 
 -198.889 
 
 40.87 
 
 -200.000 
 
 41.83 
 
 -200.000 
 
 42.79 
 
 -200.769 
 
 43.75 
 
 -202.062 
 
 44.71 
 
 -202.069 
 
 45.67 
 
 -202.083 
 
 46.63 
 
 -202.778 
 
 47.60 
 
 -203.738 
 
 48.56 
 
 -205.517 
 
 49.52 
 
 -205.674 
 
 50.48 
 
 -208.571 
 
 51.44 
 
 -210.526 
 
 52.40 
 
 -211.111 
 
 53.37 
 
 -211.702 
 
 54.33 
 
 -214.286 
 
 55.29 
 
 -214.943 
 
 56.25 
 
 -216.239 
 
 57.21 
 
 -218.584 
 
 58.17 
 
 -218.750 
 
 59.13 
 
 -220.619 
 
 60.10 
 
 -221.552 
 
 61.06 
 
 -222.078 
 
 62.02 
 
 -225.000 
 
 62.98 
 
 -230.645 
 
 63.94 
 
 -232.174 
 
 64.90 
 
 -232.941 
 
 65.87 
 
 -234.694 
 
 66.83 
 
 -236.842 
 
 67.79 
 
 -237.113 
 
 68.75 
 
 -239.175 
 
 69.71 
 
 -243.434 
 
 70.67 
 
 -243.750 
 
 71.63 
 
 -243.966 
 
 72.60 
 
 -245.977 
 
 73.56 
 
 -252.885 
 
 74.52 
 
 -255.652 
 
 75.48 
 
 -256.701 
 
 76.44 
 
 -257.843 
 
 77.40 
 
 -258.491 
 
 ',8.37 
 
 -264.935 
 
 79.33 
 
 -266.154 
 
 80.29 
 
 -266.279 
 
 81.25 
 
 -267.257 
 
 82.21 
 
 -268.817 
 
 83.17 
 
 -271.429 
 
 84.13 
 
 -277.586 
 
 85.10 
 
 -278.261 
 
 86.06 
 
 -281.395 
 
 87.02 
 
 -281.633 
 
 87.98 
 
 -282.558 
 
 88.94 
 
 -284.058 
 
 89.90 
 
 -284.211 
 
 90.87 
 
 -288.298 
 
 91.83 
 
 -289.189 
 
 92.79 
 
 -290.566 
 
 93.75 
 
 -295.349 
 
 94.71 
 
 -298.507 
 
 95.67 
 
 -302.941 
 
 96.63 
 
 -326.923 
 
 97.60 
 
 -332.653 
 
 98.56 
 
 -408.000 
 
 99.52 
 
 
 
 CUMULATIVE DISTRIBUTION OF DUCT THICKNESSES 
 
 STATION 90001, MONTH 8 
 
 
 
 
 
 
 0.204 
 
 0.48 
 
 0.202 
 
 1.44 
 
 0.201 
 
 2.40 
 
 0.189 
 
 3.37 
 
 0.180 
 
 4.33 
 
 0.170 
 
 5.29 
 
 0.168 
 
 6.25 
 
 0.168 
 
 7.21 
 
 0.158 
 
 8.17 
 
 0.158 
 
 9.13 
 
 0.157 
 
 10.10 
 
 0.154 
 
 11.06 
 
 0.152 
 
 12.02 
 
 0.147 
 
 12.98 
 
 0.145 
 
 13.94 
 
 0.145 
 
 14.90 
 
 0.144 
 
 15.87 
 
 0.144 
 
 16.83 
 
 0.144 
 
 17.79 
 
 0.144 
 
 18.75 
 
 0.143 
 
 19.71 
 
 0.143 
 
 20.67 
 
 0.142 
 
 21.63 
 
 0.141 
 
 22.60 
 
 0.134 
 
 23.56 
 
 0.130 
 
 24.52 
 
 0.127 
 
 25.48 
 
 0.127 
 
 26.44 
 
 0.126 
 
 27.40 
 
 0.126 
 
 28.37 
 
 0.124 
 
 29.33 
 
 0.123 
 
 30.29 
 
 0.122 
 
 31.25 
 
 0.121 
 
 32.21 
 
 0.117 
 
 33.17 
 
 0.116 
 
 34.13 
 
 0.116 
 
 35.10 
 
 0.116 
 
 36.06 
 
 0.115 
 
 37.02 
 
 0.115 
 
 37.98 
 
 0.113 
 
 38.94 
 
 0.113 
 
 39.90 
 
 0.108 
 
 40.87 
 
 0.107 
 
 41.83 
 
 0.107 
 
 42.79 
 
 0.106 
 
 43.75 
 
 0.106 
 
 44.71 
 
 0.105 
 
 45.67 
 
 0.105 
 
 46.63 
 
 0.104 
 
 47.60 
 
 0.102 
 
 48.56 
 
 0.099 
 
 49.52 
 
 0.099 
 
 50.48 
 
 0.099 
 
 51.44 
 
 0.098 
 
 52.40 
 
 0.098 
 
 53.37 
 
 0.098 
 
 54.33 
 
 0.098 
 
 55.29 
 
 0.098 
 
 56.25 
 
 0.098 
 
 57.21 
 
 0.098 
 
 58.17 
 
 0.098 
 
 59.13 
 
 0.098 
 
 60.10 
 
 0.098 
 
 61.06 
 
 0.097 
 
 62.02 
 
 0.097 
 
 62.98 
 
 0.097 
 
 63.94 
 
 0.097 
 
 64.90 
 
 0.097 
 
 65.87 
 
 0.097 
 
 66.83 
 
 0.096 
 
 67.79 
 
 0.096 
 
 68.75 
 
 0.096 
 
 69.71 
 
 0.095 
 
 70.67 
 
 0.095 
 
 71.63 
 
 0.094 
 
 72.60 
 
 0.094 
 
 73.56 
 
 0.094 
 
 74.52 
 
 0.094 
 
 75.48 
 
 0.093 
 
 76.44 
 
 0.090 
 
 77.40 
 
 0.087 
 
 78.37 
 
 0.087 
 
 79.33 
 
 0.086 
 
 80.29 
 
 0.086 
 
 81.25 
 
 0.086 
 
 82.21 
 
 0.086 
 
 83.17 
 
 0.085 
 
 84.13 
 
 0.084 
 
 85.10 
 
 0.078 
 
 86.06 
 
 0.077 
 
 87.02 
 
 0.077 
 
 87.98 
 
 0.077 
 
 88.94 
 
 0.075 
 
 89.90 
 
 0.075 
 
 90.87 
 
 0.069 
 
 91.83 
 
 0.068 
 
 92.79 
 
 0.067 
 
 93.75 
 
 0.065 
 
 94.71 
 
 0.065 
 
 95.67 
 
 0.064 
 
 96.63 
 
 0.057 
 
 97.60 
 
 0.046 
 
 98.56 
 
 0.037 
 
 99.52 
 
 
 
 CUMULATIVE DISTRIBUTION OF TRAPPING FREQUENCIES, STATION 90001, MONTH 8 
 
 4257.656 
 
 0.48 
 
 2532.044 
 
 1.44 
 
 2411.305 
 
 2.40 
 
 2246.992 
 
 3.37 
 
 1828.382 
 
 4.33 
 
 1707.076 
 
 5.29 
 
 1536.587 
 
 6.25 
 
 1452.918 
 
 7.21 
 
 1329.146 
 
 8.17 
 
 1250.981 
 
 9.13 
 
 1105.087 
 
 10.10 
 
 1094.283 
 
 11.06 
 
 1054.796 
 
 12.02 
 
 1015.818 
 
 12.98 
 
 963.952 
 
 13.94 
 
 959.732 
 
 14.90 
 
 898.651 
 
 15.87 
 
 836.497 
 
 16.83 
 
 793.512 
 
 17.79 
 
 788.227 
 
 18.75 
 
 774.572 
 
 19.71 
 
 773.430 
 
 20.67 
 
 762.783 
 
 21.63 
 
 748.876 
 
 22.60 
 
 689.477 
 
 23.56 
 
 686.622 
 
 24.52 
 
 679.791 
 
 25.48 
 
 667.047 
 
 26.44 
 
 665.675 
 
 27.40 
 
 645.208 
 
 28.37 
 
 612.670 
 
 29.33 
 
 608.351 
 
 30.29 
 
 605.451 
 
 31.25 
 
 590.511 
 
 32.21 
 
 586.965 
 
 33.17 
 
 585.818 
 
 34.13 
 
 583.745 
 
 35.10 
 
 581.916 
 
 36.06 
 
 576.303 
 
 37.02 
 
 571.701 
 
 37.98 
 
 557.462 
 
 38.94 
 
 555.015 
 
 39.90 
 
 554.666 
 
 40.87 
 
 552.839 
 
 41.83 
 
 550.368 
 
 42.79 
 
 536.565 
 
 43.75 
 
 535.535 
 
 44.71 
 
 511.758 
 
 45.67 
 
 509.624 
 
 46.63 
 
 508.428 
 
 47.60 
 
 507.829 
 
 48.56 
 
 496.512 
 
 49.52 
 
 493.191 
 
 50.48 
 
 491.071 
 
 51.44 
 
 486.291 
 
 52.40 
 
 484.535 
 
 53.37 
 
 456.489 
 
 54.33 
 
 455.120 
 
 55.29 
 
 454.290 
 
 56.25 
 
 450.911 
 
 57.21 
 
 441.075 
 
 58.17 
 
 439.567 
 
 59.13 
 
 439.536 
 
 60.10 
 
 434.024 
 
 61.06 
 
 433.891 
 
 62.02 
 
 429.227 
 
 62.98 
 
 422.651 
 
 63.94 
 
 420.691 
 
 64.90 
 
 418.707 
 
 65.87 
 
 413.111 
 
 66.83 
 
 412.601 
 
 67.79 
 
 410.449 
 
 68.75 
 
 400.787 
 
 69.71 
 
 398.659 
 
 70.67 
 
 396.399 
 
 71.63 
 
 394.077 
 
 72.60 
 
 393.639 
 
 73.56 
 
 387.321 
 
 74.52 
 
 385.794 
 
 75.48 
 
 383.099 
 
 76.44 
 
 374.394 
 
 77.40 
 
 369.326 
 
 78.37 
 
 365.419 
 
 79.33 
 
 363.385 
 
 80.29 
 
 362.590 
 
 81.25 
 
 361.955 
 
 82.21 
 
 360.014 
 
 83.17 
 
 355.321 
 
 84.13 
 
 351.509 
 
 85.10 
 
 347.118 
 
 86.06 
 
 341.917 
 
 87.02 
 
 323.797 
 
 87.98 
 
 322.623 
 
 88.94 
 
 321.650 
 
 89.90 
 
 321.337 
 
 90.87 
 
 320.504 
 
 91.83 
 
 317.181 
 
 92.79 
 
 308.929 
 
 93.75 
 
 306.892 
 
 94.71 
 
 298.083 
 
 95.67 
 
 298.050 
 
 96.63 
 
 292.426 
 
 97.60 
 
 274.025 
 
 98.56 
 
 272.166 
 
 99.52 
 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 8 
 
 -104.545 
 
 1.00 
 
 -105.093 
 
 3.00 
 
 -106.000 
 
 5.00 
 
 -109.016 
 
 7.00 
 
 -109.502 
 
 9.00 
 
 -110.687 
 
 11.00 
 
 -111.111 
 
 13.00 
 
 -112.500 
 
 15.00 
 
 -113.978 
 
 17.00 
 
 -118.902 
 
 19.00 
 
 -120.257 
 
 21.00 
 
 -120.556 
 
 23.00 
 
 -122.609 
 
 25.00 
 
 -124.409 
 
 27.00 
 
 -126.894 
 
 29.00 
 
 -127.362 
 
 31.00 
 
 -127.368 
 
 33.00 
 
 -127.962 
 
 35.00 
 
 -128.090 
 
 37.00 
 
 -128.495 
 
 39.00 
 
 -129.583 
 
 41.00 
 
 -130.380 
 
 43.00 
 
 -131.250 
 
 45.00 
 
 -131.902 
 
 47.00 
 
 -132.903 
 
 49.00 
 
 -133.047 
 
 51.00 
 
 -133.444 
 
 53.00 
 
 -133.673 
 
 55.00 
 
 -134.343 
 
 57.00 
 
 -135.052 
 
 59.00 
 
 -136.598 
 
 61.00 
 
 -136.813 
 
 63.00 
 
 -137.288 
 
 65.00 
 
 -138.378 
 
 67.00 
 
 -140.217 
 
 69.00 
 
 -140.645 
 
 71.00 
 
 -142.553 
 
 73.00 
 
 -144.253 
 
 75.00 
 
 -144.324 
 
 77.00 
 
 -144.545 
 
 79.00 
 
 -146.012 
 
 81.00 
 
 -146.980 
 
 83.00 
 
 -147.489 
 
 85.00 
 
 -151.402 
 
 87.00 
 
 -152.222 
 
 89.00 
 
 -153.247 
 
 91.00 
 
 -155.224 
 
 93.00 
 
 -156.081 
 
 95.00 
 
 -156.164 
 
 97.00 
 
 -156.627 
 
 99.00 
 
 
 
 
 
 
 
 
 
 
 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 8 
 
 0.311 
 
 1.00 
 
 0.307 
 
 3.00 
 
 0.302 
 
 5.00 
 
 0.279 
 
 7.00 
 
 0.264 
 
 9.00 
 
 0.244 
 
 11.00 
 
 0.240 
 
 13.00 
 
 0.233 
 
 15.00 
 
 0.232 
 
 17.00 
 
 0.224 
 
 19.00 
 
 0.221 
 
 21.00 
 
 0.220 
 
 23.00 
 
 0.219 
 
 25.00 
 
 0.216 
 
 27.00 
 
 0.211 
 
 29.00 
 
 0.200 
 
 31.00 
 
 0.198 
 
 33.00 
 
 0.194 
 
 35.00 
 
 0.186 
 
 37.00 
 
 0.185 
 
 39.00 
 
 0.185 
 
 41.00 
 
 0.184 
 
 43.00 
 
 0.182 
 
 45.00 
 
 0.180 
 
 47.00 
 
 0.178 
 
 49.00 
 
 0.177 
 
 51.00 
 
 0.174 
 
 53.00 
 
 0.166 
 
 55.00 
 
 0.164 
 
 57.00 
 
 0.163 
 
 59.00 
 
 0.163 
 
 61.00 
 
 0.158 
 
 63.00 
 
 0.155 
 
 65.00 
 
 0.155 
 
 67.00 
 
 0.149 
 
 69.00 
 
 0.148 
 
 71.00 
 
 0.146 
 
 73.00 
 
 0.134 
 
 75.00 
 
 0.131 
 
 77.00 
 
 0.127 
 
 79.00 
 
 0.117 
 
 81.00 
 
 0.115 
 
 83.00 
 
 0.110 
 
 85.00 
 
 0.107 
 
 87.00 
 
 0.098 
 
 89.00 
 
 0.097 
 
 91.00 
 
 0.095 
 
 93.00 
 
 0.094 
 
 95.00 
 
 0.090 
 
 97.00 
 
 0.077 
 
 99.00 
 
 
 
 
 
 
 
 
 
 
 
 
 
130 A WORLD ATLAS OF ATMOSPHERIC RADIO REFRACTIVITY 
 
 Table E-4. (Continued) 
 
 THICKNESS AND GRADIENT OF SUPERREFRACTIVE LAYERS OVER 300 METERS THICK 
 
 0.31100 -120.25723 
 0.30200 -133.44371 
 0.30700 -127.36157 
 
 
 NOVEMBER 
 
 144 Profiles Skipped 
 
 161 Profiles Read 
 
 Number of Ducts 
 
 Number of Superrefractive Layers 17 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER GRADIENTS, STATION 90001, MONTH 11 
 
 -100.000 2.94 
 -111.972 44.12 
 -137.255 85.29 
 
 -100.000 8.82 
 -113.592 50.00 
 -151.852 91.18 
 
 -100.000 14.71 
 -115.254 55.88 
 -152.252 97.06 
 
 -101.786 20.59 
 -118.750 61.76 
 
 -102.970 26.47 
 -120.690 67.65 
 
 -106.504 
 -122.000 
 
 32.35 
 73.53 
 
 -109.804 
 -131.667 
 
 38.24 
 79.41 
 
 
 CUMULATIVE DISTRIBUTION OF SUPERREFRACTIVE LAYER THICKNESSES, STATION 90001, MONTH 
 
 11 
 
 
 
 0.142 2.94 
 0.101 44.12 
 0.051 85.29 
 
 0.123 8.82 
 0.100 50.00 
 0.051 91.18 
 
 0.120 14.71 0.112 20.59 
 0.081 55.88 0.070 61.76 
 0.050 97.06 
 
 0.112 26.47 
 0.060 67.65 
 
 0.111 
 0.059 
 
 32.35 
 73.53 
 
 0.103 
 0.058 
 
 38.24 
 79.41 
 
 "& U.S. GOVERNMENT PRINTNG OFFICE: 1967—0 222-613