SCRIPUC SAN DIEGOLƏRARGRAPHY LIBRARY
OPTICAL SYSTEMS GROUP
Jan 1990
TECHNICAL NOTE NO. 217
UNIVERSITY OF CALIFORNIA, SAN DIEGO
3 1822 03387 3449
AUTOMATED CLOUD COVER
& VISIBILITY SYSTEMS
FOR REAL TIME APPLICATIONS
J. E. Shields
T. L, Koehler
M. E. Karr
R. W. Johnson
UNIVERSITY
OF
CALIFORNIA
SAN DIEGO
The material contained in this note is to be considered
proprietary in nature and is not authorized for distribution
without the prior consent of the Marine Physical Laboratory
and the Geophysics Laboratory
TTY
Contract Monitor, Dr. J. W. Snow
Atmospheric Sciences Division
ERSI
AN
ORN
UCHS
*1866
Prepared for
The Geophysics Laboratory, Air Force Systems Command
United States Air Force, Hanscom AFB, Massachusetts 01731
under contract No. F19628-88-K-0005
SCRIPPS
INSTITUTION
OF
OCEANOGRAPHY
MARINE PHYSICAL LAB San Diego, CA 92152-6400
TECHNICAL NOTE NO. 217
AUTOMATED CLOUD COVER & VISIBILITY SYSTEMS
FOR REAL TIME APPLICATION
This Technical Note contains the extended summary of the January 1990 presentation made at the
Cloud Impacts on DOD Operations and Systems 1989/90 Conference at the Naval Post Graduate School,
Monterey, California.
www.
SUMMARY
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Responding to a well recognized need by many in
both the modeling and operational communities for an
improved capability in the collection and assessment
of whole sky cloud characteristics, as well as sector
visibility variabilities and statistics, a new generation
of video based imaging systems has been developed
and fielded by the Marine Physical Laboratory. One of
these systems, the Whole Sky Imager, has been de-
ployed at several widely separated portions of the
United States, and has gathered several million images
appropriate for determining cloud cover at very high
spatial and temporal resolution. Cloud coverestimates
derived from a 7-month sample of these cloud images
shows very good agreement with observed sky cover
amounts. The capabilities of the Whole Sky Imager
and the other imaging systems is discussed, followed
by an overview of the current status and quality of the
WSI data base.
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TABLE OF CONTENTS
Summary ........
A.
:
List of Illustrations...
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1.0
2.0
i
Introduction .......
Automated Systems for Cloud Assessment and Visibility Determination
2.1 Whole Sky Imager..
2.2 Real Time Cloud System ....
2.3 Portable WSI...
2.4 Night-time WSI .....................
2.5 Horizon Scanning Imager ..........
2.6 Composite Cloud/Visibility System ...
.
3.0
.
WSI Cloud Data Archive
3.1 Background...........
3.2 Current Extent of the Data Base ......
3.3 Cloud Determination ........
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4.0
WSI Evaluation and Results .
4.1 Comparison with Observer .......
4.2 Specific Temporal Dynamics Case Study
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Conclusion ....
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Acknowledgements
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References ..................
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LIST OF ILLUSTRATIONS
Page
Fig. # Figure Title
1 Whole Sky Imager Camera Assembly..
2 Sample WSI Image, acquired at 650 nm.
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................
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Sample Cloud No cloud Decision Image..
Sample One-Minute Cloud/No cloud Decision Image ...
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Sample image from Real Time Cloud System......
6
Portable Whole Sky Imager .........
.....
8
..........
Home
Ver..
WSI Data Base, number of acquired data days .......
WSI Basic Image Processing Flow Chart .............
Distribution of Total Cloud Cover determinations, WSI and Weather Observer.......
Total Cloud Cover determination difference for WSI minus observer
Total Cloud Cover Time series, WSI and Observer .............
Maximum Cloud Free Arc Time series, WSI ................
WSI Cloud Cover Image at 1420, from above time series..
WSI Cloud Cover Image at 1450, from above time series......
32
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11
12
13
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MALARI

1.
INTRODUCTION
A family of imaging systems for use in automated
cloud assessment and sector visibility determination has
been developed by the Marine Physical Laboratory of
Scripps Institution of Oceanography (Johnson, 1989).
This paper will give a brief overview of these systems,
including the Whole Sky Imager (WSI) for long term
cloud studies, the Real Time Cloud system for on-line
operational support, and the Horizon Scanning Imager
(HSI) for sector visibility determinations. Each of these
systems is an automated unit operating under micro-
computer control, gathering digital imagery suitable for
automated processing and analysis.
The WSI systems have been operating at several
locations during the last year, acquiring images once a
minute, 12 hours a day. These data are archived and
subsequently converted to cloud/no cloud decision
images. The resulting cloud database can be utilized for
a variety of applications including model evaluation and
development (Hering, 1989). We will review the acqui-
sition and processing of the WSI cloud database, and
discuss some of the emerging statistical relations from
the WSI cloud determinations.
Fig. 1. Whole Sky Imager Camera Assembly
.
Fig. 2. Sample WSI Image, acquired at 650 nm
image, the center is at the zenith overhead, and the edges
are near the horizon. The black square is the sun
occultor, which shades the lens in order to minimize
stray light. A tower may also be seen in the field of view
near the occultor. In the processing, a determination is
made at each point in the image, yielding a cloud deci-
sion image at full spatial resolution. The cloud decision
image is illustrated in Fig. 3. In this illustration the areas
identified as sky are blue, the occultoris black indicating
a "no data" region, and the pixels identified as thin or
opaque cloud are yellow and white respectively.
2.0 AUTOMATED SYSTEMS FOR CLOUD
ASSESSMENT AND VISIBILITY DETERMINA-
TION
2.1 Whole Sky Imager
The Whole Sky Imager (WSI) is a ground-based
electronic imaging system, which monitors the upper
hemisphere. It is a passive, i.e. non-emissive system,
which acquires calibrated multi-spectral images of the
sky dome. The system, shown in Fig. 1, views the sky
through a series of spectral and neutral density filters,
using a fisheye lens to acquire most of the sky dome. A
fixed gain CID (charge injection device) solid state
camera is utilized, and a full set of radiometric and
geometric calibrations are acquired prior to fielding the
system. Data are acquired in 512 x 480 format, which
yields 1/3 degree spatial resolution. This corresponds to
a 17 meter footprint, for a cloud layer at 3 km height.
In the field, cloud images are acquired under control
by an IBM AT-class microcomputer. This is a stand-
alone unit, requiring essentially no user intervention;
control of all peripheral functions is fully automatic.
Four digital images are acquired every minute, and
archived on 8 mm tape, for post-processing. Approxi-
mately 1.2 gigabytes are acquired and archived per week
at each site.
A sample radiance image is shown in Fig. 2. In this

field spatial and temporal dynamics. The WSI acquires
calibrated multi-spectral sky radiances every minute.
From these, automatic cloud determinations are pro-
duced with 1/3 degree spatial resolution. The system is
currently deployed at 7 sites throughout the country, and
as of 1 Jan 90, had acquired an average of 14 months of
data per site. The data will be further discussed in
Section 3.
2.2 Real Time Cloud System
An outgrowth of the WSI is the Real Time Cloud
system, which provides automatic cloud assessments in
real time in the field, for on-line operational support.
This system acquires sky radiances, much like the WSI.
The user may input a track of interest, such as the track
of a satellite or drone. The cloud determinations are then
made from the measured radiances and presented to the
user in color-coded image format, i.e. blue for sky, white
for opaque cloud, and yellow for thin cloud as shown in
Fig. 5. In the user's display, the track is color coded by
Fig. 3. Sample Cloud/No cloud Decision Image. This
shows the full resolution results taken at 10 minute
intervals.
WHOLE SKY IHAO
Full resolution images such as shown in Fig. 3 are
saved every ten minutes. At one minute intervals, the
subset shown in Fig. 4 is saved. (Fig 4 is the one minute
cloud decision image.) This image consists of a subset
of rows and columns, each saved at full resolution (i.e.
we save 33 rows, and 33 columns, for 33 x 512+33 x 480
resolution). Thus for cloud free arc length statistics, we
have full resolution, but for cloud areal statistics such as
cloud cover, we have reduced resolution in the one
minute data. Our early studies indicated an uncertainty
of approximately a 1% in the cloud cover computed from
the one minute data as compared with the ten minute full
resolution data.
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Fig. 5. Sample image from Real Time Cloud System
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Note user-input track.
altitude, and superimposed on the image. Percent cloud
cover, both total and opaque, are presented to the user.
(Currently cloud coveris assessed only for the sky dome;
techniques to compute cloud cover specifically along the
track are in development.) If desired, the user may view
previously stored images, which are video looped to
display temporal changes in the cloud cover. The current
system is connected by optical fiber to a WSI unit, and
has its own micro-computer for independent image
acquisition and display. The system is currently de-
ployed in New Mexico, for support of the HELSTF laser
site, however it has application to a wide variety of
operational and test scenarios.
Fig. 4. Sampie One-Minute Cloud/No cloud Decision
Image. This shows the subset saved at 1 minute
intervals.
In summary, the Whole Sky Imager is a system for
cloud assessment, specifically for the archival of cloud

2.3 Portable WSI
The portable WSI unit is shown in Fig. 6. This unit,
designed for short term deployments and/or special
applications, is similar to the WSI, but has a more
transportable support and environmental control hous-
ing. The portable system is currently installed at the
University of Wisconsin, for a cooperative study with
Eloranta and Grund, using their HSRL lidar system.
The intent here is to relate the WSI cloud determina-
tions to the cloud optical depth determined by the lidar
system.
targets and the horizon, the visibility is computed, using
contrast transmittance theory.
This system essentially simulates the human determi-
nation of visibility, but it has several advantages. The
human must theoretically have black targets (i.e. -1
inherent contrast), of given angular size, seen directly
against the horizon. With the HSI, since measured
radiances are compared directly, the target need not be of
specific size and does not need to be directly adjacent to
the horizon. And since inherent contrast is a user
specified input in the computation, the requirement for a
black target may be relaxed somewhat.
Numerous computations of uncertainty in the com-
puted visibility as a function of parameters such as the
target inherent contrast show that the system is most
accurate when the target range is close to the limit of
visibility. For this reason, the system is enabled to make
an active selection of which targets to use in determining
visibility, depending on the conditions that obtain at the
time of measurement. This active target selection is one
of the advantages this system has in comparison with
staring photometers. Finally, it should be noted that
unlike a point scatter meter, this system measures visibil-
ity for the integrated path of sight, and returns the
visibility for all sectors.
2.6 Composite Cloud/Visibility System
Finally, in addition to the above systems, we have the
composite system, which has the capabilities of both the
HSI, for sector visibility determinations, and the Real
Time Cloud system, for cloud field determinations. This
system, currently on site at Geophysics Lab, is under
development for use as an automated observing station.
Our intent is to develop night-time capability for both
visibility determination and cloud assessment with this
system.
3.0 WSI CLOUD DATA ARCHIVE
Fig. 6. Portable Whole Sky Imager
2.4 Night-time WSI
We have recently designed a night-time WSI, and are
beginning the fabrication and test stage. This unit
utilizes an image intensifier, in conjunction with a CID
camera. Flux level may be controlled through a combi-
nation of voltage control on the intensifier, and variable
on-chip camera integration time. Analysis of night-time
sky radiances acquired by our group during the 1960's
(Duntley, 1970) indicates that the daytime cloud deci-
sion algorithms should be reasonably applicable at night,
down to quarter moon illumination levels.
2.5 Horizon Scanning Imager
The Horizon Scanning Imager (HSI) is a system
designed for for automated determination of sector visi-
bility. Like the WSI, this system utilizes a CID camera,
but this time with a narrow field of view and a photopi-
cally corrected response. A sequence of images around
the horizon is acquired, at predetermined azimuthal
angles. When the instrument is first installed, and at any
time later, black targets of interest within each scene are
identified. From the measured relative radiance of the
The Whole Sky Imager has been operating routinely
at several sites in the continental US for over a year. This
section discusses the background of this data archive,
and then reviews the data processing and cloud algo-
rithms.
3.1 Background
The WSI data base was acquired primarily in support
of the SDI ground-based laser program. A variety of
models predict joint multi-site probabilities of cloud free
lines of sight (CFLOS) for earth to space, and cloud free
arcs (CFARC). These joint probabilities depend on a

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5
6
Fig. 7. WSI Data Base, number of acquired data days.
Sites 1-3 and in New Mexico, 4 in Calif., 5 in
Montana, 6 in Florida, and 7 in Missouri.
variety of intermediate level relationships, such as CFLOS
and CFARC as a function of sky cover and zenith angle,
site-to-site correlation as a function of separation dis-
tance, and temporal relations such as recurrence and
persistence probabilities.
The WSI data base is in many ways an ideal set for
model evaluation. The model input, i.e. sky cover, is
measured directly. The various intermediate level re-
sults mentioned above may be computed directly from
the digitized data base, and the final multi-site joint
probabilities may be computed directly from the data.
As a result, WSI can provide the data base for testing
existing models, development of new models, and docu-
mentation of a mini-climatology of CFLOS and CFARC
at specific sites of interest.
There appears to be considerable interest in the user
community in using these data in a variety of other
applications. For example, there is interest in creating a
climatology of optically thin cloud cover, perhaps as a
function of optical depth. Additionally, the fact that
absolute radiometric calibrations are acquired for the
instruments means that a data base of calibrated radiance
could be generated from the data, for background clutter
and other applications.
3.2 Current Extent of the Data Base
We are currently archiving WSI data at seven sites:
three in New Mexico, and one each in California, Mon-
tana, Missouri, and Florida. Four images are archived
every minute, 12 hours a day, for approximately 87,000
images per month per site (5 gigabytes per month per
site). As of 1 Jan 90, we had acquired an average of 14
months of data per site. The number of days of acquired
data per site is shown in Fig. 7. Although this is a large
data base, it is not as unwieldy as might be expected, due
to the 2 gigabyte capacity of the 8 mm tapes. The raw
data to date fits on approximately 400 cassettes, and the
cloud data will fit on approximately 100 cassettes (25
with compaction). Since these data are acquired in
digital format, automated quality control and data reduc-
tion processes are very fast and reproducible in compari-
son with photographic techniques.
Initially, we had some trouble with hardware driven
system down-times. Installation of better air filters in the
computers corrected the dust-caused problems with the
tape drives, and installation of chillers directly on the
camera housings corrected a problem with heat sensitiv.
ity in the cameras in the warmer locations. Since these
upgrades were accomplished, most stations have been
quite reliable. As a result, we have had at least 4 sites
archiving concurrent data essentially every day since
Feb 89, with 6 concurrent sites 2/3 of the time, and 7 sites
yielding concurrent data 1/3 of the time.
3.3 Cloud Determination
The cloud determination sequence is illustrated in
Fig. 8. We start with four measured radiances; a blue, a
red, and a blue and a red trimmed with neutral density to
acquire those regions which are offscale bright in the
first two filters. A variety of additional neutral densities
and aperture settings are used to bring the radiances to
the proper onscale level. A number of calibrations are
then applied to these four acquired radiance images. The
most significant are the linearity calibration, which
corrects for any non-linearity of the basic sensor, and the
absolute calibration, which corrects for differences in
the pass bands of the spectral filters, non-neutrality of the
neutral density filters, and so on. (The resulting cali-
brated radiance images are not saved at this time, due to
the extra processing time required.)
Ratios of red to blue radiance are then computed,
including any correction for small differences in image
size. The best blue/red pair to use is then selected on a
pixel by pixel basis, to generate the composite ratio
image. These ratios are then saved to tape in image
format, for further processing.
The ratio tapes are next processed to yield the cloud
tapes. At this point in time, the cloud algorithm is a very
simple thresholding scheme; any pixel with a red/blue
ratio above a certain value is identified as cloud. Sepa-
rate thresholds distinguish thin cloud from opaque cloud.
In general this scheme works quite well. The algorithm
has no trouble with clouds of varying brightness (e.g.
dark grey to white). "Also, since the determination is

BASIC MAGERY
CORRECTED IMAGERY
COMPOSITE RATO
DELIVERABLE DATABASE
CALIBRATION
FUNCTIONS
AADIANCE
CONVERSIONS
CLOUDVNO-CLOUD
DECISION
ALGORITHMS
CALIB.
BLUE
IMAGE
BLUE
MAGE
512x512 x 8
RADIOMETRIC
LINEARITY
BLUERED
RATIOS
RADIOMETRIC
SENSITIVITY
UP.SUN
PED
IMAGE
512 X 512 x 8
CALIB
RED
MAGE
DERIVED
PAODUCTS
DOWN SUN
OPTICAL
DISTORTIONS
IMAGE RATIO
COMPUTATIONS
NEAR HORIZON
SPATIAL
DISTRIBUTIONS
COMPOSITE
BLUEIRED RATIO
IMAGE
OPTIMUM
CLOUDVNO-CLOUD
IMAGE
TWILIGHT
FIELD OF VIEW
DEFINITION
TEMPORAL
DISTRIBUTIONS
BLUE + N.D.
MAGE
512 X 512x8
CALIB
(BLUE)
MAGE
DAWN
STATISTICAL
PARAMETERS
SENSOR CHIP
UNIFORMITY
AS REO'D
PIXEL SELECTIONS
FOR
OPTIMUMIZED
COMPOSITE
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RATIOS
REGISTRATION
ADJUSTMENTS
REDEND
IMAGE
512x512x8
CALIB.
FREDY
MAGE
FLUX CONTROL
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Fig. 8. WSI Basic Image Processing Flow Chart
cloud algorithm identifies some clear cases as 1/10
cover, but in all other cloud categories the match in
frequency of observance is excellent.
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made on a pixel by pixel basis, small clouds are readily
identified. Comparison of the radiance images with the
processed cloud images shows that the cloud algorithm
generally agrees well with the visual assessments.
We are also investigating directionally-dependent
corrections to the cloud determination algorithms, based
on the Hering FASCAT radiance model (Hering, 1985).
Since the clear day ratios tend to be slightly higher at the
horizon and near the sun, a correction which depends on
scattering angle and relative air mass should enhance the
accuracy of the cloud determination. It should be em-
phasized that an enhanced algorithm would only make
small changes in the final cloud images, since the direc-
tional variance in the ratio normally is quite moderate;
indeed, one would expect only selective improvement,
since the fixed threshold results compare very well with
standard observers, as discussed below.
4.0 WSI EVALUATION AND RESULTS
4.1 Comparison with Observer
Much of the wsi data from the Columbia site has
been processed using the preliminary (fixed threshold)
algorithm, in order to give a preliminary assessment of
the accuracy of these techniques. Fig. 9 shows the cloud
cover distribution from 7 months of WSI data, compared
with the values of total sky cover reported on the Na-
tional Weather Service Form 10's. The WSI values are
from the one minute image at the reported time of the
weather observation. This plot is for the six hours
surrounding local apparent noon. The comparison be-
tween WSI and observer is in general quite good. The
Fig. 9. Distribution of Total Cloud Cover determinations,
WSI and Weather Observer.
Another indication of data quality is a direct case-by-
case comparison between WSI and observer. For Fig.
10, the difference between WSI and observer has been
computed for each case. That is, a WSI value of 7/10 and
observer value of 5/10 would be a difference of 2
categories. The distribution of category differences is
shown in Fig. 10. The majority of the cases show a
category difference of 0. The average difference is less
than half a category, i.e. much less than 1/10 cloud cover.
4.2 Specific Temporal Dynamics Case Study
Although the observer to WSI comparison is gener-
ally good, the WSI data can show much more variance

COLUMBIA,
M
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1450, both WSI and observer show approximately 20%
cloud cover, but during the intervening hour the cloud
cover increased to nearly 80%. The cloud images at the
middle and end of this hour are shown in Figs. 13 and 14.
The fastest rate of change during this hour occurred at
between 1400 and 1413, when the cloud cover changed
from 20 to 74%.
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Fig. 10. Total Cloud Cover determination difference for
WSI minus observer.
than the observer data, simply due to the limited tempo-
ral frequency of the observer values. One particularly
dynamic day, 14 April at Columbia, Mo., is illustrated in
Figs. 11 and 12. In Fig. 11, which shows the cloud cover
determinations for both the WSI and the observer, the
observer values are consistent with the WSI, but they
certainly do not show the true variability, due to their
limited temporal frequency. For example, at 1350 and
Fig. 13. WSI Cloud Cover Image at 1420, from above
time series.
COLUMBIA ME 14 APRIL
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Fig. 11. Total Cloud Cover Time series, WSI and Observer
COLUMBIA, M) 14 APR
1998
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Fig. 14. WSI Cloud Cover Image at 1450, from above
time series.
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One can also see, in Figs. 13 and 14, a horizontal line
part way down the image in the Northem sky. This line
represents an arc traveling from horizon to horizon,
rising to a 45 degree zenith angle. The maximum cloud
free arc length (CFARC) along this arc has been plotted
in Fig. 12. Comparing Figs. 11 and 12, one can see that
in general there is a tendency for short CFARCs to occur
with high cloud cover. As expected however, the maxi-
mum CFARC is not always well related to the cloud
cover at a given point in time. For example, at 1450, the
TORTAS
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12
13
14
15
16
20
18
THE (GM)
Fig. 12. Maximum Cloud Free Arc Time series, WSI
K-0005. Our thanks to Mr. Donald Grantham and I
William Snow of GL for their guidance. The HSI effort
is sponsored by Geophysics Lab contract #F19628-88-
C-0154 under the guidance of Dr. H. Albert Brown. The
authors would also like to recognize the outstanding
efforts of our colleagues at Marine Physical Laboratory:
Wayne Hering, Monette Karr, Gene Zawadski, Jack
Varah, Harry Sprink, Melissa Ciandro, Tim Romedy,
John Malo, and Peter Pak, for technical support, and Phil
Rapp and Carole Robb for publications support.
1
cloud free arc length was quite short, in spite of the low
cloud cover. Examination of the cloud image for this
time (Fig. 14) shows that this occurred because the cloud
band happened to lie over the selected arc.
5.0 CONCLUSION
With the development of a family of digital imaging
systems, it has been possible to support a number of
applications requiring monitoring of the cloud and visi-
bility environment. The Whole Sky Imager has been
monitoring specific sites for post analysis and evalu-
ation of cloud fields, with high temporal and spatial
resolution. The Real Time Cloud system is currently
supporting the need for immediate feed-back of the
cloud field over the sky dome and in specific directions,
for operational support. The portable system may be
used for short term test and evaluation programs. The
Horizon Scanning Imager can provide visibility deter-
minations, for both prevailing and sector visibilities.
And finally, the Composite system should provide both
real time cloud and visibility results, for a variety of
applications including automated weather stations.
: The WSI has been used specifically to gather a data
base of over a million images for assessment of cloud
fields. The data consists of cloud decision images,
containing an assessment of the cloud condition in each
direction with 1/3 degree spatial resolution, and one
minute temporal resolution. These data are currently
undergoing processing and quality evaluation. The
preliminary results appear quite good, in comparison
with standard meteorological observations. The cloud
images may be used in several applications, including
determination of cloud free line of sight and cloud free
arc probabilities, and multi-site joint probabilities.
6.0 ACKNOWLEDGEMENTS
7.0 REFERENCES
Duntley, S. Q., R. W. Johnson, J. I. Gordon, and A. R.
Boileau, (1970). Airbome Measurements of Optical
Atmospheric Properties at Night, University of Cali-
fornia, San Diego, Scripps Institution of Oceanogra-
phy, Visibility Laboratory, SIO Ref. 70-7, AFCRL-
70-0137, NTIS NO. AD 870 734.
Hering, W. S. and R. W. Johnson, (1985). The FASCAT
Model Performance Under Fractional Cloud Condi-
tions and Related Studies, University of California,
: San Diego, Scripps Institution of Oceanography, Visi-
bility Laboratory, SIO Ref. 85-7, AFGL-TR-84-0168,
NTIS NO. AOA 085 451.
Hering, W. S., (1989). Evaluation of Stochastic Models
for Estimating the Persistence Probability of Cloud-
Free Lines-of-Sight, University of California, San
Diego, Scripps Institution of Oceanography, Marine
Physical Laboratory, GL-TR-89-0275.
Johnson, R. W., W. S. Hering, and J. E. Shields, (1989).
Automated Visibility and Cloud Cover Measurements
with a Solid-State Imaging System, University of
California, San Diego, Scripps Institution of Oceanog-
raphy, Marine Physical Laboratory, SIO 89-7, GL-
TR-89-0061.
This work was sponsored by Geophysics Lab, Air
Force Systems Command, under contract #F19628-88-
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