One difficulty in using satellite remote sensing data is the spatial variability of cloud properties on scales smaller than most meteorological satellite fields of view (approx. 4 to 8 km). The variation is examined of satellite derived cloud cover as a function of the satellite sensor spatial resolution for seven cloud cover retrieval methods: (1) Reflectance threshold; (2) Temperature threshold; (3) ISCCP; (4) HBTM (Hybrid Bispectral Threshold Method); (5) NCLE; (6) Spatial coherence; and (7) Functional Box Counting. The first two methods are simple single spectral thresholds which specify a satellite pixel as cloud filled if the measured reflectance is greater than the threshold, or if the measured equivalent blackbody temperature is less than the threshold. The next three methods are bispectral, using one visible wavelength window channel and one thermal infrared wavelength window. The final two algorithms rely on the spatial variability within the cloud field to determine cloud cover. Spatial coherence assumes only that the cloud field occurs in a single layer and that the clouds are optically thick in the infrared window. LANDSAT Thematic Mapper (TM) data is used to test the spatial resolution dependence of the cloud algorithms. The ISCCP bispectral threshold applied to the full resolution data is used as the reference or truth cloud cover, after which the retrieval methods are applied to the spatial resolutions. Studies of the fraction of pixels in the scene at cloud edge, and of the profile of reflectance and temperature near cloud edges indicate an uncertainty in the reference cloud fraction of 1 to 5 percent

Parker, Lindsay

Wielicki, Bruce A.

English

NASA Technical Reports Server

N90-28268
COMPARISON OF SATELLITE BASED CLOUD RETRIEVAL METHODS
FOR CIRRUS AND STRATOCUMULUS
Lindsay Parker
Aerospace Technologies Division, Planning Research Corporation
Hampton, VA. 23666
Bruce A. Wielicki
Atmospheric Sciences Division, NASA Langley Research Center
Hampton, VA. 23665-5225
1. INTRODUCTION
One difficulty in using satellite remote sensing data is the spatial variability of cloud
properties on scales smaller than most meteorological satellite fields of view (approximately 4 to
8 km). The present study examines the variation of satellite derived cloud cover as a function
of the satellite sensor spatial resolution for seven cloud cover retrieval methods:
1. Reflectance Threshold:
2. Temperature Threshold:
3. ISCCP:
4. HBTM:
5. NCLE:
6. Spatial Coherence:
7. Functional Box Counting:
Threshold = clear-sky reflectance Rclr + 3%
Threshold = clear-sky temperature Tclr - 3K
Bispectral threshold (Rclr + 3% , Tclr - 3K)
Hybrid Bispectral Threshold Method (Minnis et al., 1987)
Bispectral method (Stowe et al., 1988)
Coakley and Bretherton (1982)
Lovejoy et al. (1987)
The first two methods are simple mono-spectral thresholds which specify a satellite pixel
as cloud filled if the measured reflectance is greater than the threshold, or if the measured
equivalent blackbody temperature is less than the threshold.
The next three methods are bispectral, using one visible wavelength window channel and
one thermal infrared wavelength window channel. For ISCCP, the pixel is classified as cloud
filled if the measurement exceeds either one of the single spectral channel thresholds. The
HBTM has a more complicated strategy explained in Minnis and Harrison (1984) and Minnis et
al (1987). One of the critical features of the HBTM is a series of checks of retrieved cloud
albedo against climatological values. The NCLE yields a weighted cloud cover from two
independent estimates of total cloud cover (one from an infrared channel at l l.5um and one
from a UV channel at 0.38urn). The philosophy of the NCLE is to use the TOMS reflectance
channel for boundary layer clouds (i.e. low thermal contrast) and to use the THIR l l.5 um
channel for middle and high level clouds (i.e. strong thermal contrast) as detailed in Stowe et
a1(1988).
The final two algorithms rely on the spatial variability within the cloud field to determine
cloud cover. Spatial coherence assumes only that the cloud field occurs in a single layer and
that the clouds are optically thick in the infrared window. Functional box counting uses the
variation in reflectance threshold cloud cover over spatial scales observed by the satellite (here
assumed to be l to 8 km for meteorological satellites) to predict the cloud cover for scales
smaller than those observed (less than l km). This method relies on spatial scale invariance of
the radiance fields to account for the resolution dependence of cloud cover. The study of
Lovejoy et al (1986) used radar and GOES satellite data to indicate that this scale invariance
held to scales in the atmosphere as small as l km.
Landsat Thematic Mapper (TM) data is used to test the spatial resolution dependence of
the cloud algorithms. The TM radiometer has a near visible channel at 0.83urn and a thermal
infrared window channel at l l.5um. The spatial resolution of the visible channel is 28.5 meters
and the infrared channel is l l4 meters. The full resolution data for these two spectral bands is
then averaged in steps of two to provide spatial resolutions of 28.5m, 57m, l l4m, 228m, 456m,
263
https://ntrs.nasa.gov/search.jsp?R=19900018952 2020-03-19T21:11:07+00:00Z
912m, 1824m, 3648m, and 7296m. These spatial resolutions are referred to in the following
discussions as nominally 1/32, 1/16, 1/8, 1/4, 1/2, 1, 2, 4, and 8 km resolution data.
The ISCCP bispectral threshold applied to the full resolution data is used as the reference
or "truth" cloud cover, after which the retrieval methods are applied to each of the spatial
resolutions. For most of the scenes, sufficient clear regions are present in the full resolution
Landsat data to use the peak of the bispectral histogram to define Rclr and Tclr. In the
remaining scenes, the cloud field images were examined to select radiances in clear regions
between cloud cells (at least 0.5 km from the nearest cloud cell). The same Rclr and Tclr are
used at all spatial resolutions, for all retrieval methods, with the exception of the spatial
coherence method which calculates its own clear and cloudy radiances. Note that reflectance is
defined by R ffi 100 _ L / (S(t) coso) where L is spectral radiance derived using the calibration
_i
coefficients of Markham and Barker (1986), S(t) is the spectral solar constant for the date of the
observation and e is the solar zenith angle. Brightness temperature calibration for the thermal
channel is also takSen from Markham and Barker (1986). The Landsat viewing angles are within
5 degrees of nadir.
Studies of the fraction of pixels in the scene at cloud edge, and of the profile of
reflectance and temperature near cloud edges indicate an uncertainty in the reference cloud
fraction of 1 to 5% (Minnis and Wielicki, 1988).
2. RESULTS
The 24 cloud regions are 58.4 km by 58.4 km and are grouped and analyzed by cloud type
(cirrus, cumulus, etc). Table l gives the location, date, reference cloud fraction, clear-sky
brightness temperature (Tclr), clear-sky reflectance (Rclr), average cloudy pixel temperature
(Tcld), average cloudy pixel reflectance (Rcld), and a description of the cloud types present.
The descriptions are based on examination of the full resolution visible and infrared cloud
images, and the bispectral histograms of the cloud fields. The cloud top temperatures given in
the description are taken from the optically thick portions of the cloud fields as identified in
the bispectral histograms (i.e. nadir reflectance greater than 40%). Where two distinct cloud
levels occurred, both cloud tops are given. Time of observation for the Landsat sun-
synchronous orbit is approximately 9:45 a.m. local time.
TABLE 1. CLOUD FIELD LOCATIONS, TIMES, AND PROPERTIES
Date Cloud Rclr Rcld Tclr Tcld
Scene Lat/L0n Da/M0/Yr Cover _ _ _ KLK.)___.
A 19.7S/ 75.3W 7/13/87 0.671 2.5 19.5 289.7 287.5
B 20.7S/75.1W 7/13/87 0.915 2.5 28.2 289.7 286.5
C 33.7N/129.9W 7/10/87 0.521 3.3 21.4 288.8 285.6
D 26.4N/79.4W 1/14/83 0.708 4.0 44.6 298.0 280.5
E 25.7N/ 78.3W 1/14/83 0.390 3.5 52.4 296.3 277.4
F 31.SN/122.2W 7/07/87 0.662 3.8 33.3 289.7 284.8
G 31.8N/120.7W 6/30/87 0.889 2.7 38.7 288.8 282.9
H 28.3N/ 90.0W 1/06/83 0.718 3.2 11.8 305.4 279.2
I 44.6N/86.9W 10/28/86 0.950 4.3 13.8 280.5 266.5
J 43.0N/ 87.5W 10/28/86 0.646 3.6 9.1 283.5 273.9
K 40.3N/71.8W 4/19/85 0.568 4.1 6.2 276.9 269.1
L 40.3N/ 70.3W 5/30/85 0.839 5.3 8.6 278.5 270.5
M 28.9N/ 87.6W 3/15/84 0.625 3.5 14.9 287.8 273.1
DescriPtion (Cld top temp)
Stratocumulus (285K)
Stratocumulus (284K)
Stratocumulus (283K)
Stratocumulus (272K)
Stratocumulus (270K)
Stratocumulus (283K)
Stratocumulus (282K)
Cirrus (=220K)
Cirrus (=243K,=220K)
Cirrus ("238K)
Cirrus (=264K,=254K)
Cirrus (=262K)
Cirrus (-_254K)
a. Stratocumulus Cloud Fields
Results for stratocumulus are given in Fig. 1 (Scenes A-G, Table 1). The Reflectance,
ISCCP and HBTM methods are within 0.05 of cloud "truth" for spatial resolutions less than 1/2
kilometer. These methods show a strong dependence on spatial resolution for pixel sizes beyond
1/2 kilometer. The Reflectance and ISCCP methods overestimate cloud fraction by 0.16 and the
264
HBTM by 0.09 for 8 kilometer data. The overestimation of cloud fraction is caused by partially
filled pixels being considered as cloud filled pixels.
The Temperature threshold method underestimates cloud cover by about 0.20 at all spatial
resolutions. Comparing the ISCCP bispectral result to the Reflectance and Temperature
threshold results, we conclude that the solar reflectance channel dominates the bispectral cloud
retrieval for stratocumulus. While this result is qualitatively expected, the magnitude of the
difference in cloud cover (0.35) between the mono-spectral threshold methods is surprising.
Examining Table l, three of the stratocumulus cloud fields have Tclr - Tcld of 2.2 to 3.2K.
These same three cases also have the lowest cloud reflectances, ranging from 19.5 to 28.2%.
The low reflectances indicate that substantial portions of the cloud field have l l.Sum emittances
less than 1.0. In this case, substantial portions of the cloud field are missed by the Tclr - 3K
threshold. These optically thin portions of the cloud fields are also the cause of the
underestimate of cloud cover by 0.12 for the Spatial Coherence results in Fig. I. The Spatial
Coherence method derives an effective cloud cover which is cloud fraction times cloud
emittance.
For low clouds the NCLE algorithm gives strongest weighting to the cloud cover derived
using the reflectance of the TOMS 0.38urn channel. Therefore, we would expect good results
for the stratocumulus clouds. Fig. l, however, indicates that the NCLE underestimates the
cloud fraction by about 0.20. The NCLE algorithm determines its TOMS-based cloud cover as
a linear function of reflectance between the clear reflectance and an assumed overcast cloud
albedo of 50%. In the present analysis, Landsat nadir reflectance is substituted for albedo using
the Earth Radiation Budget Experiment (ERBE) anisotropic models for overcast cloud (Suttles et
al, 1988) to convert albedo to an equivalent nadir reflectance. This gives an overcast nadir
reflectance of 51%, 48%, and 45% for solar zenith angles of 32 ° , 41 °, and 49 ° respectively. The
average stratocumulus nadir reflectance in Table l, however, is only 34.0%. As a result, the
NCLE algorithm underestimates the cloud fraction for these cloud cases. The NCLE albedo for
overcast low cloud was derived by averaging the 0.38urn reflectance of TOMS fields of view (45
km at nadir) judged to be cloud filled (L. Stowe, personal communication). An examination of
the Landsat spatially degraded data showed that the albedo of cloud filled pixels is a systematic
function of pixel size. The average reflectance of 1/32 km pixeis is 34.0%, 1/8 km is 37.0%,
1/2 km is 42.3%, and 2 km is 47.0%. For 8 km pixels, only cloud fields D-G have overcast
pixels, with an average reflectance of 58.1%. We conclude that the larger the spatial extent of
the contiguous cloud cover, the larger the cloud optical depth. This brings into question the
NCLE assumption of cloud cover linear in cloud reflectance. A caveat on this result, however,
is the use of the Landsat 0.83urn Landsat channel to mimic the 0.3Sum TOMS channel.
Finally, Fig. l shows that the Functional Box counting method underestimates cloud
fraction for spatial resolution less than l kilometer. This method assumes the slope of the
change in cloud cover from 1 to 8 km can be extended to scales less than l km. Fig. l
demonstrates this method is incorrect. Cahalan (1988) and Welch et al (1989) found a break in
the scale invariance between 0.5 and 1.0 km, consistent with the present results.
Fig. 2 gives a scatter plot of the estimated versus reference cloud fraction for each
retrieval method. This figure demonstrates that the mean errors examined in Fig. l vary greatly
from cloud field to cloud field.
Fig. 3 gives the Temperature, Reflectance, and ISCCP Bispectrai thresholds which would
result in unbiased cloud cover estimates for the seven stratocumulus cloud fields. As spatial
resolution degrades, the reflectance and ISCCP thresholds must be further removed from the
clear-sky background to avoid the biasing effect of partially cloudy pixels. ISCCP and the
Reflectance threshold would require a threshold of 9.5% for stratocumulus clouds using 8km
spatial resolution data. The Temperature threshold would require a threshold of Tclr - l.SK
colder than the clear-sky temperature. Unfortunately, use of temperature thresholds much less
than Tclr - 3K would cause false detection of cloud given typical variations in ocean surface
temperature and atmospheric water vapor.
265
b. Cirrus Cloud Fields
Results for cirrus are given in Fig. 4 (Scenes H-M Table 1). The results for cirrus are
very different from the stratocumulus results. The cirrus results show little dependence on the
sensor spatial resolution, indicating that the cirrus are not dominated by the small scale cellular
features prevalent in the stratocumulus fields. The effect of spatial resolution on derived cloud
cover is less than 0.02 for spatial scales less than 2 kin, reaching 0.07 for the ISCCP algorithm
using 8 km resolution data.
The agreement between the Temperature threshold and ISCCP bi-spectral results indicates
the dominance of the 11.5/_m channel for cloud detection of cirrus. Consistent with this view,
the Reflectance threshold underestimates cirrus cloud cover by 0.18 to 0.25, depending on the
spatial resolution. The cirrus clouds are optically thin with an average Rcld of 10.6%. As a
result, the Reflectance threshold misses a substantial portion of the cloud field with reflectances
less than 3% above the clear reflectance. The HBTM results are intermediate between the
Reflectance and Temperature threshold results, underestimating cirrus cloud cover by 0.10 for 8
km resolution data.
The cloud cover underestimate by the Spatial Coherence method is larger than that found
for stratocumulus because the cirrus emittance is lower. The Box Counting method
underestimates cirrus cloud cover for two reasons. First, the reflectance threshold causes an
underestimate as discussed above. Second, the scale invariance is again a poor approximation
for spatial scales less than 1 km. For these cirrus clouds, the scale invariance appears to be a
poor approximation even for scales of 1 to 8 km, in contrast to the result for stratocumulus
cloud fields.
Finally, the NCLE result underestimates cirrus cloud cover by 0.38 to 0.42. This result
appears to be caused by two factors. First, the NCLE uses a Tcir - 6K threshold at ll.5um,
thereby missing some of the cirrus detected by the ISCCP method using a Tclr - 3K threshold.
This was especially important for cloud fields K and L for which the average Tcld was only 7
to 8 K colder than Tclr. A second difficulty is that the TOMS reflectance channel cloud cover
estimate is still being given significant weight by the NCLE algorithm. This is because much of
the cirrus cloud field is warmer than the approximately Tclr - 9K temperature used to separate
low level from middle and high level clouds. These nwarm" portions of the cloud field are
treated as if they were low cloud, thereby using the TOMS channel cloud estimate. Since the
cirrus reflectance is much less than 50%, the TOMS channel greatly underestimates the cirrus
cloud cover.
Figure 5 gives the estimated versus reference cloud cover results for each individual cloud
field. As for stratocumulus, the errors are not a simple constant bias. Figure 6 gives the
threshold levels which if applied to the 6 cirrus fields would have resulted in an unbiased cloud
cover. The thresholds show less dependence on spatial resolution than was found for the
stratocumulus cloud cases.
3. SUMMARY
1. For the threshold methods, stratocumulus cloud cover is strongly dependent on satellite
sensor spatial resolution. Cirrus cloud cover is weakly dependent on satellite sensor
spatial resolution.
2. Differences between current cloud retrieval algorithms are large, especially between the
ISCCP and NCLE algorithms.
3. Varying treatment of cloud optical thickness (i.e. shortwave reflectance or thermal
emittance) appears to account for many of the differences in the cloud retrieval
algorithms.
4. Functional Box Counting incorrectly estimates cloud fraction below 1 km due to breaks in
the scale invariant power law between 0.5 and 1 km for stratocumulus and 2-4 km for
cirrus.
5. For the threshold cloud retrieval methods, the solar reflectance channel dominates cloud
cover retrieval for stratocumulus, while the thermal channel dominates for cirrus.
266
REFERENCES
Cahalan, R. F.,
Love joy and D. Schertzer.
Coakley, J. A. and F. P. Bretherton, 1982: JGR, 87, 4917-4932.
Lovejoy, S. et al, 1987: _, 235, 1036-1038.
Markham, B. L. and J. L. Barker, 1986, Landsat Technical Notes, Aug. 1986.
Minnis, P., E. F. Harrison, and G. G. Gibson, 1987:, J. GeoDhvs, Res., 92, 4051-4073.
1988: in Scaling, Fractals. and Nonlinear Variability in Geoohvsics, Ed. S.
Fig. 1
Minnis P. and B. A. Wielicki, 1988: JGR, 93, 9385-9403.
Platt, C. M R. et al, 1980: MWR, 108, 195-204.
Stowe, L. L. et ai., 1988: ,I. Clim.. 1, 445-470.
Welch, R. M. et al., 1988: JAM, 2--7, 341-362.
Stratocumulus Cloud Fields
Average of 7 cloud fields
0.20 0.10
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Fig. 6
267



Comparison of satellite based cloud retrieval methods for cirrus and stratocumulus

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