3.4 Cryosphere-related Algorithms

GLI Algorithm Description

3.4.-1

3.4.1 CTSK1
A. Algorithm Outline(1) Algorithm Code: CTSK1(2) Product Code: CLFLG_p(3) PI Name: Dr. Knut Stamnes(4) Overview of Algorithm
This algorithm theoretical basis document (ATBD) describes the algorithm development forthe cloudy/clear discriminator (ADEOS-II/GLI/CTSK1a) and the snow/sea-ice discriminator(ADEOS-II/GLI/CTSK1b) to be applied in the polar regions and in snow-covered mid-latitude
areas. We present the theoretical basis for the algorithm, processing flow charts, and its
application to MODIS Airborne Simulator (MAS) data. The calibrated radiance data at level
1B of GLI measurements in channels 8, 13, 17, 19, 24, 27, 30, 31, 34, 35, and 36 will be used
as input to this algorithm. The algorithm is based on pre-defined thresholds. The snow/sea-ice
discriminator works only during daytime. Fortunately, bare sea-ice conditions are expected tooccur primarily in summer, and the bright polar summer will offer the opportunity to use thesnow/sea-ice discriminator. The output of the cloudy/clear and snow/sea-ice discriminator
algorithm will be an 8-bit word for each field of view. It includes information about whether aview of the surface is obstructed by cloud and the surface type for each pixel. The output of
the algorithm is not a simple yes/no judgment for cloudy/clear discrimination. There are fourlevels of confidence to indicate whether a pixel is judged to be cloudy or clear. In addition, thisalgorithm also allows for on-screen display of a color image of the output file of a scene
remotely-sensed by satellite. This color image will be a plot of the distribution of cloud andvarious types of surfaces. The output file of this algorithm is also an important and necessary
input for cloud and surface property retrieval algorithms as well as other related studies.

GLI Algorithm Description

3.4.1-1

B. Theoretical Description(1) Methodology and Logic Flow
1. Input and Output
1.1 Input
As mentioned above, the calibrated radiance data of GLI at level 1B in channels 8, 13, 17,
19, 24, 27, 30, 31, 34, 35, and 36 will be used as input for the algorithm. If there are any
incomplete and bad data in these channels for a pixel being remotely-sensed, the first bit (bit 0)of the 8-bit output word (see below) will be set to 0 for this pixel, and then the algorithm will
not be executed. Thus a hole will be created in the final output map. Additionally, several
ancillary data sets are required as input to the algorithm:•

Sun position and sensor position, i.e., zenith and azimuthal angles for both the sun and thesatellite sensor•

The latitudes and longitudes for the current scan area•

A land/water map with 1 km resolution•

GLI surface-identification maps for the previous 8 or 4 days that will be used for the
temporal uniformity test

1.2 Output
The output of the cloudy/clear and snow/sea-ice discriminator algorithm will be an 8-bit
word for each field of view. The output bit structure is shown in Table 2.1. The output fileconsists of the results of cloud mask (i.e., cloudy or clear) and the results of surface
identification for cloud-free pixels. In some cases, however, it is still ambiguous to define
whether a pixel is cloudy or not. For example, a pixel may be partially cloudy, or a pixel may
appear cloudy at one spectral channel but appear cloud-free at another channel. A brightness
temperature difference test, based on the 10.8 and 3.7 µm channels, is sensitive to water clouds,
but not to thin cirrus. In contrast, the 1.38 µm channel is sensitive to the presence of high levelcirrus due to the strong water vapor absorption. Thus, multi-spectral approaches are expected
to be particularly useful for cloud detection in the polar regions. The output of the algorithm is
not a simple yes/no judgment for cloudy/clear discrimination. There are 4 levels of confidence
to indicate whether a pixel is judged to be cloudy or not (bit 2-1 of the output word) based on
the analysis of the results from different spectral tests.
A brief description of the output bits is given as follows (see Table 2.1):

GLI Algorithm Description

3.4.1-2

Table 2.1 The output of the cloudy/clear and snow/sea-ice discriminator algorithm. The output
is an 8-bit word, i.e.,
7–6

Bit No.
bit 0

bit 2-1

bit 5-3

Bit output
0
1
00
01
10
11
000
001
010
011
100
101
110
111

5–3

2–1

0

Meaning
not executed
executedclear skyhigh-confidence cloudy
middle-confidence cloudy
low-confidence cloudy
snow over ice
sea icecloud shadow on snow
land with vegetation
open oceansnow over landsnow over land
contaminated withvegetation
bare land with lessvegetation

bit 7-6•

Remarkconfidence = 100%50% confidence <100%0% < confidence < 50%

Spares

Bit 0: If the sensor-measured radiance of a pixel is deemed to be bad, bit 0 will be set to 0,
and the algorithm will not be executed for this pixel. If the algorithm is executed normally
bit 0 will be set to 1.

•

Bit 2-1: Bit 2-1 represents the final results of the cloudy/clear discrimination processing.
For a given pixel bit 2-1 indicates clear or cloudy with different confidence levels (seeTable 2.1).
Based on the threshold value for each individual cloud test, a confidence flag is assigned.
There is a linear interpolation between the high-confidence-cloud threshold (100%) andthe clear-sky threshold (0%). If the confidence is 100%, bit 2-1 is set to 01. If theconfidence is between 50% and 100%, bit 2-1 is set to 10. If the confidence is between 0%
and 50%, bit 2-1 is set to 11. For the case with confidence of 0%, bit 2-1 is set to 00, andthe pixel is assumed to be clear (cloud-free).
Because various test techniques (or methods) may be sensitive to different kinds of cloud,the final determination will be made by considering the maximum confidence of all tests
applied. This implies that the algorithm will be cloud conservative. For a pixel with low-

GLI Algorithm Description

3.4.1-3

confidence cloud, additional spatial and temporal consistency tests will be implemented
for improving the cloudy/clear discrimination capability.
•

Bit 5-3: For a clear pixel (bit 2-1 is set to 00), the algorithm will assign the surface type.
Generally, the thresholds for reflectance and brightness temperature can be used to
distinguish pixels with snow/sea-ice from pixels with the land/open-ocean. The land/water
map will be used to flag land and ocean. Further, the snow/sea-ice discriminator will be
used to distinguish the bare sea-ice from snow-covered sea-ice and/or open water in ocean
areas. There are five surface types that will be tagged by this algorithm: snow (includingsnow-covered sea-ice), bare sea-ice (snow-free), land (including vegetation), open ocean,and cloud shadow over snow. The cloud shadow test will be implemented only over snow
during daytime.
In addition, this algorithm can also offer the ability to plot a color image of the processing

results. The color image will be a plot of the distribution of cloud and various types of surface.
It provides a visualization useful for testing our understanding of the algorithm. The output fileof this algorithm is also an important and necessary input for cloud retrieval and surface
property retrieval algorithms as well as other related studies.
2. Implementation of the Algorithm
The algorithm includes four parts: cloudy/clear discriminator, snow/sea-ice discriminator,
cloud shadow detection, and surface classification (as shown in Figure 2.1). It can be used to
identify clear scenes and to provide surface classification. Sea-ice will be classified into baresea-ice and snow-covered sea-ice. This information will be saved in an output file consisting ofan 8-bit word for each pixel that is required for retrieval of cloud and surface properties.
The cloudy/clear discriminator consists of a daytime mode and a nighttime mode. In eachmode, there are two independent tests for detection of low cloud and thin cirrus cloud. Because
surface temperature inversions often exist under clear-sky conditions during polar winter, atemperature inversion test will be included in both the daytime mode and the night time mode
to improve the capability of cloud detection. The result of each test is expressed as aconfidence level that indicates the likelihood that the pixel is contaminated with cloud. The
final confidence level is determined by the maximum confidence level resulting from applying
several tests. Most pixels have either confidence 1 (high confidence cloud) or 0 (clear pixel).
Pixels with an intermediate confidence value, tend to be found at cloud boundaries.
For clear pixels, surface classification will be applied to tag the surface as land, open ocean,snow-covered land, or snow/ice-covered ocean. Each clear pixel will be classified as being

GLI Algorithm Description

3.4.1-4

land or ocean according to its location in the land/water map. Simple reflectance and/or
brightness temperature threshold tests are used to distinguish snow/ice-covered surface fromland or open ocean.

Figure 2.1
The flow chart of algorithm for single pixel test.

GLI Algorithm Description

3.4.1-5

Cloud shadow detection is applied for low albedo surfaces. Further work on the cloud
shadow detection over snow/ice will be needed in the future.
The snow/sea-ice discriminator relies on visible and near infrared channels to distinguishbare sea ice from snow-covered sea ice. The NDII technique and the visible reflectance ratio
test used in the algorithm will only be applied during daytime. Bare sea-ice appears mainly
during the summer. Thus, the long daylight hours of the polar summer is an advantage since
the snow/sea-ice discriminator is limited to daytime use.
An outline of the algorithm has been shown in Figure 2.1. The implementation steps ofalgorithm are as follows:
I.

Input GLI calibrated radiance data at level-1B and other ancillary data, including
A. solar and sensor position, and the location of the scan area
B. land/water map
C. GLI surface identification maps for the previous 8 or 4 days

II.

Apply cloudy/clear discriminator to set confidence flag as cloudy or clear.
A. determine whether the pixel is in a day or night regime
B. apply single cloudy/clear test to assign confidence level for each test, depending on its
value relative to the threshold
C. determine final confidence flag, which is the maximum confidence of all applied tests

III. Apply surface classification algorithm to classify surface into high albedo surface (snowand ice) and low albedo surface (bare land, land with vegetation and open ocean).
IV. Apply cloud shadow test to tag shaded pixels.
V. Apply snow/sea-ice discriminator to tag bare sea-ice and snow-covered sea-ice.
VI. Output the results of the algorithm, which is an 8-bit word for each pixel.
The algorithm has been applied to MAS (MODIS Airborne Simulator, deployed on-board the
ER-2 aircraft) data (ARMCAS, April-95 Alaska, WINCE). MAS has a spatial resolution of
approximately 50 m, which is much higher than the ground resolution (1 km) of the GLI
sensor. Results from these applications should aid in algorithm development. A comparison of
NDSI technique and brightness temperature difference test of 10.8 and 3.7 µm has beenpresented to verify the feasibility of using brightness temperature difference test instead of
NDSI technique. The algorithm has been applied to high latitude areas (ARMCAS and April-

GLI Algorithm Description

3.4.1-6

95 Alaska data) as well as to mid-latitude areas (WINCE data). It is feasible to extend thisalgorithm to snow-covered mid-latitude areas. Due to the absence of winter data and the 6.7µm channel in MAS, the temperature inversion test could not be tested by use of MAS data.

(2) Physical and Mathematical Aspects of Algorithm
1. Objective
There are two main objectives of this investigation: (i) to determine whether a given FOV(field of view) is obstructed by clouds, and (ii) for cloud-free pixels to distinguish the bare seaice from snow-covered sea-ice. The algorithm yields an 8-bit word output for each pixel todescribe the sky condition (i.e., whether the pixel is obstructed by clouds) and the surface typefor a given pixel under cloud-free conditions. The surface will be classified into five possible
types: snow, sea-ice, cloud shadow, land (tundra), and open ocean. When sea-ice is covered bysnow (even only a few centimeters), the surface radiative characteristics will be similar tosnow (Jin et al., 1994). Thus, sea-ice covered by snow will be classified as a snow surface,
while only bare sea-ice is classified as sea-ice.
2. BackgroundOur development of the cloudy/clear discriminator and the snow/sea-ice discriminator for
ADEOS-II/GLI has benefited from previous studies in this field (Hall et al., 1996; Ackerman
et al., 1997). Since some of the GLI channels are similar to those of MODIS (The Moderate
Resolution Imaging Spectrometer), we will adopt the results of previous studies in our work.
Compared to AVHRR (Advanced Very High Resolution Radiometer) that has only fivechannels, the multiple GLI channels will allow us to extract more information about the cloudand snow/sea-ice surface in the polar regions which has been considered as a difficult remotesensing problem. However, many methods and techniques as well as related results concerninga cloudy/clear discriminator developed for the AVHRR can be employed and modified for use
with the GLI.
The ISCCP (International Satellite Cloud Climatology Project) cloud masking algorithm, as
described by Rossow (1989) and Rossow and Garder (1993), relies on comparing the observed
radiances with their corresponding clear-sky values in two channels (0.6 µm and 11 µm).
Rossow and Garder (1993) summarized the five steps employed for ISCCP cloud detection as
follows: (i) space contrast test on a single infrared image; (ii) time contrast test on threeconsecutive visible images; (iii) accumulation of space/time statistics for infrared and visible
images; (iv) construction of clear-sky composites for the infrared and visible every five days ateach diurnal phase and location; and (v) radiance threshold for the infrared and visible for eachpixel.

GLI Algorithm Description

3.4.1-7

The AVHRR Processing scheme Over cLoud Land and Ocean (APOLLO) (Kriebel andSaunders, 1988; Kriebel et al., 1989; Gesell, 1989) uses AVHRR channels 1 through 5 basedon five threshold tests, i.e., a brightness temperature threshold test, the reflectance ratio ofchannels 2 to 1, the temperature difference between channels 4 and 5, and the spatial
uniformity over ocean (Kriebel et al., 1989). A pixel is defined as cloud contaminated if it fails
in any single test, so in that sense the APOLLO scheme is cloud conservative.
The NOAA CLAVR algorithm (Phase I) relies on multispectral decision, channel
differences, and spatial differences, and then employs a series of sequential decision tree tests
with all five channels of the AVHRR (Stowe et al., 1994). A 2 x 2 global area coverage (GAC)
pixel array is used for identification in such a way, that if all four pixels satisfy just one of thecloud tests, then the array is labeled as 100% cloudy or “restored-clear,” otherwise, it islabeled as mixed or 50% cloudy. Subsequent phases of CLAVR will use dynamic thresholds
and pixel by pixel classification. A cloud-contaminated pixel is radiatively typed as belonging
to low stratus, thin cirrus or a deep convective cloud system.
Several algorithms have also been developed for cloud clearing for the TIROS-N
Operational Vertical Sounder (TOVS). For example, the fifth version of the International
TOVS Processing Package (ITPP-5) uses collected AVHRR and HIRS/2 (High-Resolution
Infrared Sounder) to cloud-clear the HIRS/2 footprints (3 x 3 retrieval box). Several methods,
such as a brightness temperature (BT) or an albedo threshold test, a BT difference test or a BT,
a skin temperature difference test, and a difference test employing BT derived from AVHRR
and HIRS/2, are used to determine cloudy sky condition. The nature of the cloud cover isdetermined in further steps. Operational GOES products by NESDIS use an array of n x ncontinuous pixels and use a method similar to that of ITPP-5 (Ackerman et al., 1997).
As the high albedo of snow cover presents a good contrast with most other natural surfaces,snow cover was observed in the first image obtained from the TIROS-1 weather satellite
following its launch in April 1960 (Singer and Popham, 1963), and has been mapped in the
Northern Hemisphere on a weekly basis since 1966 by NOAA. A variety of sensors, including
AVHRR and passive-microwave sensors, have been widely used for the detection of snow
cover. Various techniques, ranging from visual interpretation, multispectral image
classification, decision trees, change detection, and ratios (Kyle et al., 1978; Bunting andd’Entremont, 1982) have been developed to map snow cover with remotely sensed data.
Spectral-mixture modeling is also used for subpixel classification of snow in a scene (Nolin etal., 1993; Rosenthal, 1993).
The snow-mapping algorithm (SNOMAP) developed for MODIS relies on use of an NDSI
(Normalized Difference Snow Index) test and a decision rule to identify snow in each 500 m
MODIS pixel (Hall et al., 1995). The use of the NDSI for snow/cloud discrimination is based

GLI Algorithm Description

3.4.1-8

on the fact that the snow reflects visible radiation more strongly than it reflects radiation in the
middle-infrared part of the spectrum, while cloud reflectance remains high in these two
spectral regions. Thus, a low value of the NDSI is indicative of cloud, and a relatively higher
value of snow cover. However, a visible threshold test must be conducted to separate snow
cover from land/open ocean. The NDSI test is of limited value over dense forest cover, or in
the presence of thin cirrus clouds. Other advanced classification techniques (Nolin et al., 1993)
permit subpixel classification and improve the identification of fractional snow cover. It is
likely that spectral-mixture modeling or another advanced classification technique that does
not rely on thresholding could be implemented after launch of MODIS (Hall et al., 1995).
Some of the above cloud masking algorithms have been incorporated into current global
cloud climatologies and have been run in an operational mode over long time periods. We
have also benefited from other cloud detection studies, e.g., the algorithms developed for
MODIS, in our development of a cloudy/clear discriminator for the GLI. However, due to thespecial difficulty associated with cloud detection over high albedo surfaces in the polar regions
and the characteristics of the GLI multispectral information, some improvements to theprevious algorithms have been made and used with the specific GLI channels. Since most of
our data processing studies are based on MAS data and radiative transfer simulations, further
improvements may be possible when data from the GLI become available.
The detection of cloud shadows is a difficult problem in the development of a proper
cloudy/clear discriminator. Theoretically, they can be computed from the given view angle,
solar angle, distribution of cloud edges, and cloud altitude, but it takes considerable computing
time. There are two thresholds used for cloud shadow detection over land, which are based on
the reflectance at channels 13 (0.678 µm), 19 (0.865 µm), and 24 (1.05 µm): R0.865/R0.678 > 0.9;
R1.05 < 0.12 (Ackerman et al., 1997). Cloud shadow detection over snow is currently at animmature stage and will require substantial development beyond the scope of this project.
3. GLI Characteristics
The GLI has 36 channels with channels 1–29 in the visible and near-infrared (NIR), and therest of the channels in the thermal-infrared ranging from 3 to 12 µm. The specifications of the
GLI channels used here are shown in Table 2.2. There are some channels that are similar
among GLI, MODIS and AVHRR (see Table 2.2). Due to this similarity, our work is
benefiting from previous experience and results by others in algorithm development.

GLI Algorithm Description

3.4.1-9

Table 2.2 GLI channels to be used in the algorithm
GLI
channel
8

λ
(µm)
0.545

Δλ
(µm)
0.01

GR
(km)
1

13

0.678

0.01

1

17

0.763

0.008

1

19

0.865

0.01

1

24

1.05

0.02

1

27

1.38

0.04

1

30

3.715

0.33

1

31

6.70

0.50

1

34

8.60

0.50

1

35

10.8

1.0

1

36

12.0

1.0

1

Purposesurface classification,
cloudy/clear andsnow/sea-ice
discriminatorsurface classification,
cloud shadow,
cloudy/clear andsnow/sea-ice
discriminatorcloudy/clear
discriminatorsnow/sea-ice
discriminator andcloud shadow
cloud shadow,
snow/sea-ice
discriminatorcloudy/clear
discriminatorcloudy/clear
discriminatorcloudy/clear
discriminatorcloudy/clear
discriminatorcloudy/clear
discriminatorcloudy/clear
discriminator

MODIS
channel4

AVHRR
channel1

MASchannel
1

1

1

2

15
2

5
2

7
9

26
20

3

15
(1.88 µm)
30

27
29

42

31

4

45

32

5

46

Figure 2.2 shows simulated planetary albedoes for various types of surfaces and water/ice
clouds in the 0.3-2.5 µm spectral range. The location and bandwidth of GLI channels 1-29 are
also shown in Figure 2.2. The 36 GLI channels offer the opportunity to use multispectral
approaches for cloud detection. The analysis of cloud cover in the polar regions from satellite
data is more difficult than at lower latitudes because the visible and thermal contrasts between
the cloud cover and the underlying surface are frequently quite small. However, a notabledifference between cloud and snow can be found at 1.6 µm. Based on this information, the
NDSI technique could be used to distinguish clouds from snow surfaces (Hall et al., 1995).
Unfortunately, the ground resolution of GLI channel 28 (1.64 µm) has been designed as 250 m,

GLI Algorithm Description

3.4.1-10rather than 1 km which is the ground resolution for all channels we used here. So we cannot
use the NDSI technique in this algorithm. Another brightness temperature difference test using
channel 30 (3.7 µm) and channel 35 (10.8 µm) will be adopted for the cloudy/clear

Figure 2.2
The planetary albedoes of various types of surfaces and clouds in the spectral range between
0.3 and 2.5 µm for solar zenith angle θ=60°. The location and bandwidth of GLI channels 129 are also shown at the top part of the figure with black dots or lines.

discriminator instead of the NDSI technique.
The cloudy/clear discrimination can be accomplished using GLI channels 8, 13, 19, 24, 27,
30, 31, 34, 35 and 36. These channels are employed for detecting low cloud, high cloud, andcloud shadow.
The principal differences in planetary albedo between snow and ice occur at NIR bands(0.6-1.2 µm), as shown in Figure 2.2. As a result of the strong absorption of sea ice in the NIR,
the reflectance over ice is much lower than over snow. This difference provides the theoreticalbasis for the snow/sea-ice discriminator. Based on the different albedo between snow and seaice, GLI channels 8, 13, 19 and 24 are used for snow/sea-ice discrimination. We use a newindex called the NDII (Normalized Difference Ice Index) as our main test for snow/sea-ice
discrimination. The NDII test will be used only during day time. Fortunately, sea-ice iscovered by snow most of the year. Bare sea-ice appears primarily in the summer season. The

GLI Algorithm Description

3.4.1-11

bright polar summer offers a possibility to apply the NDII-based snow/sea-ice discriminator
which is effective only during day time.
GLI channels with 1 km ground resolution will be used for this algorithm. GLI will
provide measurements at the same position every four days. The four-day recovery schedule isalso used to test for temporal uniformity for the confirmation of surface type.
MAS data are used for the validation of the algorithm. The MAS data set used in this study
includes ARMCAS (Arctic Radiation Measurement in Column Atmosphere-surface System),
ALASKA_April_95 (i.e., snow, glaciers and sea-ice measurements, March-April 1995, in
north central Alaska), and WINCE (WINter Cloud Experiment, Jan. 23 to Feb. 13, 1997, overthe Great Lakes area) data. These data sets provide the opportunity to apply and test the clouddetection and surface identification algorithm over polar and mid-latitude snow-covered areas.
4. Physical aspects of the algorithm
This algorithm is different from the MODIS cloud-mask algorithm, which is based on agiven surface condition. We have designed an algorithm which is independent of surface
conditions so that is will be applicable to the variable surface conditions encountered in thepolar regions during the spring, summer and autumn. We also employ multispectral ratios andthreshold-based methods to obtain a simple and effective algorithm.
The algorithm consists mainly of the cloudy/clear discriminator (CTSK1a), the snow/seaice discriminator (CTSK1b), and a surface classification. The last component goes beyond thetasks CTSK1a and CTSK1b that we committed to do as part of this project. We offer thissimple surface classification algorithm to help identify high-albedo ocean areas from the clear
pixels. The snow/sea-ice discriminator algorithm will be implemented only for these highalbedo ocean areas, to distinguish the bare sea-ice from snow-covered sea-ice. A flow chart of
the algorithm for a single pixel test is shown in Figure 2.1. The algorithm will start with singlepixel (1 km FOV) tests. For pixels with low-confidence cloud, a spatial coherence test may be
applied on 3 x 3 pixel segments to improve the confidence of the cloud detection.
For nomenclature, we shall denote the satellite-measured radiance as I and the normalizedreflectance as R , and we shall refer to the infrared radiance as brightness temperature(equivalent blackbody temperature determined using the Planck function) denoted as BT .
Subscripts refer to the wavelength in µm at which the measurement is made. We define thenormalized reflectance as:πI

Rλ = Sλ cosλ θ0

GLI Algorithm Description

3.4.1-12

where S is the extraterrestrial solar irradiance. A threshold will be set for each single test
based on the normalized reflectance R when the channel wavelength is less than 3 µm, andbased on brightness temperature when the channel wavelength is larger than 3 µm.
4.1 The Cloudy/Clear Discriminator
The detection of clouds in the polar regions or in snow-covered mid-latitude areas isdifficult because of insufficient albedo contrast between the cloud and the underlying snow or
sea ice surface. Since snow and ice are considerably more reflective in the visible than in the
NIR part of the spectrum and the reflectance of clouds remains high in the NIR, theNormalized Difference Snow Index (NDSI) technique based on a channel at 1.64 µm is used to
distinguish snow from clouds (Hall et al., 1995). Due to the limitation of ground resolution of
GLI channel 28 (1.64 µm), we cannot use the NDSI technique in our cloudy/clear
discriminator. A brightness temperature difference (BT10.8 and BT3.7), which was used only innight mode in our earlier version, will be used also during daytime, instead of the NDSI
technique. A comparison between the NDSI technique and the brightness temperature
difference, based on MAS data, has demonstrated the usefulness of the brightness temperature
difference test. Additionally, we use the 1.38 µm channel for thin cirrus detection during
daytime and we use four thermal channels (3.7, 8.6, 11, and 12 µm) for cloud detection during
night.
Under clear-sky conditions, strong surface temperature inversions often exist in the polar
regions during winter. Clouds generally tend to inhibit the formation of the inversion.
Therefore, the presence of a strong, low-level temperature inversion is a good indication of aclear sky condition. The 6.7 and 10.8 µm channels will be used for temperature-inversion
detection in polar winter for both the daytime and the nighttime modes. A detailed analysis of
the temperature-inversion test will be deferred (see below).
A combination of these tests constitutes the main part of the algorithm, since none of themcan be considered as a perfect technique for detecting all kinds of clouds under different
environmental conditions. For example, some are more reliable for low-cloud detection, whileothers are better for high-cloud detection. The result of each single test is expressed as aconfidence level ranging from 1 (cloudy pixel with high confidence) to 0 (clear pixel). The
final confidence flag will be assigned by considering the maximum confidence of all tests
applied. This is called the cloud conservative approach.
A common result of the cloudy/clear discrimination from this algorithm is either highconfidence cloudy or clear, except for cases in which there are some low-confidence clouds atcloud boundaries, or thin, low, or small clouds exist. In these cases, spatial and/or temporalcontinuity tests need to be conducted. The cloudy/clear discrimination algorithm has a daytime

GLI Algorithm Description

3.4.1-13

mode and a nighttime mode depending on the solar zenith angle. Daytime is defined bylimiting the solar zenith angle to θ0 < 85°. The daytime mode includes brightness
temperature difference (using the 3.7 and the 10.8 µm channels) and the 1.38 µm channel thincirrus tests; and the nighttime mode includes infrared brightness temperature difference tests,
using channels at 3.7 and 10.8 µm. The temperature-inversion test is also included in both thedaytime and the nighttime modes. Figure 2.3 shows a flow chart of the cloudy/clear
discriminator algorithm.

Figure 2.3
The flow chart of cloudy/clear discriminator. F1, F2, F3, and F are the confidence levels forindividual test and final result, respectively.

GLI Algorithm Description

3.4.1-14

4.1.1 Brightness Temperature Difference Test (BT10.8 and BT3.7)
At high latitudes, infrared window tests based on satellite-measured radiances are difficult
due to the cold surface, and especially the small temperature contrast between cloud and snow
surface in winter. However, a nighttime polar (Antarctic) cloud/surface discrimination
algorithm has already been applied by Yamanouchi et al. (1987). This algorithm is based upon
the brightness temperature difference between the AVHRR 3.7 and 11 µm channels andbetween the AVHRR 11 and 12 µm channels.
GLI channel 30 (3.7 µm) measures radiances in the window region near 3.5-4 µm, so we
can use GLI channels 30 and 35 (10.8 µm), another window region, to detect the presence ofclouds. In particular we can detect partial cloudiness or thin cloud cover at night based on the
difference between BT10.8 and BT3.7. The MAS data at 3.7 µm (channel 31) and 11.0 µm(channel 45) will be used for validation and determination of the threshold. Another methodfor determining the threshold is based on modeling results. Small or negative differences
BT10.8 – BT3.7 are observed only for cases when an opaque scene (such as thick cloud or thesurface) fills the field of view of the sensor. Negative difference BT10.8 – BT3.7 occurs at night
over extended clouds due to the lower cloud emissivity at 3.7 µm. Large negative difference
BT10.8 – BT3.7 occurs during daylight hours because of the large reflection of solar radiance at
3.7 µm.
This test will be used in both daytime and nighttime modes. Because of the reflection ofsolar energy at 3.7 µm during daytime, the thresholds used for daytime and nighttime modes
are quite different.
4.1.2 Thin Cirrus Test (1.38 µm) channel
The detection of thin cirrus is a challenging problem, especially in polar regions.
Fortunately, GLI channel 27 (1.38 µm) provides an opportunity to detect the presence of thincirrus in the upper troposphere under daytime viewing conditions by use of reflectancethresholds (Gao et al., 1993). The premise of this test comes from the strong water vapor
absorption in the 1.38 µm region. Therefore, most of the earth's surface is obscured at thischannel. With relatively little of the atmosphere’s moisture located high in the troposphere,
high clouds appear bright. Simple low and high reflectance threshold will be used to separate
thin cirrus from clear scenes as well as scenes with thick cloud. The MAS 1.88 µm channel
will be used in conjunction with theoretical simulations for validation and for determining thethreshold, because strong water vapor absorption exists in both the 1.38 µm channel and the1.88 µm channel.

GLI Algorithm Description

3.4.1-15

4.1.3 Brightness Temperature Difference Test (BT8.6 – BT12)
The 1.38 µm channel thin cirrus test can not be used for cloudy/clear discrimination during
the dark winter time in the polar regions. Instead we have to use infrared channels for thispurpose. Because the sensitivity of the refractive index for ice and water vapor is different in
the window region, their absorption peaks are in opposite halves of the window region around8-12 µm. The difference in brightness temperature between GLI channels 34 (8.6 µm) and 36(12 µm), BT8.6 – BT12, can therefore be applied during night time to discriminate between highlevel clouds and cloud-free conditions.
4.1.4 Temperature Inversion Test (BT6.7 and BT6.7 – BT10.8)
Normally, there is strong temperature inversion under clear-sky conditions in polar winter(Curry et al., 1996). Thus, a temperature-inversion test is another way to achieve cloudy/clear
discrimination. Infrared channels whose weighting functions peak low in the atmosphere willoften have a larger BT than a window channel (e.g., BT6.7 – BT10.8). In clear sky situations, the6.7 µm radiation measured by the satellite instrument is emitted by water vapor in an
atmospheric layer located approximately between 200-500 hPa (Soden and Bretherton 1993;
Wu et al., 1993), while 6.7 µm radiation emitted by the surface or low clouds is absorbed in theatmosphere and is not sensed by the satellite instruments. On the contrary, the measured 10.8
µm radiation under clear sky originates primarily at the surface. Strong inversions in polar
winter under clear-sky conditions lead to large negative differences in BT10.8 – BT6.7 (below 10 K) that can be used to detect cloud-free conditions. The GLI channels 31 (6.7 µm) and 35(10.8 µm) will be used for this test.
4.1.5 Confidence Flags
Almost all tests mentioned above rely on the pre-defined thresholds. Instead of a single
threshold, the user can obtain information about how much confidence can be placed in the
results. The confidence of every single test is assigned a value between 0 and 1, representing
increasing confidence that clouds are present. Figure 2.4 is a graphical representation of how aconfidence level is assigned for a test. As shown in Figure 2.4, an observational value greaterthan γ corresponds to high confidence that a cloud is present and is assigned a confidence
level of 1. An observed value less than α is clear and will be assigned a confidence level of 0.
These high-confidence cloudy and clear thresholds, γ and α respectively, are determinedfrom observations and/or theoretical simulations. Observational values between α and γ are
assigned a confidence level between 0 and 1. Assignment is based on a linear interpolation.
Each of the tests mentioned above returns a confidence level ranging from 1 (highconfidence cloud) to 0 (clear-sky). Based on the cloud conservative approach, we set final

GLI Algorithm Description

3.4.1-16

confidence level as the maximum confidence level of all applied tests (shown in Figure 2.3).
The final confidence level will determine the final flag as cloudy or clear for the output file (bit2-1).

Figure 2.4
A graphical representation of confidence level.

4.2 Theoretical Description of the Snow/Sea-ice Discriminator
Snow exhibits strong reflectance in the visible region and strong absorption in the NIR
region. The NDSI technique has been successfully applied to distinguish snow from clouds as
discussed in previous sections. Sea ice has characteristics similar to snow. It is more difficult
to distinguish sea-ice from snow than detecting clouds over snow. However, there is adifference in albedo between snow and sea-ice in the region of 0.6-1.2 µm (see Figure 2.1).
Due to the strong absorption of sea-ice, the planetary albedo of sea-ice drops rapidly at 0.6 µm,
while snow maintains relatively high albedo until 1.2 µm. Based on this characteristic, we

GLI Algorithm Description

3.4.1-17

employ the NDII technique (Normalized Difference Ice Index) (see section 4.2.1) as the main
test for snow/sea-ice discrimination. A reflectance ratio test (see section 4.2.2) is an auxiliary
test used in the snow/sea-ice discriminator algorithm. The two tests in the snow/sea-ice
discriminator will only be used during daytime.
4.2.1 Normalized Difference Ice Index (NDII) Test
Under clear-sky conditions, the principal differences in planetary albedo between snow andsea-ice occur in the NIR region between 0.6 and 1.2 µm, as shown in Figure 2.2. Snow and sea
ice have similar reflectance for wavelengths shorter than 0.6 µm. As a result of the strong seaice absorption in the NIR, the reflectance over sea-ice is much lower than over snow in thatwavelength region. Thus channel 24 (1.05 µm) can be used to distinguish snow from sea ice.
In addition, we also need to employ another band (0.5-0.7 µm) to distinguish ocean fromsnow/sea-ice. Since ocean has lower albedo than snow/sea-ice in this visible region (see Figure2.2), GLI channel 8 (0.545 µm) can be used for the separation of snow/sea-ice and ocean.
Actually, there are plenty of melt ponds on the sea ice due to the melting during summer.
The visible radiances for such cases will have values between that corresponding to the sea-ice
case and that corresponding to the open-ocean (water) case. However, albedo of water is low at
both channel 8 (0.545 µm) and channel 24 (1.05 µm), while albedo of sea ice is high at channel
9 and low at channel 24, and albedo of snow is high at both channel 8 and channel 24 (seeFigure 2.2). We then define a snow/sea-ice discriminator using NDII that is similar to the caseof NDSI:

R

−R

1.05
NDII = R0.545
+
R
0.545
1.05

where R0.545 and R1.05 are the normalized reflectances of channels 8 and 24, respectively. A
reference radiance at channel 13 (0.678 µm) is required to distinguish snow from dark surfaces.
For open ocean, the reference radiance is always low. When the reference radiance is high andthe NDII is below a pre-defined value threshold, the current pixel is covered by snow. Whenthe reference radiance is high and the NDII is larger than this threshold value, the pixel iscovered by sea ice.
4.2.2 Reflectance Ratio Test
The reflectance ratio test employs the ratio of satellite-measured radiance between channels
19 (0.865 µm) and 13 (0.678 µm) (i.e., R0.865/R0.678). This test is based on the fact that the
spectral reflectances at these two wavelengths are similar over snow or water (ratio is near 1)
and different over sea-ice.

GLI Algorithm Description

3.4.1-18

4.3 Surface Classification
There is a simple surface classification included in the algorithm. It will be applied to all
clear pixels tagged by the cloudy/clear discriminator. Visible and infrared (needed during nighttime) threshold tests can be applied to discriminate high albedo surfaces from low albedo
surfaces. We use GLI channels 8 (0.545 µm) and 35 (11 µm) for visible and infrared thresholdtests, respectively. The land and ocean area will be determined by the land/water map. Thus,
the classification of four types of surface (open ocean, land, snow-covered land, and snow/seaice covered ocean) can be accomplished by using two threshold tests and the land/water map.
It is necessary to tag the snow/sea-ice-covered ocean area prior to applying the snow/sea-ice
discriminator.

C. Practical Considerations(1) Programming Requirements
Table 3.1 provides information about the software generated from this algorithm.
Table 3.1 Program Requirements
Program Memory
Program Size
Required Channels
Necessary/Ancillary Data
Expected Disk Volume
Special Programs or SubroutinesThresholds list

400 KBytes
100 KBytes
8,13, 17, 19,24,27,30,31,34,35,36
land/water map
5 MBytesmask.fthresholds.inc(2) Calibration and Validation
1. Cloudy/clear Discriminator for MAS data
For the cloudy/clear discriminator there are two modes applied during daytime andnighttime, respectively. A brightness temperature difference test using channels at 10.8 and 3.7µm is used for both daytime and nighttime modes to distinguish cloud from snow. In addition,
a 1.38 µm channel test and a test employing the brightness temperature difference between the
8.6 and the 12 µm channels are used to detect the presence of thin cirrus in both daytime andnighttime modes, respectively. Since nighttime MAS data are not available, we have used theinfrared data obtained during daytime data for validation and improvement of the algorithm.
Because the threshold of the daytime mode is quite different from that of the nighttime mode,
MAS data cannot be used to establish the correct nighttime threshold.

GLI Algorithm Description

3.4.1-19

1.1 Comparison between NDSI test and BT11 – BT3.7 test
Figure 3.1 shows the results from one scene of WINCE data using the NDSI test and thebrightness temperature difference test. The scene used is the observation made on February 9,
1997 over Lake Erie. We use the MAS channel at 1.88 µm instead of GLI channel at 1.38 µm
for the thin cirrus test because MAS does not have a channel at 1.38 µm and there is strong
water vapor absorption at both the 1.38 µm and the 1.88 µm channels. High radiance at the1.38 µm channel (1.88 µm channel of MAS) indicates the presence of high level cloud(including thin cirrus cloud). For the left panel of Figure 3.1, we used the NDSI test as well as
the 1.88 µm channel test for the cloudy/clear discrimination. For the right panel of Figure 3.1,
we used the test based on the brightness temperature difference between channels at 11 and 3.7µm as well as the 1.88 µm channel test for the cloudy/clear discrimination. The different colors
used in Figure 3.1 represent the results of cloudy/clear discrimination (see Table 3.2).
Comparing the two panels in Figure 3.1, one finds that the difference between the two images(i.e. the two panels in Figure 3.1) is less than 10%. This indicates that the brightness
temperature difference test may generally work as well as the NDSI test. Therefore, we can use
the brightness temperature difference test as the main test instead of the NDSI test forcloudy/clear discrimination.

Table 3.2 Description of colors in cloud mask image
color
red
blue
yellowgreen
sea greencoralmagentacyanmeaningsnow over ocean
sea icelandhigh confidence cloud
middle confidence cloud
low confidence cloudsnow over land
water (open ocean)

1.2 Comparison between 1.38 µm test and BT8.6 - BT12 test
In cloudy/clear discriminator, we use the 1.38 µm test for the daytime mode and the test
employing the brightness temperature difference between the 8.6 and 12 µm channels fornighttime modes. These two tests offer the potential to improve the detection of thin cirrus.
Here we show a comparison between these two tests for a scene of the MAS ARMCAS data.
This scene obtained on June 11, 1995 over the North Slope of Alaska. Figure 3.2 shows thecloud mask resulting from use of the 1.38 µm test, the BT8.6 – BT12 test, and the BT11 – BT3.7

GLI Algorithm Description

3.4.1-20

test, respectively. The left panel of Fig. 3.2 corresponds to the 1.38 µm test for the daytime
mode, the middle panel corresponds to BT8.6 – BT12 test for the nighttime mode, and the rightpanel corresponds to BT11 – BT3.7 test for both daytime mode and nighttime mode. Cirrus
cloud should have a high value at 1.38 µm and BT8.6 – BT12. At the top of image, there aresome water clouds detected by 1.38 µm test, but not by BT8.6 – BT12 test. It implies that 1.38

Figure 3.1
An example of a cloudy/clear discriminator image derived from MAS WINCE data over theLake Erie on Feb. 9, 1995 (track 3). The left panel is for the NDSI test. The right panel is forthe brightness temperature difference test.
µm test is not only sensitive to cirrus cloud, but also to some thick low cloud. These water
clouds have also been detected by BT11 – BT3.7 test which is a main test for both daytime andnighttime mode (See right panel of Fig. 3.2). Figure 3.3 shows two images of cloud mask
results using the same data as shown in Fig. 3.2 for daytime mode (left panel) and nighttime
mode (right panel). From Fig.3.3, we have found that both daytime mode and nighttime modeyield similar cloud mask images.

GLI Algorithm Description

3.4.1-21

Figure 3.2
An example of a cloudy/clear discriminator image derived from MAS ARMCAS datacollected over the North Slope of Alaska on June 11, 1995 (track 4). The left panel is for the
1.38 µm test, the middle panel is for the BT8.6 – BT12 test, and the right panel is for BT11 – BT3.7
test.

From the results of the two cases in Figs. 3.1 and 3.2, we have found that we can successfully
carry out cloudy/clear discrimination using BT11 – BT3.7 test instead of NDSI test in daytime
mode. We have also found that both the daytime and the night modes of the cloudy/clear
discriminator algorithm seem to work well over snow snow/ ice surfaces in the polar regions aswell as in mid-latitude areas.
2. Snow/sea-ice Discriminator for MAS data
Under clear-sky conditions, snow/sea-ice discrimination over ocean area will be based on
the NDII test and the ratio-threshold test. Validation of this algorithm has been carried out by
using MAS data obtained in the ARMCAS and Alaska-April-95 campaign. MAS channels 2(0.653 µm) and 9 (0.945 µm) are employed in the NDII test to mimic GLI channels 8 (0.545µm) and 24 (1.05 µm). The reflectance ratio between MAS channels 2 (0.653 µm) and 5

GLI Algorithm Description

3.4.1-22

Figure 3.3
An example of a cloud mask iamge derived from MAS ARMCAS data over North Slope of
Alaska on June 11,1995 (track 4). The left panel is for daytime mode, and right panel is fornighttime mode.

(0.781 µm) is also employed to mimic GLI channels 13 (0.678 µm) and 19 (0.865 µm), as thesecond test for discriminating snow from bare sea-ice.
One case is presented here to indicate that the NDII test and the ratio test can be used todiscriminate snow from bare sea-ice. This scene is an observation made on April 8, 1995
(track 14) over the Bering Sea (Fig. 3.4). This figure shows the ice mask combining the NDIIGLI Algorithm Description

3.4.1-23

test and ratio test, The algorithm is sea-ice conservative. The colors in image have been
described in Table 3.2.

Figure 3.4
An example of snow/sea-ice discriminator derived from MAS data over the Bering Sea of
Alaska on April 8, 1995 (track 14). This shows the results of the cloud mask and snow/sea-ice
discriminator.
Prior to generating the sea ice mask, pixels with high confidence cloud occurrence (greenarea) are identified based on the BT11 - BT3.7 test and the 1.88 µm test. Then, a reference
channel (MAS channel 2, corresponding to GLI channel 13) is used to discriminate sea icefrom open water. Finally, the NDII and the ratio tests are applied to mark the bare sea ice

GLI Algorithm Description

3.4.1-24

surface (blue area). The cyan area is the open ocean, caused by ice lead or pond after the snow
over sea ice melts, when the reflectance in MAS channel 2 is low. The red area is snowcovered area when the reflectance in channel 2 is high. In Fig. 3.4, St. Lawrence Island
covered by snow is just at the top of panel. There is open water along the coast. Near the coast,
there are many floating ice sheets. This is a scene under clear-sky condition, except for a small
amount of cumulus clouds over the ocean surface. Snow-covered sea-ice was present at thebottom of the scene.
From the case discussed in this section, it is obvious that the sea-ice can be discriminatedfrom snow and water by the algorithm described above.

(3) Quality Control and Diagnostic Information(4) Exception Handling(5) Constraints, Limitations, Assumptions
According to the requirements of ADEOS-II/GLI/CTSK1a and ADEOS-II/GLI/CTSK1b,
an algorithm has been developed for cloudy/clear discrimination and snow/sea-ice
discrimination. The cloudy/clear discriminator consists of a daytime as well as a nighttime
mode. The BT11 – BT3.7 test has been successfully and effectively used in the daytime mode of
the cloudy/clear discriminator instead of NDSI technique due to the lack of 1.64 µm channel.
The 1.38 µm channel test, which is important for the detection of thin cirrus, has been includedin the daytime mode. In the nighttime mode, the BT8.6 – BT12 brightness temperature difference
test based on the different refractive indexes for ice and water is available to detect cirrus cloud.
During polar winter, a temperature inversion test is included into both the daytime and thenighttime modes to improve the capability of the cloud detection.
The snow/sea-ice discriminator is a daytime only mode. The so-called NDII technique andthe reflectance-ratio approach have been used in the algorithm. GLI channel 24 (1.05 µm) is an
important channel for discriminating snow from bare sea ice. The rapid decline of ice
reflectance in the 0.6-1.2 µm region is the basis for the snow/sea-ice discriminator algorithm.
Applications to the MAS data set reveal that the NDII is an effective method for the separationof sea-ice and snow/open-water.
The basic features of the algorithm may be summarized as follows:(i) The cloud detection algorithm is designed to work independently without considering
surface conditions and to be valid for both polar regions and snow-covered mid-latitude
regions. The threshold for each test applied in this algorithm has the same value for

GLI Algorithm Description

3.4.1-25

different surfaces. Thus, the cloud-contaminated pixels can be detected before thesurface condition is known.
(ii) The algorithm is based on pre-defined thresholds. The results of the cloudy/clear
discriminator are indicated as 4 levels of confidence with regard to whether a pixel isconsidered to be cloudy. This algorithm is constructed to be cloud-conservative,
minimizing false cloud detection but missing clouds that resemble clear conditions.
(iii) The snow/sea-ice discriminator works only during daytime. Fortunately, bare sea-ice
occurs primarily in summer. Bright polar summers will offer the opportunity to use thesnow/sea-ice discriminator for extended periods of time.
(iv) The nighttime mode is necessary during polar winter. The thresholds of the infrared
channels used in the nighttime mode should be determined based on nighttime
observational data. Due to the lack of nighttime data, the nighttime mode will bevalidated in the future.
(v) Because GLI channels are not exactly matched by MAS channels, the thresholds foreach test should be further adjusted to fit the GLI channels.
This work has been completed based on existing approaches. However, there are several
remaining issues that need to be investigated before and after the launch of ADEOS-II. Sometests in this algorithm should be further validated with upcoming satellite observations and/or
higher resolution aircraft data. Our validation relies heavily on the use of observational data,primarily the MAS data. MODIS data will also be considered for validation when such data
become available. Since the spectral distribution of the GLI and the MODIS channels are quite
similar, we expect MODIS data to be very useful for testing existing approaches to thecloudy/clear discriminator and snow/sea-ice discriminator and for developing new ones as
deemed necessary.

(6) Publications and Papers
Li, W., X. Xiong, K. Stamnes, and B. Chen, An algorithm for mapping snow/ice cover in the
Arctic ocean using MODIS Airborne Simulator (MAS) data, 49th Arctic Science
Conference, Alaska, October 25-28, 1998. (in press).

GLI Algorithm Description

3.4.1-26

D. References
Ackerman S., K. Strabala, P. Menzel, R. Frey, C. Moeller, L. Gumley, B. Baum, C. Schaaf,and G. Riggs, 1997: Discriminating clear-sky from cloud with MODIS algorithm
theoretical basis document (MOD35).
Bunting, J.T. and R.P. d’Entremont, 1982: Improved cloud detection utilizing defense
meteorological satellite program near infrared measurements. Air Force Geophysics
Laboratory, Hanscom AFB, MA, AFGL-TR-82-0027, Environmental Research Papers, No.
765, 91pp.
Curry J.A., W.B. Rossow, D. Randall, and J.L. Schramm, 1996: Overview of Arctic cloud andradiation characteristics. J. of Climate, 9, 1731–1764.
Gao, B.-C., A.F.H. Goetz, and W.J. Wiscombe, 1993: Cirrus cloud detection from airborne
imaging spectrometer data using the 1.38 micron water vapor band. Geophys. Res. Lett., 20,
301–304.
Gesell, G., 1989: An algorithm for snow and ice detection using AVHRR data: An extension
to the APOLLO software package. Int. J. Remote Sens., 10, 897–905.
Hall, D.K., G.A. Riggs, and V.V. Salomonson, 1995: Development of methods for mapping
global snow cover using Moderate Resolution Imaging Spectroradiometer data. Remote
Sens. Env., 54, 127–140.
Hall, D.K., G.A. Riggs, and V.V. Salomonson, 1996: Algorithm Theoretical Basis Document(ATBD) for the MODIS snow-, Lake Ice- and Sea Ice-Mapping Algorithms.
Jin, Z., K. Stamnes, W.F. Weeks, and S.C. Tsay, 1994: The effect of sea ice on the solar
energy budget in the atmosphere-sea ice-ocean system: A model study. J. Geophys. Res., 99,
25281–25294.
Kriebel, K.T. and R.W. Saunders, 1988: An improved method for detecting clear sky andcloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123–150.
Kriebel, K.T., R.W. Saunders, and G. Gesell, 1989: Optical properties of clouds derived fromfully clouds pixels. Contr. Phys. Atmos., 62, 165–171.
Kyle, H.L., R.J. Curran, W.L. Barnes, and D. Escoe, 1978: A cloud physics radiometer. Third
Conference on Atmospheric Radiation, Davis, CA, 107–109.
Nolin, A., J. Dozier, and L.A.K. Mertes, 1993: Mapping alpine snow using a spectral mixture
modeling technique. Ann. Glaciol., 17, 121–124.
Rosenthal, C.W., 1993: Mapping montane snow cover at subpixel resolution from the Landsat
thematic mapper. M.A. Thesis, Department of Geophysics, University of California at Santa
Barbara, 70pp.
Rossow, W.B., 1989: Measuring cloud properties from space: A review. J. Climate, 2, 201–
213.
Rossow, W.B. and L.C. Garder, 1993: Cloud detection using satellite measurements ofinfrared and visible radiances for ISCCP. J. Climate, 6, 2341–2369.

GLI Algorithm Description

3.4.1-27

Singer, F.S. and R.W. Popham, 1963: Non-meteorological observations from weather satellites,
Astronautics and Aerospace Engineering, 1 (3), 89–92.
Soden, B.J. and F.P. Bretherton, 1993: Upper tropospheric relative humidity from the GOES
6.7 µm channel: Method and climatology for July 1987. J. Geophys. Res., 98, 16,669–
16,688.
Stowe, L.L., S.K. Vemury, and A.V. Rao, 1994: AVHRR clear sky radiation data sets at
NOAA/NESDIS. Adv. Space Res., 14, 113–116.
Wu, X., J.J. Bates, and S. Singh Khalsa, 1993: A climatology of the water vapor band
brightness temperature for NOAA operational satellite. J. Climate, 7, 1282–1300.
Yamanouchi, T., K. Suzuki, and S. Kawaguci, 1987 : Detection of clouds in Antarctiva from
infrared multispectral data of AVHRR. J. Meteor. Soc. Japan, 65, 949–962.

GLI Algorithm Description

3.4.1-28