Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm
Description, Validation, and Comparisons
Michael J. Pavolonis
Cooperative Institute for Meteorological Satellite Studies
University of Wisconsin-Madison
Madison, WI
Andrew K. Heidinger
Office of Research and Applications
NOAA/NESDIS
Madison, WI
Taneil Uttal
Environmental Technology Laboratory
NOAA
Boulder, CO
Submitted to
Journal of Applied Meteorology
May ??, 2004
Corresponding author address: Michael Pavolonis, 1225 West Dayton St., Madison, WI 53706
[email protected]
1
Abstract
Three multi-spectral algorithms for determining cloud type of previously identified
cloudy pixels during the day with satellite imager data are presented. Two algorithms were
created to be used with 0.65 µm, 1.6 µm/3.75 µm, 10.8 µm, and 12.0 µm data from the Advanced
Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric
Administration (NOAA) operational polar-orbiting satellites.
The AVHRR algorithms are
identical except for the near-infrared data that is used. One algorithm uses AVHRR channel 3a
(1.6 µm) reflectances and the other uses AVHRR channel 3b (3.75 µm) pseudo-reflectances.
Both of these algorithms are necessary since the AVHRR's on NOAA-15 through NOAA-17
have the capability to transmit either channel 3a or 3b data during the day, while all other
AVHRR's on NOAA-7 through NOAA-14 only transmitted channel 3b data. The two AVHRR
cloud typing schemes are used operationally in NOAA's extended Clouds from AVHRR
(CLAVR-x) processing system. The third algorithm utilizes additional spectral bands in the 1.38
µm and 8.5 µm regions of the spectrum that are available on the Moderate resolution Imaging
Spectroradiometer (MODIS) and will be available on the Visible/Infrared Imager/Radiometer
Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on board the National Polarorbiting Operational Environmental Satellite System (NPOESS), which is scheduled to be
launched in 2008.
Five cloud type categories are employed: warm water, supercooled water/mixed
phase, opaque ice, non-opaque ice (cirrus), and cloud overlap (multiple cloud layers). Each
algorithm was qualitatively evaluated through scene analysis and then validated against
inferences of cloud type derived from ground-based observations of clouds at the three primary
2
Atmospheric Radiation Program (ARM) sites to help assess the potential continuity of a
combined AVHRR channel 3a/AVHRR channel 3b/VIIRS cloud type climatology.
It was
determined that the two AVHRR algorithms produce nearly identical results except for certain
thin clouds and cloud edges. The AVHRR3a algorithm tends to mis-classify the thin edges of
some low and mid-level clouds as cirrus and opaque ice more often than the AVHRR3b
algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant
improvement in the identification of cirrus clouds, cloud overlap, and overall phase identification
of thin clouds, compared to the capabilities of the AVHRR algorithms presented in this paper.
3
1. Introduction
The Earth's energy budget is greatly influenced by clouds in that radiative heating
rates, latent heating rates, and moisture transport are all significantly impacted by clouds. The
microphysical properties, spatial coverage and location of clouds dictate the effect of clouds on
the Earth-Atmosphere system. Cloud type, which incorporates the following characteristics:
cloud thermodynamic phase, cloud-top height, and cloud optical thickness provides a means for
assessing the radiative effect of a particular cloud. For instance Pavolonis and Key (2003)
demonstrated that the radiative effect that clouds have on the surface varied significantly with
cloud thermodynamic phase, cloud-top height, and cloud optical thickness. Chen et al. (2000)
showed that cloud type will influence the earth radiation budget just as much as cloud amount.
In addition, it is also important to have the ability to detect when multiple cloud layers are
present since atmospheric heating/cooling rates are affected by the vertical distribution of clouds
(Liang and Wang, 1997). Surface observations have shown that multi-layered cloud systems
occur in most parts of the world (Warren et al., 1985), especially in the tropics and in association
with mid-latitude cyclones (Hahn et al., 1982, 1984; Tian and Curry, 1989).
It is useful for cloud type, including the identification of multiple cloud layers, to be
determined using imaging satellite instruments which can provide data at a much higher spatial
and temporal resolution than surface observations. This is especially true over oceans. Also, it
is important to determine cloud type from satellite observations prior to retrieving additional
cloud properties. Several previous studies have focused on classifying cloudy satellite imager
pixels. For instance, Baum et al. (1997) applied a fuzzy logic approach to classifying global
Advanced Very High Resolution (AVHRR) data. The 5 channel AVHRR has been on the
National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites
4
since 1981. Further work with AVHRR data includes a neural network cloud classification
system used by Tag et al. (2000), and Hutchinson (1999) and Key and Intrieri (2000)
demonstrated the utility of near-infrared reflectances in determining cloud top phase. Strabala et
al. (1994) and Baum et al. (2000a) used multispectral infrared data to separate water clouds from
ice clouds using data from the MODIS (Moderate Resolution Imaging Spectroradiometer)
Airborne Simulator (MAS). Li et al. (2003) utilized a maximum likelihood classification method
with MODIS measurements. A bispectral grouping approach using the 1.63 µm and 11 µm MAS
bands for detecting cloud overlap was demonstrated by Baum and Spinhirne (2000b).
In this paper, three separate globally applicable algorithms for classifying cloudy
satellite pixels into the cloud type categories listed below are presented.
1).warm liquid water clouds
2).supercooled water/mixed phase clouds
3).opaque ice clouds/deep convection
4).non-opaque ice clouds (e.g. cirrus)
5).cloud overlap (e.g. multiple cloud layers)
The warm liquid water cloud category includes clouds composed of water droplets that have a
temperature greater than 273.16 K.
The second class accounts for clouds that are either
composed entirely of supercooled water droplets or both ice and supercooled water. Opaque ice
clouds are taken to be clouds that are either entirely composed of ice particles and opaque clouds
that have glaciated tops consistent with deep convection. The fourth cloud type consists of ice
clouds that are at least somewhat transmissive. Most cirrus clouds would fall into this category.
Finally, the cloud overlap category is used to identify situations when more than one cloud layer
is present. The cloud overlap detection methods are unique to these algorithms and have already
5
been detailed and validated in Pavolonis and Heidinger (2004), hereafter, PH04.
Each algorithm utilizes threshold values that are applied to a single satellite pixel at
a time. Two of the algorithms presented in this study were designed to be used with AVHRR
data and the other algorithm was created to be used with Visible/Infrared Imager/Radiometer
Suite (VIIRS) data. VIIRS is the 16 channel instrument that will replace the AVHRR on board
the National Polar-orbiting Operational Environmental Satellite System (NPOESS), which is
scheduled to be launched in 2008. The VIIRS will have a 8.5 µm band, which in combination
with the 11 µm band, has been shown to be very useful for retrieving cloud top phase and a 1.38
µm band which can be used to identify high clouds. Neither of these bands are available on the
5-channel AVHRR which has the following bands: 0.63 µm, 0.86 µm, 1.6 µm/3.75 µm, 10.8 µm,
and 12.0 µm. Both of the AVHRR algorithms are necessary since the AVHRR's flown after
NOAA-14 can transmit either channel 3a or 3b data during the day, while all AVHRR's flown on
NOAA-14 and earlier only had the capability to measure and transmit channel 3b data. Future
AVHRR's will continue to be able to switch between channels 3a and 3b. These two AVHRR
cloud typing schemes are used operationally in NOAA's extended Clouds from AVHRR
(CLAVR-x) processing system. The development of all three algorithms is critical in that the
AVHRR algorithms can be used to process over 20 years of data for long-term climate studies
and the VIIRS algorithm represents the capabilities for at least the next 20 years. Comparison of
the results from these three algorithms will provide preliminary guidance on the potential
continuity of a combined AVHRR3b/AVHRR3a/VIIRS cloud type climatology.
The following questions will be addressed in this paper.
Will both AVHRR
algorithms produce similar results? How much of an improvement can be made by utilizing the
6
1.38 µm and 8.5 µm bands that are available on MODIS and will be available on VIIRS, but not
the AVHRR? All three algorithms will be described in detail and theoretical data from radiative
transfer models that were used to help derive various thresholds utilized in each algorithm will
be presented. Each algorithm will be applied to three vastly different scenes and the results will
be examined and inter-compared. Because MODIS offers channels with similar spectral and
spatial resolution as the AVHRR/VIIRS, MODIS data, with a spatial resolution of 1 km, is used
in this study exclusively and all spectral wavelengths given from this point forward will
represent MODIS band central wavelengths.
In addition, each algorithm will be validated
against inferences of cloud type based on measurements made by ground-based instruments at
the Atmospheric Radiation Program (ARM) sites in the Tropical Western Pacific, Southern
Great Plains, and North Slope of Alaska.
2. Models
Two radiative transfer models were used to develop the theoretical basis for each
algorithm presented in this study. The first model, Streamer (Key and Schweiger, 1998), was
used to develop one of the cloud overlap detection techniques and to simulate all infrared data
used in either algorithm. Liquid water cloud droplets were taken to be spherical and a Mie
scattering regime was assumed. In the infrared, ice particles were taken to be spherical and Mie
calculations were performed. Ice crystals may take on a number of different shapes, so the
assumption that ice crystals in the infrared behave as spheres may be flawed (Takano and Liou,
1989; Schmidt et al., 1995), but scattering in the longwave is secondary to absorption.
Data for all other algorithm tests was simulated using a model that employs a
standard adding/doubling approach to solve the radiative transfer equation with delta-M scaling
of the phase function (Wiscombe, 1977). A correlated-k approach is used to model gaseous
7
absorption by H2O, CO2, O3, CO, CH4, O2, N2O, and other trace gases (Bennartz and Fischer,
2001; Kratz, 1995). Both water and ice particles were taken to be spheres at all wavelengths and
Mie scattering was assumed. The spectral bands available are the same as those associated with
the MODIS instrument. Streamer was originally used to simulate all spectral channels, but since
the Streamer bandwidths were determined to be too broad to simulate data in the 1.38 µm and
1.65 µm regions of the spectrum to produce model results that can be used to derive quality
thresholds for cloud typing, all near-infrared (NIR) data was re-simulated using this model.
However, there was no reason to re-simulate infrared (IR) data since the Streamer bandwidths
for the IR channels of interest are similar to the corresponding MODIS bands.
The cloud effective particle size was set to 10 µm for all water particles and 30 µm
for all ice particles.
These are the same values used in the International Satellite Cloud
Climatology Project (ISCCP) data set processing (Rossow et al., 1996). The cloud liquid/ice
water content was set to 0.2 gm-3 for water clouds and 0.07 gm-3 for ice clouds, though these
values should have little impact since the visible cloud optical depth is being specified directly.
All algorithm threshold values and/or functions were developed initially from theoretical data
then adjusted, if needed, based on the analysis of many AVHRR or MODIS scenes for a large
variety of conditions. Thus, even though ice particles were taken to be spherical in the NIR and
IR, cloud particle size was fixed, and only totally cloudy scenes were simulated (e.g. cloud edges
were not simulated) by the radiative transfer models, the thresholds were adjusted based on
actual satellite data to help account for these and other factors. In summary, simulations were
used to define the expected behavior and shape of the threshold functions and comparisons with
actual observations were used to adjust the final threshold curves.
3. Calculation of 3.75 µm Reflectance
8
During the day, the radiance at 3.75 µm has both a significant solar and thermal
component. To obtain a 3.75 um reflectance, the contribution to the total radiance from thermal
emission must approximated and removed. As in Key and Intrieri (2000) and Heidinger et al.
(2004), the 3.75 µm reflectance (R[3.75])) is calculated as shown in equation 1.
R 3.75
L 3.75
Lo u
B T 11
B T 11
(1)
L[3.75] is the observed 3.75 um radiance, B(T[11]) is the Planck Function radiance at 3.75 um
calculated using the observed 11 um brightness temperature, Lo is the solar constant for the 3.75
um band (adjusted for earth-sun distance), and u is the cosine of the solar zenith angle.
4a. AVHRR Cloud Overlap Detection
The presence of cloud overlap is checked for using the AVHRR algorithm
described in detail in PH04. Only a summary is given here. The AVHRR cloud overlap
detection algorithm utilizes the 0.65 µm reflectance (R[0.65]) and brightness temperatures from
the infrared window region of the spectrum (11 µm and 12 µm). The physical basis of this
algorithm is that for a single layer cloud, the 0.65 µm reflectance and the 11 – 12 µm brightness
temperature difference (split window brightness temperature difference) should behave as
predicted by plane-parallel radiative transfer simulations. In general, as a single layer cloud
becomes optically thick, its reflectance increases and its split window brightness temperature
difference (SWBTD) decreases (Inoue, 1985). In the case of a semi-transparent cirrus cloud
overlying a lower water cloud, the vertical separation has little effect on its reflectance but a
large effect on the SWBTD. Given a sufficient temperature difference between the cirrus and the
lower water cloud, the difference in transmission through the cirrus cloud at 11 µm and 12 µm
9
will result in a SWBTD that is much larger than that predicted by plane-parallel theory for a
single-layer water cloud with a similar reflectance. The detection of cloud overlap in the
AVHRR algorithm is fundamentally a detection of this deviation from plane-parallel behavior.
Model simulations were performed using Streamer to determine appropriate 11 – 12 µm
brightness temperature difference as a function of R[0.65] for the detection of cloud overlap.
The viewing and solar zenith angles are also taken into account and a few other constraints are
also applied to the algorithm. For details concerning those constraints refer to PH04. Further,
poleward of 60o a near-infrared reflectance minimum and maximum thresholds are applied in an
effort to reduce the cloud overlap false alarm rate over snow and ice. When AVHRR channel 3a
(3b) is available the 1.65 µm (3.75 µm) reflectance must be greater than 20% (6%) and less than
40% (18%) in order to apply the SWBTD test. The lower bound is used to help prevent single
layer cirrus clouds over a snow surface from being classified as cloud overlap and the upper
bound is to prevent single layer water clouds in the presence of a dry mid and upper atmosphere
from being identified as overlap. Note that when snow is present at lower latitudes the risk for
the false detection of cloud overlap will be greater with this test.
4b. VIIRS Cloud Overlap Detection
The VIIRS cloud overlap algorithm is also described in detail in PH04 so only a
brief synopsis is given here. The VIIRS algorithm includes all of the tests associated with the
AVHRR cloud overlap detection algorithm and an additional group of tests that incorporate NIR
data. If a given pixel passes either group of tests, then it is assumed that cloud overlap is present.
The following NIR spectral properties are exploited in the VIIRS algorithm. In the 1.65 µm
region of the spectrum, ice particles absorb radiation much more strongly than water particles
10
(Pilewskie and Twomey, 1987). Thus, the radiation reflected back to the satellite at 1.65 µm will
be greater when an optically thick water cloud is present compared to an optically thick ice
cloud. Further, in the 1.38 µm region, water vapor is a strong absorber of radiation, so the
radiation detected by a satellite at this wavelength will mainly be from the upper troposphere,
unless the atmosphere is very dry. Due to this fact, the 1.38 µm band is very effective at
detecting cirrus clouds (Gao et al., 1993). If both the 1.65 µm reflectance (R[1.65]) and the 1.38
µm reflectance (R[1.38]) are greater than some specified thresholds, there is a good possibility
that both a high ice cloud and a lower water cloud are present in a given satellite field-of-view.
The model built to simulate MODIS data was used to determine the threshold values used in the
NIR test. Thresholds also vary with viewing and illumination geometry. A few other constraints
are applied to the algorithm, these are described in PH04.
Also, only the near-infrared
reflectance test is used poleward of 50o.
5a. AVHRR Cirrus Detection
The absorption of radiation by water vapor and cloud particles in semi-transparent
clouds is greater at 12 µm than 11 µm (Inoue, 1985). Cirrus clouds are mainly present in the
upper troposphere, above which water vapor amounts are relatively small. Thus for semitransparent ice clouds (e.g. cirrus), the difference between the brightness temperature at 11 µm
and 12 µm should mainly be due to the difference in absorption by the cloud particles at the two
wavelengths. Semi-transparent clouds that are lower in the troposphere should have a smaller
SWBTD since more absorption by water vapor will occur at both 11 µm and 12 µm because of
the greater path length through the atmosphere. If a semi-transparent ice cloud located in the
11
upper troposphere is present in a given satellite pixel, the SWBTD should be greater than for
clear sky or most lower clouds. These relationships were also described in Inoue (1987).
However, the SWBTD may also be large when viewing the edges of lower clouds or optically
thin lower clouds when the atmosphere above is sufficiently dry.
Streamer was used to perform a variety of calculations for single layer ice and water
clouds with various visible optical depths and the 1761 different atmospheric profiles contained
in the Television Infrared Observation Satellite (TIROS) initial guess atmospheres (TIGR-2)
database (Moine et al., 1987). Model simulations were performed for 7 different satellite
viewing zenith angles. Figure 1 shows the simulated SWBTD as a function of 11 µm brightness
temperature (BT[11]) for a viewing angle of 11o. For clarity, only simulations for about 120 of
the 1761 profiles used are shown. The threshold function used for this particular viewing zenith
angle is also shown. Threshold functions were initially determined by fitting a fourth degree
polynomial to model output in such a manner that visually provided the optimal boundary
between ice clouds with a visible optical depth of approximately 5 or less and all water clouds
and ice clouds with a visible optical depth greater than about 5. Thus, the algorithm was
designed to isolate the clouds indicated by the diamond and asterisk symbols shown in Figure 1
as best as possible (ice clouds with an optical depth between 2 and 5 are also of interest, but are
not shown in Figure 1 for clarity). Of course there is some ambiguity in the region close to the
threshold function, but the threshold function was created so that most of the ambiguous results
resided below the threshold function. Because of this, some non-opaque ice clouds will be
missed using this technique. When this test is applied to satellite data, the viewing zenith angle
is checked and the appropriate threshold function is used for a given satellite pixel. If the
observed SWBTD is greater than the threshold determined for a given BT[11], then this test is
12
passed. The model results indicate that this test best suited for identifying ice clouds with a
visible optical depth between 1 and 2. However, as Figure 1 shows, thin water cloud can exceed
the cirrus threshold value, so for daytime applications a NIR reflectance threshold is also applied
to aid in preventing cloud edges and thin low and mid-level water clouds from being classified as
cirrus clouds since clouds that contain water droplets will generally have a higher R[1.65] or
R[3.75] than clouds that only contain ice. Table 1 shows the surface type dependent nearinfrared thresholds that have been adopted based on the analysis of many MODIS and AVHRR
scenes.
Finally, cirrus detection with this test will not work very well in colder (drier)
atmospheres since the model results indicate that the SWBTD for an ice cloud with an optical
depth of 1 in a cold (dry) environment will have roughly the same SWBTD as an ice cloud of
optical depth 5 that is present in a much warmer (more moist) overall environment. A summary
of this test is give in Table 2. The coefficients used to define the SWBTD threshold functions
can be found at http://cimss.ssec.wisc.edu/viirs/.
5b. VIIRS Cirrus Detection
In the first VIIRS cirrus detection method, the R[1.38] and R[3.75] values are used
to supplement the SWBTD test described in section 4a. As discussed earlier, R[1.38] will most
often only exceed a certain threshold value when high cloud is present in a given satellite pixel.
The threshold value chosen is 0.025(2.5%). Also, the presence of ice cloud particles will act to
reduce R[3.75]. R[3.75] is used instead of R[1.65] because R[3.75] model simulations indicate
that R[3.75] is not as dependent on surface type. Both R[1.38] and R[3.75] can then be applied
to the SWBTD used in the AVHRR algorithm as extra constraints in order to help prevent certain
mid-level cloud features and mid and low cloud edges from being misclassified as non-opaque
ice clouds. The R[3.75] supplemental thresholds used are listed in Table 1.
13
A second method, that is applied independently of the test described above, utilizes
the 1.38µm/0.65µm reflectance ratio (RAT[1.38, 0.65]) based on the work of Roskovensky and
Liou (2003). The use of this ratio helps to lesson the effects of water vapor associated with
detecting cloud in the mid and upper troposphere solely using the 1.38 µm reflectance and
provides for a much greater sensitivity to cirrus with an optical depth less than 1 as Roskovensky
and Liou demonstrated. If RAT[1.38, 0.65] is greater than the threshold value shown in Figure 2
and the 0.65 µm reflectance is less than 0.40 (40%), cirrus is said to be present. The 0.40 visible
reflectance threshold was selected since only ice clouds with an optical depth of about 5.0 or less
are of interest. In Figure 2, RAT[1.38, 0.65] is shown as a function of scattering angle(Θ)
defined as:
Θ = cos-1(cosθsun cosθsat + sinθsun sinθsat cosφ),
(2)
where θsun is the solar zenith angle, θsat is the satellite zenith angle, and φ is the relative azimuth
angle. By this definition, angles less (greater) than 90o represent backward (forward) scattering.
Since RAT[1.38, ,0.65] is not strong function of scattering angle, a constant threshold value was
adapted. Over snow and ice surfaces this test will not be effective at identifying cirrus clouds
because RAT[1.38, 0.65] will be greatly reduced due to the increase in R[0.65] caused by the
bright surface. However, the SWBTD test will be unaffected by the presence of a reflective
surface. A summary of these tests are given in Table 2. An additional multi-spectral cirrus
detection test is described in Section 6b.
6a. AVHRR NIR Test for Cloud Phase Discrimination
During the day, NIR reflectances can be effectively utilized to infer cloud phase,
14
especially for optically thick clouds.
Pilewskie and Twomey (1987) demonstrated this ability
using reflectances in the 1.65 µm region of the spectrum. Key and Intrieri (2000) used AVHRR
channel 3b (3.75 µm) reflectances to determine cloud phase in the Arctic. The 1.65 µm and 3.75
µm reflectances were modeled for single layer water and ice clouds of various optical depths
ranging from 0.1 to 20.0 for a variety of viewing and illumination angles and surface types.
Results for a vegetated (grass) surface are shown for the 1.65 µm and 3.75 µm bands in Figures
3a and 3b, respectively, as a function of scattering angle. Model results indicate that the distinct
separation between water and ice does not vary very much as a function of scattering angle for
either R[1.65] or R[3.75], so constant threshold values are used for three different surfaces:
water/snow/ice, vegetated land, non-vegetated land (e.g. desert). These values are shown in
Table 3. Water (ice) cloud is deemed to be present if the observed NIR reflectance is greater
(less) than the threshold value. As with most methods, NIR reflectance thresholds will have a
limited utility for clouds that have a visible optical depth less than about 1. NIR reflectances are
also very sensitive to cloud particle size (Lee et al., 1997), meaning that ice clouds composed of
very small particles may have a similar NIR reflectance as a water cloud composed of large
water droplets.
6b. VIIRS NIR/IR Test for Cloud Phase Discrimination
A combined NIR/IR technique is used in a series of cloud phase determining
spectral tests designed to be used with the VIIRS. As Strabala et al. (1994) and Baum et al.
(2000a) showed, 8.5 µm – 11 µm brightness temperature differences can be used to effectively
separate water cloud from ice cloud in that the 8.5 µm – 11 µm difference (BTD[8.5, 11]) will be
larger when mainly ice is present at cloud top compared to water. The physical reason for this
15
behavior can be elucidated upon examination of the imaginary index of refraction (mi), which is
a direct indicator of absorption/emission strength for a given size and shape distribution of cloud
particles. Near 8.5 µm, mi is roughly the same for water and ice particles, whereas at 11 µm mi is
larger for ice particles than water particles. Hence BTD[8.5, 11] will be larger for an ice cloud
than a water cloud if both clouds had the same temperature. Figure 4 shows BTD[8.5, 11] as a
function of BT[11] for a viewing angle of about 11 degrees. The threshold function used to
separate water from ice cloud is also shown on the figure. The threshold function was chosen
based on the modeling of single layer water and ice clouds of various optical depths ranging
from 0.1 to 20 using over 100 atmospheric profiles using Streamer. The cloud top pressure of all
ice (water) clouds was fixed at 300 mb (700 mb). Cloud top heights and temperature will vary
simply because many different atmospheric profiles were used. Threshold functions for 7
different viewing angles were created in the same manner as the SWBTD cirrus detection
threshold functions. Observed values of BTD[8.5, 11] that are greater (less) than threshold value
for a given BT[11] and viewing zenith angle implies that ice (water) cloud is present. The
coefficients used to define the threshold functions used in this bi-spectral infrared test can be
found at http://cimss.ssec.wisc.edu/viirs/.
NIR/visible reflectance thresholds are used as additional constraints to the bispectral IR algorithm discussed above to aid in further distinguishing cloud phase.
The
following additional constraints are employed: 1). ice clouds must have a 1.65 µm/0.65 µm
reflectance ratio less than 1.0, 2). opaque ice clouds must have a BT[11] < 263.16 K, and 3). if
BT[11] > 273.16 K, the cirrus cloud type can be retrieved if BTD[8.5, 11] > dynamic threshold
and R[1.38] > 0.01 (1%) and R[3.75] < threshold value shown in Table 1.
7a. AVHRR Algorithms - logic
16
Figure 5 shows the complete AVHRR algorithm decision tree.
initially assigned to a series of spectral tests based solely on BT[11].
Each pixel is
From a physical
perspective, this approach helps to take into account the dependence of cloud top phase on cloud
top temperature. Unless BT[11] of the pixel is greater than 270 K, the presence of cloud overlap
is checked for. If BT[11] < 233.16 K, the maximum temperature for which homogeneous
freezing occurs, and the cloud overlap test was failed, the pixel is tested for the presence of nonopaque ice clouds using the AVHRR cirrus detection test. If the cirrus test is failed, the pixel is
classified as an opaque ice cloud, otherwise it is classified as a non-opaque ice cloud. However,
once a given pixel passes the cloud overlap test, no other tests are performed and the final
classification for that given pixel is cloud overlap.
If 233.16 K < BT[11]
253.16 K, and the cloud overlap test is failed, the cirrus test
is performed. If the cirrus test is failed, the NIR reflectance test is utilized. A pixel that has a
NIR reflectance greater than the threshold value is typed as being supercooled water/mixed
phase; otherwise, the opaque ice cloud type is assigned to the pixel.
If the 253.16 K < BT[11]
273.16 K, and the cloud overlap test is failed, the cirrus
test is performed. If the cirrus test is failed, the NIR reflectance test is utilized. A pixel that has
a NIR reflectance less than the threshold value and has a BT[11] < 263.16 K is typed as being an
opaque ice cloud; otherwise, the supercooled water/mixed phase cloud type is assigned to the
pixel.
If BT[11] > 273.16 K, the melting point of pure water, the cirrus detection test is
simply applied. If it is passed, then the pixel is classified as non-opaque ice cloud, otherwise, the
type is a warm water cloud.
7b. VIIRS Algorithm - logic
17
The VIIRS algorithm logic is analogous to the AVHRR algorithm logic and is
outlined in Figure 5. The series of spectral tests applied to each pixel is determined by BT[11].
The SWBTD overlap test is only applied when BT[11] < 270 K, whereas the NIR cloud overlap
test is applied when BT[11] < 280 K. The VIIRS algorithm structure is the same as the AVHRR
algorithm structure, except the expanded cloud overlap and cirrus algorithms are used and an
IR/NIR/visible test is utilized (as described in Section 6b) instead of the basic NIR test
associated with the AVHRR algorithm.
8. Algorithm Performance
Three different scenes are analyzed here in order to analyze the performance of each
algorithm. A tropical, mid-latitude, and high latitude scene are used to roughly qualitatively
assess the global applicability of each algorithm. All of the data shown were taken by the
MODIS instrument on board the Terra spacecraft and the MODIS cloud mask product
(Ackerman et al., 1998) was used to identify cloudy pixels. The first scene is mainly located
over the Indian Ocean near the northwest coast of Australia extending north through Indonesia
(April 4, 2003 at 0250-0300 UTC), this is the tropical scene. The mid-latitude scene is located
over the central United States and the data is from April 6, 2003 (1715-1725 UTC). Finally, the
high latitude scene covers much of Alaska and Northwestern Canada and portions of the Arctic
Ocean on May 7, 2000 at 2130 UTC.
Four different spectral 1-km images are used to qualitatively analyze the retrieved
cloud types in each scene. The 0.65 µm reflectance (MODIS band 1), 1.65 µm reflectance
(MODIS band 6), the inverted 11 µm brightness temperature (MODIS band 31), and 8.5 µm – 11
µm brightness temperature difference (MODIS band 29 – band 31) are displayed in four separate
18
images. As discussed in sections 6a and 6b, the R[1.65] and BTD[8.5, 11] images are useful for
discerning water clouds from ice clouds. The R[0.65] and BT[11] images can be used to help
infer cloud optical thickness and cloud height as well as cloud overlap.
a. Tropical Scene
In the tropical scene (Figures 6a-6d), low, mid, high, and deeply convective cloud
systems can be readily identified. There are also regions where multiple cloud layers are present,
especially in association with Tropical Cyclone Inigo in the right half of the image (this is even
more readily apparent in multispectral RGB images). The cloud typing results are shown in
Figures 7a-7c and the fraction of each cloud type, based only on cloudy pixels, is given in Table
4. The results from the two AVHRR algorithms (Figures 7a and 7b) are similar, except, at times,
for very thin cirrus. A visual analysis of this scene indicates that the AVHRR3a algorithm does a
better job at detecting cirrus than the AVHRR3b algorithm. But analysis of scenes where more
mid-level cloud is present (not shown) shows that the AVHRR3a algorithm often results in thin
mid-level clouds or the edge of mid-level cloud to be classified as cirrus, whereas, the
AVHRR3b algorithm does not result in such a large misclassification. The near-infrared (1.65
µm or 3.75 µm) thresholds used in the SWBTD cirrus detection algorithm were set as to produce
optimal results (minimize misclassification and maximize the number of correct retrievals),
based on model output and the analysis of many scenes, for each algorithm.
The AVHRR3a
algorithm detects about 39.40% of the cloudy pixels to be cirrus in this scene compared to
38.50% for the AVHRR3b algorithm. The VIIRS algorithm, however, is clearly much more
effective at finding thin cirrus clouds (46.45%). This is due to the 1.38 µm/0.65 µm ratio test
which is much more sensitive to high thin cloud than the SWBTD cirrus detection algorithm.
The thin cirrus that the VIIRS algorithm detects are generally classified as water clouds by the
19
AVHRR algorithm, this is clearly reflected in the statistics. In addition, about 3 percentage units
(pu) more cloud overlap is also detected with the VIIRS algorithm. The amount of cloud overlap
detected by each AVHRR algorithm is the same since the algorithms are identical at low
latitudes. It is important to note that the determination of cloud phase for optically thick, nonmulti-layered cloud systems is nearly the same for all three algorithms. Thus, at low latitudes,
the additional spectral information used in the VIIRS algorithm will likely be most beneficial
when viewing optically thin clouds or multi-layered cloud systems. In addition, the greatest
differences between the two AVHRR algorithms will occur near cloud edges.
b. Mid-latitude Scene
As can be seen in Figures 8a-8d, this scene is rather complex and is located over a
mostly land surface. Low and high clouds, much of which overlap, are present. Some of the
clouds with high tops are quite thick, while semi-transparent cirrus cloud is also present in the
Upper Midwest and Great Lakes regions.
The cloud typing results from the AVHRR3a,
AVHRR3b, and VIIRS algorithms are shown in Figures 9a through 9c respectively and Table 5.
Once again, all three algorithms produce roughly the same results for optically thick nonoverlapped clouds. Further, the only noticeable differences between the two AVHRR algorithms
is near cloud edges and for thin cirrus, but in general the results are very similar. The AVHRR
algorithms seem to perform well when optically thick clouds are present; however, very thin
cirrus clouds are quite often misclassified as supercooled water/mixed phase or water. This
problem is largely eliminated in the VIIRS algorithm, with approximately 3 pu more cirrus
detected despite the fact that some of the cirrus identified by the AVHRR algorithms corresponds
to cloud overlap in the VIIRS results. Also, in this scene, much more cloud overlap is detected
by the VIIRS algorithm (28.92% compared to 19.83%) because of the NIR reflectance test that is
20
used in addition to the SWBTD test. As is pointed out in PH04, the NIR reflectance test is more
effective than the SWBTD test at identifying cloud overlap when the top cloud layer is more
optically thick as is the case in this scene (see Figures 8a and 8b).
c. High Latitude Scene
This scene contains single layer supercooled water/mixed phase cloud and ice cloud
with some overlap between 140oW and 160oW south of 72oN (Figures 10a-10d). Also, this
scene is challenging since much of the cloud present is semi-transparent, even surface features
such as leads in the ice pack are still apparent in the 0.65 µm image in some of the cloudy
regions north of 72oN. The cloud typing results are shown in Figures 11a-11c and Table 6. Both
AVHRR algorithms, but especially the AVHRR3a algorithm, have difficulty near the edge of the
thin cloud north of 72oN. The R[1.65] and BTD[8.5, 11] images indicate that this cloud is
mostly composed of water droplets which are likely supercooled since BT[11] are less than
273.16K in this region. Much of the cloud edge is mistakenly labeled as cloud overlap and
opaque ice. The SWBTD cloud overlap test does not perform as well in the high latitudes
because of the bright snow surface and, because the atmosphere is generally quite dry, thin lower
level clouds are often detected using the SWBTD cloud overlap test, if no low level temperature
inversion is present. Also, clouds that are mostly composed of water droplets, if optically thin,
can be mistaken for ice cloud when a snow or ice surface is present beneath the cloud. This is
because the NIR reflectance used to help determine cloud phase will be more influenced by the
dark surface (dark at NIR wavelengths) than by the thin cloud, whereas BTD[8.5, 11] seems to
provide a better ice/water phase sensitivity for this situation, hence the results are much
improved for the VIIRS algorithm. The 3.75 µm reflectance is significantly more effective for
21
discriminating cloud phase than the 1.65 µm reflectance in this scene. It was found that R[1.65]
was very small (< 20%) for the edge of the mixed phase cloud referenced earlier and yet quite
large (> 30%) for some regions of ice cloud, R[3.75] did not exhibit this sort of large variation.
Thus, it is difficult to type this scene using a constant thresholding approach with R[1.65].
At high latitudes, only the NIR reflectance test is used to detect cloud overlap in the
VIIRS algorithm, so the occurrence of false cloud overlap detection is much less. It is also
important to point out, that much of the thin ice cloud over the snow/ice surface in this scene is
called opaque ice, not cirrus, by each of the algorithms. This is because the 1.38 µm/0.65 µm
ratio test is not very effective over surfaces that are bright at visible wavelengths and the
SWBTD cirrus test is less effective due to the more complicated atmospheric structure
characteristic of the high latitudes. For instance, persistent near surface temperature inversions
are very common (Liu and Key, 2003). These temperature inversions will often cause the
SWBTD to be closer to zero or, at times, negative. Cirrus detection will then often be limited at
high latitudes; however, the cloud phase appears to be generally accurate in this scene with the
VIIRS algorithm being most effective and the AVHRR3b algorithm being slightly better than the
AVHRR3a algorithm.
9. Validation
In an effort to quantitatively evaluate the performance and global applicability of
each algorithm, data from various instruments at the Department of Energy (DOE) Atmospheric
Radiation Program (ARM) Tropical Western Pacific (TWP), Southern Great Plains (SGP), and
North Slope of Alaska (NSA) sites were used to determine cloud top temperature or cloud type.
Comparisons were then made to cloud type retrievals from each algorithm using Terra-MODIS
data. At the TWP (Manus and Nauru) and SGP (Central Facility) sites cloud top temperature
22
was estimated from millimeter cloud radar derived cloud top heights (Clothiaux et al., 2000) and
atmospheric profiles. The atmospheric profiles for the TWP sites were taken from rawinsonde
data and profiles derived from integrated ground-based remote sensors (Han and Westwater,
1995; Turner et al., 1997) were used at the SGP site. Rawinsonde profiles were not used at the
SGP site because they were often not available near the time of the Terra overpass. At the NSA
(Barrow) site, NOAA's Environmental Technology (ETL) cloud classification product, which
was derived from measurements taken by ETL's millimeter cloud radar, microwave radiometer,
and rawinsondes profiles, were compared to the satellite retrievals of cloud type. A brief
description of the ETL cloud classification technique is given here. The cloud radar is used to
determine vertical cloud location and rawinsonde profiles are used to estimate the temperature
within in the cloud layer. If the temperature within the entire cloud layer is less than 233.16 K,
then the cloud layer is automatically classified as all ice. Otherwise, the combined microwave
radiometer and cloud radar technique of Frisch et al. (1995, 1998, 2002) is used to retrieve liquid
water path. If the liquid water path of a given cloud layer is roughly zero, then the cloud is
classified as an ice cloud. If the liquid water path is greater than zero and the temperature within
the cloud is less than 263.16K, the cloud is classified as mixed phase, otherwise it is classified as
all water. It should be noted that the millimeter cloud radar may sometimes fail to detect some
thin clouds and some high clouds, but in general should be a good indicator of vertical cloud
location. Though the work described here is termed “validation,” we acknowledge that this is
not a comparison to direct observations of cloud phase/cloud type, but more of a consistency
check based on observations that are completely independent of the satellite retrievals presented
in this paper.
Since both cloud overlap detection algorithms have already been validated in PH04,
23
only cases where a single cloud layer is indicated by millimeter cloud radar within a 30 minute
interval centered on the satellite overpass time are considered at the SGP and NSA sites. All
scenes chosen were picked in an automated fashion using software that examined the cloud radar
data, the only requirements imposed were that single layer cloud was present the entire 30
minute interval (e.g. no overlap and no clear breaks), the solar zenith angle be < 88o, and the
viewing angle be < 55o.
Thus no attempt was made to select scenes based on human
examination of the conditions near each site. For the TWP site, single layer ice clouds were not
common during the time period probed (2000-2002). Cirrus clouds were almost always located
above lower cloud layers, so multi-layered cloudy situations were used in the analysis as long as
the height of the top cloud layer did not vary by more than 1 km during the 30 minute time
interval considered. Since it was shown in PH04 that most pixels were indeed classified as cloud
overlap when the radar indicated multiple cloud layers for these scenes, the cloud overlap
detection algorithms were turned off and only single layer cloud types were considered. This
sort of analysis is also useful to determine if the top cloudy layer is typed correctly when lower
cloud layers are present, should cloud overlap not be detected by the algorithms.
At the TWP, SGP, and NSA sites 35, 43, and 78 scenes were examined
respectively. Cloudy MODIS pixels (as given by the operational MODIS cloud mask) that were
within 15 km of the ground-based instrumentation location were used to calculate an areaweighted cloud phase. The 30 minute time interval and 15 km radius were selected to roughly
account for cloud movement as in PH04. Pixels that were flagged as having multi-layered cloud
were also excluded from this analysis so that statistics were derived using the exact the same
pixels for each algorithm, although cloud overlap was not found to be the dominant cloud type
for any of the SGP or NSA scenes. The area-weighted cloud phase (AWP) was determined as
24
follows:
AWP = (0.0*Nwater + 0.5*Nsupercooled/mixed + 1.0*Nice)/Nsingle,
(3)
where Nwater is the number of pixels with type warm water, Nsupercooled/mixed is the number of
pixels with type supercooled water/mixed phase, Nice is the number of pixels with type opaque
ice or non-opaque ice, and Nsingle is the total number of non-overlapped (based on algorithm
results) cloudy pixels. A value of 0.0 indicates that every single layer cloudy pixel was typed as
water cloud, conversely, 1.0 indicates that every single layer cloudy pixel was typed as ice cloud.
Since any value between 0.0 and 1.0 may indicate that at least two different cloud types were
found, the dominant cloud type (e.g. the statistical mode of the cloud type) is shown as part of
the results. Thus an area-weighted cloud phase of 0.6, where supercooled water/mixed phase is
the dominant cloud type, indicates that the majority of pixels not typed as supercooled
water/mixed phase were typed as ice.
In Figures 12a-12c the results from the TWP site are shown. The average cloud top
temperature for the entire 30 minute interval is plotted against the AWP. The dominant cloud
type for each case is denoted by the different colors and cases in which only optically thin mid or
high cloud was likely present are symbolized by a triangle and all other cases by an asterisk.
Optically thin cloud cases are defined as: R[0.65] < 0.20 (20%) and BT[11] - Tcld > 10 K, where
R[0.65] and BT[11] of the MODIS pixel nearest the respective ARM site are used and Tcld is the
estimated average cloud top temperature at the site. Although, only one of the ice scenes
examined here meets these requirements. The total fraction, expressed as a percentage, of water,
mixed-phase, and ice clouds retrieved as a function of cloud top temperature and algorithm are
given in Table 7. Each percentage is derived by summing all of the pixels of a given type for
25
each case shown in Figure 12 and then summing over all cases and dividing by the total number
of cloudy non-overlapped pixels which were summed in the same manner. As indicated by the
cloud top temperature estimates, only one mid-level cloud scene was found out of those searched
with the rest of the scenes either having high cloud or boundary layer cloud. The VIIRS
algorithm, on average, detects slightly more cirrus (~1 pu more than the AVHRR3a algorithm
and ~2 pu more than the AVHRR3b algorithm) when the radar indicates a high cloud layer.
Almost all of the high cloud scenes examined were multi-layered situations (this is why nearly
all of the ice clouds have a visible reflectance greater than 20%), but none of the algorithms
retrieve anything other than cirrus as the dominant cloud type. This may be an indication that
when high cloud overlaps lower cloud layers and cloud overlap is not detected, the top cloud
layer will still mainly be typed correctly. The two AVHRR algorithms produce similar results
for each scene. Boundary layer cloud seems to be typed well by each algorithm, but each
algorithm detects mostly high cloud when the cloud radar only indicated mid-level cloud.
Although, due to horizontal inhomogeneity in cloud fields, it is possible that multiple cloud types
are present within 15 km of each ARM site.
Analogous to Figures 12a-12c and Table 7, Figures 13a-13c and Table 8 show the
results from the ARM SGP site. As the scene analysis in Section 8 indicated, the AVHRR3a
algorithm has a propensity to detect more cirrus than the AVHRR3b algorithm, correctly and
incorrectly. When supercooled water/mixed phase cloud is present at the SGP site, more pixels
tend to be classified as cirrus (see Table 8) in the AVHRR3a algorithm. All three algorithms are
unable to differentiate optically thin supercooled water/mixed clouds from water clouds when
BT[11] > 273.16 K. This is a known weakness associated with each algorithm. As expected, the
VIIRS algorithm is best at detecting optically thin ice clouds (~13 pu more than either AVHRR
26
algorithm), although there are still a few cases where optically thin cirrus (indicated by the
triangle symbol in Figure 13) are being misclassified as water clouds. Since model results reveal
that the 1.38µm/0.65µm reflectance ratio test should be effective at unambiguously detecting
high clouds with a visible optical depth of about 0.5 or greater, the cirrus present in these cases
must be very tenuous and this is apparent in the R[0.65] and BT[11] data near the SGP site. The
sensitivity of the 1.38µm/0.65µm reflectance ratio test can be adjusted to detect such thin high
clouds, but many mid-level clouds would then be misclassified as cirrus. R[0.65] for the pixel
closest to the ARM site was less than 40% for all of the cases where cirrus is the dominant cloud
type, so it appears as though no optically thick ice clouds were mislabeled as cirrus by any of the
algorithms. Water clouds appear to be handled well by each algorithm.
As stated earlier, the validation effort at the NSA site is aimed at comparing the
satellite-derived cloud phase with the ETL's multi-instrument retrievals which will serve as
“truth”. Figures 14a-14c show the 30 minute average shortwave optical depth, calculated from
retrievals of liquid/ice water path and particle size, plotted against AWP. For water clouds, the
techniques of Frisch et al. (1995, 1998, and 2002) are applied to cloud radar and microwave
radiometer data to derive water optical depth. Cloud radar and IR radiometer data and the
methods of Matrosov (1999) and Matrosov et al. (2002) are used to determine ice cloud optical
depth. The optical depth of mixed phase clouds is a linear combination of the water and ice
contributions. Although the uncertainties in the retrieved parameters used to calculate the optical
depth may be as large as 40% (50%) for water (ice) particle size and 60% (100%) for liquid (ice)
water content, the optical depth is only shown to gain some rough insight as to whether a
particular cloud layer was optically thin or thick, so the exact values are not significant in this
context.
27
In Figures 14a-14c, the ETL cloud classification is given by the different symbols
and, as before, the dominant satellite-retrieved cloud type is depicted by the different colors.
Table 9 is analogous to Tables 7 and 8. According to the ETL cloud classification, either a
mixed-phase cloud or an ice cloud was present for the majority of the 78 scenes used in this
analysis.
Of the 18 water scenes though, the dominant retrieved phase is supercooled
water/mixed phase the majority of the time, regardless of the algorithm. This result occurs
simply because BT[11] < 273.16 K for many of the MODIS pixels within 15 km of the NSA site.
With respect to mixed-phase clouds, the VIIRS algorithm, with one exception, always retrieves
supercooled water/mixed phase as the dominant cloud type, but the AVHRR3a (AVHRR3b)
algorithm classifies 9 (5) scenes as being ice cloud dominated. This even occurs with some
clouds estimated to have an optical thickness greater than 10. The tendency observed here was
also evident in the scene described in Section 8c. For ice clouds with an estimated optical depth
less than 1, both AVHRR algorithms classify more pixels as containing ice cloud than the VIIRS
algorithm. The AVHRR3 algorithms retrieve almost 83% of pixels used in these comparisons to
contain ice cloud when the ETL retrieval indicates an ice cloud, whereas this occurs for about
77% of the pixels when the VIIRS algorithm is used. This is the opposite of the results scene at
the TWP and SGP sites. This could be because over snow/ice/water surfaces, which are dark in
the NIR, the NIR reflectance test used in the AVHRR algorithms will be naturally biased toward
less reflective ice clouds when clouds that are very optically thin are present. In other words, if
clear sky snow-covered pixels were processed, they would always be typed as opaque ice by the
NIR reflectance test if BT[11] < 263.16 K. Conversely, the BTD[8.5, 11] tends to be biased
towards retrieving supercooled water cloud when very thin cloud of any phase is present. These
relationships may account for the greater fraction of ice cloud retrieved by the AVHRR
28
algorithms for optically thin cloudy situations. These differences may also be partly due to
errors in the ETL cloud classification. None of the algorithms are able to effectively separate
cirrus clouds from opaque ice clouds. For the reasons discussed in Section 8c, the methods used
for cirrus detection often do not perform well at high latitudes.
10. Conclusion
Three algorithms for determining cloud type during the day with satellite imager
data were described.
Five cloud type categories are considered: water cloud, supercooled
water/mixed phase cloud, opaque ice cloud, non-opaque ice cloud (e.g. cirrus), and cloud overlap
(e.g. multiple cloud layers).
Two algorithms were created to be used with data from the
AVHRR. The AVHRR algorithms are identical except for the near-infrared data that is used.
One algorithm uses AVHRR channel 3a (1.6 µm) reflectances and the other uses AVHRR
channel 3b (3.75 µm) pseudo-reflectances. Both of these algorithms are necessary since channel
3a was not available until NOAA-15 and it is not possible to transmit data from both channels
simultaneously. The two AVHRR cloud typing schemes are used operationally in NOAA's
CLAVR-x processing system. The third algorithm utilizes additional spectral bands that are
available on the MODIS and will be available on the 16 channel VIIRS, which will replace the
AVHRR on board the NPOESS. The AVHRR algorithms utilize data in the 0.65 µm, 1.65 µm or
3.75 µm (depending on which is available), 11 µm, and 12 µm regions of the spectrum. In
addition to the bands just listed, the VIIRS algorithm incorporates additional information from
the 1.38 µm and 8.5 µm regions.
The analysis of three 1 km MODIS scenes revealed that the VIIRS algorithm will
be much more effective as identifying thin cirrus clouds, especially those with a visible optical
29
depth less than 1, than the AVHRR algorithms. This is accomplished through the added use of a
1.38 µm/0.65 µm reflectance ratio test in the VIIRS algorithm. The VIIRS algorithm also has
the capability to identify cloud overlap for a greater variety of multiple cloud layer situations.
The cloud phase of single layer clouds was also shown to be more accurately determined using
the 8.5 µm – 11 µm brightness temperature difference in the VIIRS algorithm than near-infrared
reflectances used in the AVHRR algorithms. This is mainly because the surface albedo at nearinfrared wavelengths can vary considerably with surface type and the 8.5 µm – 11 µm brightness
temperature difference will not vary much with respect to surface type. It was also determined
that the two AVHRR algorithms produce nearly identical results except for certain thin clouds
and cloud edges. The AVHRR3a algorithm tends to mis-classify the thin edges of some low and
mid-level clouds as cirrus and opaque ice more often than the AVHRR3b algorithm. Thin
supercooled water/mixed phase clouds will be generally classified as water clouds by all three
algorithms. At high latitudes, cirrus clouds are often classified as opaque ice clouds because of
inherent weaknesses in the cirrus detection algorithms. A comparison with inferences of cloud
type based on measurements made by ground-based instruments at the Atmospheric Radiation
Program (ARM) sites in the Tropical Western Pacific, Southern Great Plains, and North Slope of
Alaska supports these conclusions. Overall, the techniques implemented in the VIIRS algorithm
result in a significant improvement over the capabilities of the AVHRR algorithms.
The work presented in this paper was needed to lay the ground work for future
cloud type studies involving AVHRR and VIIRS (MODIS) data. A future study will include a
global analysis of several days of MODIS data to learn more about algorithm impact for a larger
variety of conditions and to assess the impact on global cloud type statistics. Comparisons to the
MODIS operational cloud phase algorithm (Platnick et al., 2003) will also be performed in future
30
studies. Finally, the algorithms described in this manuscript are relatively simple to implement
and require no auxiliary data and may be applicable to current and future sensors other than the
AVHRR and VIIRS (MODIS).
For instance the AVHRR algorithm could be used on
Geostationary Orbiting Environmental Satellite (GOES) I-series imager data and the VIIRS
algorithm on GOES-R (scheduled for launch in 2013) Advanced Baseline Imager (ABI) data.
Acknowledgments
We would like to thank Dr. Bryan Baum for insightful discussions. The MODIS data were
obtained from the National Aeronautic and Space Administration (NASA) Distributed Active
Archive Center (DAAC). Millimeter cloud radar data and atmospheric profile data for the
Tropical Western Pacific (TWP) and Southern Great Plains (SGP) sites were obtained from the
Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S. Department of
Energy, Office of Science, Office of Biological and Environmental Research, Environmental
Sciences Division. This research was funded by the NOAA Integrated Program Office (IPO)
(Federal Fund: NA07EC0676). The views, opinions, and findings contained in this report are
those of the authors and should not be construed as an official National Oceanic and
Atmospheric Administration or U.S. Government position, policy, or decision.
31
11. References
Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C Moeller, and L. E. Gumley,
1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103, 3214132157.
Baum, B. A., P. F. Soulen, K. I. Strabala, M. D. King, S. A. Ackerman, W. P. Menzel, and P.
Yang, 2000a: Remote sensing of cloud properties using MODIS airborne simulator
imagery during SUCCESS, 2, Cloud thermodynamic phase. J Geophys. Res., 105, 1178111792.
Baum, B. A. and J. D. Spinhirne, 2000b: Remote sensing of cloud properties using MODIS
airborne simulator imagery during SUCCESS, 3, Cloud overlap. J Geophys. Res., 105,
11793-11804.
Baum, B. A., V. Tovinkere, J. Titlow, and R. M. Welch, 1997: Automated cloud classification of
global AVHRR data using a fuzzy logic approach. J. Appl. Meteor., 36, 1519-1540.
Bennartz, R. and J. Fischer, 2000: A modified k-distribution approach applied to narrow band
water vapor and oxygen absorption estimates in the near infrared. J. Quantitative
Spectroscop. Radiat. Transfer, 66, 539-553.
Chen, T., W. B. Rossow, Y. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate,
13, 264-286.
Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and
B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using
a combination of active remote sensors at the ARM CART Sites. J. Appl. Meteor., 39,
645-665.
Frisch, A. S., M. Shupe, I. Djalalova, G. Feingold, and M. Poellot, 2002: The retrieval of stratus
32
cloud droplet effective radius with cloud radars. J. Atmos. And Oceanic Tech., 19, 835842.
Frisch, A. S., C. W. Fairall, G. Feingold, T. Uttal, and J. B. Snider, 1998: On cloud radar and
microwave radiometer measurements of stratus cloud liquid water profiles. J. Geophys.
Res., 103 (D18), 23195-23197.
Frisch, A. S., C. W. Fairall, J. B. Snider, 1995: Measurement of stratus cloud and drizzle
parameters in ASTEX with Ka-band Doppler radar and a microwave radiometer. J. Atmos.
Sci., 52 (16), 2788-2799.
Gao, B.-C., A. F. H. Goetz, and W. J. Wiscombe, 1993: Cirrus cloud detection from airborne
imaging spectrometer data using 1.38 um water vapor band. Geophys. Res. Lett., 20, 301304.
Hahn, C. J., S. G. Warren, J. London, R. M. Chervin, and R. Jenne, 1982: Atlas of Simultaneous
Occurrence of Different Cloud Types over the Ocean. NCAR Tech. Note TN-201 + STR,
212 pp. [NTIS PB83-152074.]
Hahn, C. J., S. G. Warren, J. London, R. M. Chervin, and R. Jenne, 1984: Atlas of Simultaneous
Occurrence of Different Cloud Types over Land. NCAR Tech. Note TN-241 + STR, 214
pp.
Han, Y. and E. R. Westwater, 1995: Remote sensing of tropospheric water vapor and cloud
liquid water by integrated ground-based sensors. J. Atmos. And Oceanic Tech., 12 (5),
1050-1059.
Heidinger, A. K., R. Frey, and M. J. Pavolonis, 2004: Relative Merits of the 1.6 and 3.75 µm
channels of the AVHRR/3 for cloud detection. Canadian J. Rem. Sen., 30 (2), 1-13.
Hutchison, K. D., 1999: Application of AVHRR/3 imagery for the improved detection of thin
33
cirrus clouds and specification of cloud-top phase. J. Atmos. Oceanic Tech., 16, 18851899.
Inoue, T., 1987: A cloud type classification with NOAA-7 split-window measurements. J.
Geophys. Res., 92 (D4), 3991-4000.
Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent
cirrus clouds by bi-spectral measurements in the 10 µm window region. J. Meteor. Soc.
Japan, 64(1), 88-99.
Key, J. R. and J. M. Intrieri, 2000: Cloud particle phase determination with AVHRR. J. Appl.
Meteor., 39, 1797-1804.
Key, J. R. and A. Schweiger, 1998: Tools for atmospheric radiative transfer: Streamer and
FluxNet. Comput. Geosci., 24, 443-451.
Kratz, D. P., 1995: The correlated k-distribution technique as applied to the AVHRR channels. J.
Quant. Spectrosc. Radiat. Transfer, 53, 501-517.
Lee, T. F., F. J. Turk, and K. Richardson, 1997: Stratus and fog products using GOES-8-9 3.9
µm data. Wea. and Forecasting, 12, 664-677.
Li, J., W. P. Menzel, Z. Yang, R. A. Frey, and S. A. Ackerman, 2003: High-spatial-resolution
surface and cloud-type classification from MODIS multispectral band measurements. J.
Appl. Meteor., 42, 204-226.
Liang, X. Z. and W. C. Wang, 1997: Cloud overlap effects on general circulation model climate
simulations. J. Geophys. Res., 102, 11039-11047.
Liu, Y. and J. R. Key, 2003: Detection and analysis of clear-sky, low-level atmospheric
temperature inversions with MODIS. J. Atmos. and Oceanic Tech., 20, 1727-1737.
Matrosov, S. Y., A. V. Korlolev, and A. J. Heymsfield, 2002: Profiling of cloud ice mass and
34
particle characteristic size from Doppler radar measurements. J. Atmos. And Oceanic
Tech., 19, 1003-1018.
Matrosov, S. Y., 1999: Retrieval of vertical profiles of ice cloud microphysics from radar and IT
measurements using adjusted regressions between reflectivity and cloud parameters. J.
Geophys. Res., 104 (D14), 16741-16753.
Moine, P., A. Chedin, and N. A. Scott, 1987: Automatic classification of air mass type from
satellite vertical sounding data.
Application to NOAA-7 observations.
Ocean-Air
Interactions, 1, 95-108.
Pavolonis, M. J., and A. K. Heidinger, 2004: Daytime cloud overlap detection from AVHRR and
VIIRS. J. Appl. Meteor., 43 (5), 762-778.
Pavolonis, M. J., and J. R. Key, 2003: Antarctic cloud radiative forcing at the surface
estimated from the AVHRR Polar Pathfinder and ISCCP D1 Datasets, 1985-1993. J.
Appl. Meteor., 42, 827-840.
Pilewskie, P., and S. Twomey, 1987: Cloud phase discrimination by reflectance measurements
near 1.6 and 2.2 µm. J. Atmos. Sci., 44, 3419-3420.
Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A.
Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE
Trans. on Geosci. and Rem. Sens., 41 (2), 459-471.
Roskovensky, J. K. and K. N. Liou, 2003: Detection of thin cirrus from 1.38 µm/0.65 µm
reflectance ratio combined with 8.6-11 µm brightness temperature difference. J. Geophys.
Res. Lett., 30 (19), 1985, doi:10.1029/2003GL018135.
Rossow, W. B., A.W. Walker, D. E. Beuschel, and M. D. Roiter, 1996: International Satellite
35
Cloud Climatology Project (ISCCP) documentation of cloud data. World Climate Research
Programme, WMO, 115 pp.
Schmidt, E. O., R. F. Arduini, B. A. Wielicki, R. S. Stone, and S. C. Tsay, 1995: Considerations
for modeling thin cirrus effects via brightness temperature differences. J. Appl. Meteor.,
34, 447-459.
Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 1994: Cloud properties inferred from 8-12µm data. J. Appl. Meteor., 33, 212-229.
Tag, P. M., R. L. Bankert, and L. R. Brody, 2000: An AVHRR multiple cloud-type classification
package. J. Appl. Meteor., 39, 125-134.
Takano, Y. and K. N. Liou, 1989: Solar radiative transfer in cirrus clouds. Part I: Singlescattering and optical properties of hexagonal ice crystals. J. Atmos. Sci., 46, 3-19.
Tian, L. and J. A. Curry, 1989: Cloud Overlap Statistics. J. Geophys. Res., 94, 9925-9935.
Turner, D. D., T. R. Shippert, J. C. Liljegren, Y. Han, and E. R. Westwater, 1997: Initial analysis
of water vapor and temperature profiles retrieved from integrated ground-based remote
sensors. Proceedings of the Sixth Atmospheric Radiation Measurement (ARM) Science
Team Meeting, DOE CONF-9603149.
Warren, S. G., C. J. Hahn, and J. London, 1985: Simultaneous occurrence of different cloud
types. J. Climate and Appl. Meteor., 24, 658-667.
Wiscombe, W. J., 1977: The Delta-M Method: Rapid yet accurate radiative flux calculations for
strongly asymmetric phase functions, J. Atmos. Sci.., 34, 1408-1422.
36
Figure Captions
Figure 1: Calculations of 11µm brightness temperature and the 11 µm – 12 µm brightness
temperature difference for a viewing zenith angle of 11.12o for water and ice particles of various
cloud optical depths (CODs). The thick bold line represents the function used to identify cirrus
clouds.
Figure 2: Calculations of 1.38 µm/0.65 µm reflectance ratio as a function of scattering angle for
water and ice particles of various cloud optical depths (CODs) over a water surface. The thick
bold line represents the threshold value used to help identify cirrus clouds.
Figure 3: Calculations of 1.65 µm reflectance (A) 3.75 µm reflectance (B) as a function of
scattering angle for water and ice cloud of various optical depths, ranging from 0.1 to 20.0, over
a vegetated grass surface. The thick bold line represents the threshold value used to help
separate water and ice clouds.
Figure 4: Calculations of 11 µm brightness temperature and the 8.5 µm – 11 µm brightness
temperature difference for a viewing zenith angle of 11.12o for water and ice cloud of various
optical depths ranging from 0.1 to 20.0. The thick bold line represents the function used to
separate water and ice clouds.
Figure 5: A flow chart representing the cloud typing algorithms presented in this study. “BT(11
µm)” symbolizes the 11 µm brightness temperature and “IR and/or NIR” represents the infrared
and near-infrared cloud phase tests. Only the NIR test is used in the AVHRR algorithms and a
combination IR and NIR test is used in the VIIRS algorithm.
Figure 6: Terra-MODIS images for April 4, 2003 (0250-0300 UTC): (A) 0.65 µm reflectance,
(B) 11 µm brightness temperature, (C) 1.65 µm reflectance, and (D) 8.5 µm – 11 µm brightness
37
temperature difference over the tropical Indian Ocean.
Figure 7: Cloud typing results using MODIS data from April 4, 2003 (0250-0300 UTC) for (A)
the AVHRR channel 3a algorithm, (B) the AVHRR channel 3b algorithm, and (C) the VIIRS
algorithm.
Figure 8: The same as figure 6, except for April 6, 2003 (1715-1725 UTC) over the continental
United States.
Figure 9: Same as Figure 7, except for April 6, 2003 (1715-1725 UTC).
Figure 10: The same as figures 6 and 8, except for May 7, 2000 (2130 UTC) over Alaska.
Figure 11: The same as figures 7 and 9, except for May 7, 2000 (2130 UTC).
Figure 12: Radar/atmospheric profile-derived cloud top temperatures plotted versus the areaweighted cloud phase retrieved using MODIS data over the ARM Tropical Western Pacific
(TWP) site. Water clouds are given a weight of 0.0, supercooled water/mixed phase clouds 0.5,
and ice clouds 1.0. Only cloud types for cloudy pixels that were within 15 km of the TWP site
were used to determine the area-weighted cloud phase. Both single and multi-layered clouds
were considered, but the cloud overlap detection algorithms were not used. The dominant cloud
type (the statistical mode) is also indicated by the different colors. The 0.65 µm reflectance and
the average estimated cloud top temperature at the ARM site minus the 11 µm brightness
temperature of the MODIS pixel closest to the ARM site are used to help identify cases where
very thin cloud may be present. A total of 35 MODIS scenes were analyzed.
Figure 13: The same as figure 12, except for the ARM Southern Great Plains site. In addition,
only single layer cloudy cases were considered A total of 43 MODIS scenes were analyzed.
Figure 14: Radar/IR radiometer/microwave radiometer-derived shortwave cloud optical depth
38
plotted versus the area-weighted cloud phase retrieved using MODIS data over the ARM North
Slope of Alaska site (NSA). Water clouds are given a weight of 0.0, supercooled water/mixed
phase clouds 0.5, and ice clouds 1.0. Only cloud types for cloudy pixels that were within 15 km
of the NSA site were used to determine the area-weighted cloud phase. Only single-layered
cloud cases were considered. The dominant cloud type (the statistical mode) is also indicated by
the different colors. The various symbols are used to identify the cloud phase given by NOAA's
Environmental Technology Laboratory (ETL) cloud classification product, which is derived
using cloud radar, microwave radiometer, and rawinsonde data. A total of 78 MODIS scenes
were analyzed.
39
Figure 1: Calculations of 11µm brightness temperature and the 11 µm – 12
µm brightness temperature difference for a viewing zenith angle of 11.12o for
water and ice particles of various cloud optical depths (CODs). The thick
bold line represents the function used to identify cirrus clouds.
40
Figure 2: Calculations of 1.38 µm/0.65 µm reflectance ratio as a function of
scattering angle for water and ice particles of various cloud optical depths
(CODs) over a water surface. The thick bold line represents the threshold
value used to help identify cirrus clouds.
41
Figure 3: Calculations of 1.65 µm reflectance (A) 3.75 µm reflectance (B) as a function of scattering angle for
water and ice cloud of various optical depths, ranging from 0.1 to 20.0, over a vegetated grass surface. The thick
bold line represents the threshold value used to help separate water and ice clouds.
42
Figure 4: Calculations of 11 µm brightness temperature and the 8.5 µm – 11
µm brightness temperature difference for a viewing zenith angle of 11.12o for
water and ice cloud of various optical depths ranging from 0.1 to 20.0. The
thick bold line represents the function used to separate water and ice clouds.
233.16 K < BT(11
YES
YES
Cirrus Test
YES
Type:
Cirrus
253.16 K < BT(11
NO
Type:
Opaque Ice
YES
Cirrus Test
YES
Type:
Cirrus
NO
273.16 K
BT(11
NO
Type:
Supercooled
Water/Mixed
Type:
Cirrus
NO
Type:
Opaque Ice
YES
Cirrus Test
YES
IR and/or NIR
Test # 1
YES
Overlap Test
NO
Type:
Overlap
m) > 273.16 K
YES
Overlap Test
NO
Type:
Overlap
m)
YES
Overlap Test
NO
Type:
Overlap
NO
253.16 K
YES
Overlap Test
YES
m)
NO
233.16 K
m)
BT(11
YES
IR and/or NIR
Test # 2
Type:
Opaque Ice
Cirrus Test
Type:
Overlap
NO
YES
NO
Type:
Cirrus
NO
Type:
Water
NO
Type:
Supercooled
Water/Mixed
Figure 5: A flow chart representing the cloud typing algorithms presented in this study. “BT(11 µm)” symbolizes the 11 µm brightness temperature
and “IR and/or NIR” represents the infrared and near-infrared cloud phase tests. Only the NIR test is used in the AVHRR algorithms and a
combination IR and NIR test is used in the VIIRS algorithm.
43
Figure 6: Terra-MODIS images for April 4, 2003 (0250-0300 UTC): (A) 0.65 µm reflectance, (B) 11 µm brightness
temperature, (C) 1.65 µm reflectance, and (D) 8.5 µm – 11 µm brightness temperature difference over the tropical
Indian Ocean.
44
Figure 7: Cloud typing results using MODIS data from April 4, 2003 (0250-0300 UTC) for (A) the AVHRR channel
3a algorithm, (B) the AVHRR channel 3b algorithm, and (C) the VIIRS algorithm.
45
Figure 8: The same as figure 6, except for April 6, 2003 (1715-1725 UTC) over the continental United States.
46
Figure 9: Same as Figure 7, except for April 6, 2003 (1715-1725 UTC).
47
Figure 10: The same as figures 6 and 8, except for May 7, 2000 (2130 UTC) over Alaska.
48
Figure 11: The same as figures 7 and 9, except for May 7, 2000 (2130 UTC).
49
50
Figure 12: Radar/atmospheric profile-derived cloud top temperatures plotted versus the area-weighted cloud
phase retrieved using MODIS data over the ARM Tropical Western Pacific (TWP) site. Water clouds are
given a weight of 0.0, supercooled water/mixed phase clouds 0.5, and ice clouds 1.0. Only cloud types for
cloudy pixels that were within 15 km of the TWP site were used to determine the area-weighted cloud phase.
Both single and multi-layered clouds were considered, but the cloud overlap detection algorithms were not
used. The dominant cloud type (the statistical mode) is also indicated by the different colors. The 0.65 µm
reflectance and the average estimated cloud top temperature at the ARM site minus the 11 µm brightness
temperature of the MODIS pixel closest to the ARM site are used to help identify cases where very thin
cloud may be present. A total of 35 MODIS scenes were analyzed.
51
Figure 13: The same as figure 12, except for the ARM Southern Great Plains site. In addition, only single
layer cloudy cases were considered A total of 43 MODIS scenes were analyzed.
52
Figure 14: Radar/IR radiometer/microwave radiometer-derived shortwave cloud optical depth plotted
versus the area-weighted cloud phase retrieved using MODIS data over the ARM North Slope of Alaska
site (NSA). Water clouds are given a weight of 0.0, supercooled water/mixed phase clouds 0.5, and ice
clouds 1.0. Only cloud types for cloudy pixels that were within 15 km of the NSA site were used to
determine the area-weighted cloud phase. Only single-layered cloud cases were considered. The dominant
cloud type (the statistical mode) is also indicated by the different colors. The various symbols are used to
identify the cloud phase given by NOAA's Environmental Technology Laboratory (ETL) cloud
classification product, which is derived using cloud radar, microwave radiometer, and rawinsonde data. A
total of 78 MODIS scenes were analyzed.
53
Table Captions
Table 1:
Near-infrared reflectance thresholds used in conjunction with the split window
brightness temperature test to detect cirrus clouds. The 1.65 µm (R[1.65]) reflectance or the 3.75
µm (R[3.75]) reflectance, depending on which is used, must be less than the threshold values
given in this table.
Table 2: A summary of the AVHRR3a, AVHRR3b, and VIIRS cirrus identification methods is
presented. In this Table, SWBTD is the 11 µm – 12 µm brightness temperature difference,
R[1.65], R[3.75], R[1.38], and R[0.65] represents the 1.65 µm, 3.75 µm, 1.38 µm, and 0.65 µm
reflectance respectively. RAT[1.38, 0.65] is the 1.38 µm/0.65 µm reflectance ratio and BTD[8.5,
11] is the 8.5 µm – 11 µm brightness temperature difference.
Table 3: Near-infrared reflectance thresholds used to distinguish water and ice clouds.. The 1.65
µm (R[1.65]) reflectance or the 3.75 µm (R[3.75]) reflectance, depending on which is used, must
be less (greater) than the threshold values given in this table for ice (water) clouds.
Table 4: Cloud type statistics for the MODIS scene shown in Figure 6. The percentage of
cloudy pixels that were flagged as each type are shown.
Table 5: Cloud type statistics for the MODIS scene shown in Figure 8. The percentage of
cloudy pixels that were flagged as each type are shown.
Table 6: Cloud type statistics for the MODIS scene shown in Figure 10. The percentage of
cloudy pixels that were flagged as each type are shown.
Table 7: The total fraction, expressed as a percentage, of water, mixed-phase, and ice clouds
retrieved at the TWP site as a function of cloud top temperature (Tcld) and algorithm. Each
percentage is derived by summing all of the pixels of a given type for each case shown in Figure
54
12 and then summing over all cases and dividing by the total number of cloudy non-overlapped
pixels which were summed in the same manner. N is the number of cases corresponding to each
cloud top temperature category.
Table 8: The same as Table 7, except for the SGP site.
Table 9: The same as Tables 7 and 8, except for the NSA site and the ETL-derived cloud phase
is substituted for cloud top temperature.
55
Table 1: Near-infrared reflectance thresholds used in conjunction with the split window brightness temperature
test to detect cirrus clouds. The 1.65 µm (R[1.65]) reflectance or the 3.75 µm (R[3.75]) reflectance, depending on
which is used, must be less than the threshold values given in this table.
Surface Type
R[1.65] Threshold
R[3.75] Threshold
Water/Snow/Ice
0.25 (25%)
0.15 (15%)
Vegetated Land
0.30 (30%)
0.15 (15%)
Non-vegetated land (desert)
0.55 (55%)
0.40 (40%)
56
Table 2: A summary of the AVHRR3a, AVHRR3b, and VIIRS cirrus identification methods is presented. In this
Table, SWBTD is the 11 µm – 12 µm brightness temperature difference, R[1.65], R[3.75], R[1.38], and R[0.65]
represents the 1.65 µm, 3.75 µm, 1.38 µm, and 0.65 µm reflectance respectively. RAT[1.38, 0.65] is the 1.38
µm/0.65 µm reflectance ratio and BTD[8.5, 11] is the 8.5 µm – 11 µm brightness temperature difference.
Logic
Algorithm If a given set of tests are found to be true for a given algorithm, then cirrus cloud may be present.
AVHRR3a
SWBTD > Dynamic Threshold and R[1.65 µm]< Threshold[Table 1]
AVHRR3b
SWBTD > Dynamic Threshold and R[3.75 µm] < Threshold[Table 1]
VIIRS
SWBTD > Dynamic Threshold and R[3.75] < Threshold[Table 1] and R[1.38] > 0.025 (2.5%)
or
RAT[1.38, 0.65] > 0.17 and R[0.65] < 0.40 (40%)
or
BT[11] > 273.16 K and BTD[8.5, 11] > Dynamic Threshold and R[1.38] > 0.01 (1%) and R[3.75] <
Threshold[Table 1]
57
Table 3: Near-infrared reflectance thresholds used to distinguish water and ice clouds.. The 1.65 µm (R[1.65])
reflectance or the 3.75 µm (R[3.75]) reflectance, depending on which is used, must be less (greater) than the
threshold values given in this table for ice (water) clouds..
Surface Type
R[1.65] Threshold
R[3.75] Threshold
Water/Snow/Ice
0.25 (25%)
0.06 (6%)
All other surface
0.32 (32%)
0.06 (6%)
58
Table 4: Cloud type statistics for the MODIS scene shown in Figure 6. The percentage of cloudy pixels that were
flagged as each type are shown.
Algorithm
Water (%)
(%)
Non-opaque ice
(cirrus) (%)
Multi-layered
Clouds (%)
Supercooled
Water/Mixed (%)
Opaque Ice
AVHRR3a
35.40
0.79
9.44
39.40
14.98
AVHRR3b
36.05
1.02
9.45
38.50
14.98
VIIRS
25.19
1.19
9.12
46.45
18.06
59
Table 5: Cloud type statistics for the MODIS scene shown in Figure 8. The percentage of cloudy pixels that were
flagged as each type are shown.
Algorithm
Water (%)
(%)
Non-opaque ice
(cirrus) (%)
Multi-layered
Clouds (%)
Supercooled
Water/Mixed (%)
Opaque Ice
AVHRR3a
13.23
19.36
33.69
13.88
19.83
AVHRR3b
13.28
19.59
33.93
13.38
19.83
VIIRS
13.03
15.38
25.97
16.70
28.92
60
Table 6: Cloud type statistics for the MODIS scene shown in Figure 10. The percentage of cloudy pixels that
were flagged as each type are shown.
Algorithm
Water (%)
(%)
Non-opaque ice
(cirrus) (%)
Multi-layered
Clouds (%)
Supercooled
Water/Mixed (%)
Opaque Ice
AVHRR3a
3.46
53.28
16.20
14.55
12.51
AVHRR3b
3.50
58.22
15.43
13.01
9.84
VIIRS
3.65
64.30
13.90
12.39
5.76
61
Table 7: The total fraction, expressed as a percentage, of water, mixed-phase, and ice clouds retrieved at the TWP
site as a function of cloud top temperature (Tcld) and algorithm. Each percentage is derived by summing all of the
pixels of a given type for each case shown in Figure 12 and then summing over all cases and dividing by the total
number of cloudy non-overlapped pixels which were summed in the same manner. N is the number of cases
corresponding to each cloud top temperature category.
Algorithm (Cloud Type)
Tcld
243.16 K
(%)
243.16 K < Tcld
(%)
273.16 K
Tcld > 273.16 K
(%)
N = 19
N=1
N = 15
AVHRR3a (water)
3.97
2.49
99.28
AVHRR3b (water)
4.28
3.81
99.28
VIIRS (water)
1.66
12.27
98.84
AVHRR3a (supercooled water/mixed)
0.71
7.63
0.09
AVHRR3b (supercooled water/mixed)
1.53
16.09
0.09
VIIRS (supercooled water/mixed)
1.95
31.68
0.09
AVHRR3a (ice)
95.33
89.88
0.63
AVHRR3b (ice)
94.20
80.10
0.63
VIIRS (ice)
96.39
56.05
1.08
62
Table 8: The same as Table 7, except for the SGP site.
Algorithm (Cloud Type)
Tcld
243.16 K
(%)
243.16 K < Tcld
(%)
273.16 K
Tcld > 273.16 K
(%)
N = 23
N=9
N = 11
AVHRR3a (water)
28.02
9.66
76.13
AVHRR3b (water)
28.73
15.54
76.78
VIIRS (water)
22.67
16.65
76.16
AVHRR3a (supercooled water/mixed)
9.25
67.94
22.74
AVHRR3b (supercooled water/mixed)
9.38
80.28
23.03
VIIRS (supercooled water/mixed)
1.91
80.61
22.74
AVHRR3a (ice)
62.74
22.40
1.14
AVHRR3b (ice)
61.90
4.18
0.20
VIIRS (ice)
75.42
2.74
1.10
63
Table 9: The same as Tables 7 and 8, except for the NSA site and the ETL-derived cloud phase is substituted for
cloud top temperature.
Algorithm (Cloud Type) ETL Type = Ice ETL Type = Mixed Phase ETL Type = Water
(%)
(%)
(%)
N = 22
N = 38
N = 18
AVHRR3a (water)
0.17
0.13
15.49
AVHRR3b (water)
0.17
0.13
15.49
VIIRS (water)
0.17
0.13
15.49
AVHRR3a (supercooled water/mixed)
16.86
79.34
78.22
AVHRR3b (supercooled water/mixed)
16.96
84.40
82.71
VIIRS (supercooled water/mixed)
23.37
98.41
84.51
AVHRR3a (ice)
82.98
20.53
6.29
AVHRR3b (ice)
82.87
15.47
1.79
VIIRS (ice)
76.46
1.45
0.00