Daytime Cloud Overlap Detection from AVHRR and VIIRS

Transcription

1 Daytime Cloud Overlap Detection from AVHRR and VIIRS Michael J. Pavolonis Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Andrew K. Heidinger Office of Research and Applications NOAA/NESDIS Accepted by the Journal of Applied Meteorology December 10, 2003 Corresponding author address: Michael Pavolonis, 1225 West Dayton St., Madison, WI

2 ABSTRACT 1 Two algorithms for detecting multi-layered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65 µm, 11 µm, and 12 µm regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38 µm and 1.65 µm near-infrared regions 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 next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semi-transparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second (VIIRS) algorithm was able to detect more cloud overlap than the first (AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed in order to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

3 2 1. INTRODUCTION Surface observations have shown that multi-layered cloud systems occur in most parts of the world (Warren et al., 1985). Such systems are especially common in the tropics where anvils associated with convective systems can spread out over large horizontal distances and with mid-latitude cyclones (Hahn et al., 1982, 1984; Tian and Curry, 1989). Atmospheric heating/cooling rates are greatly influenced by the vertical distribution of clouds (Liang and Wang, 1997). Gupta et al. (1992) and Wielicki et al. (1995) showed that the earth radiation budget will be largely influenced by the vertical location and coverage of clouds. General Circulation Model (GCM) simulations are also sensitive to the parameterization of multiple cloud layers (Liang and Wang, 1997; Morcrette and Jakob, 2000). For instance, Morcrette and Jakob (2000) found that the surface and top of the atmosphere radiative fluxes varied significantly in the European Centre for Medium-Range Weather Forecasts (ECMWF) general circulation model when the cloud overlap scheme was varied. In addition, satellite cloud property retrievals are generally performed under the assumption that only a single cloud layer is present in a given pixel. Because of this assumption, the quality of cloud optical depth, particle size, and cloud height retrievals will suffer when more than one cloud layer is actually present. The parameterization of cloud overlap in GCM's and cloud property retrievals in regions where cloud overlap is present need to be improved. Thus, it is important to gain a more complete understanding of the spatial and temporal characteristics of multi-layered cloud systems. With the creation of automated algorithms, satellite data can greatly aid in the study of cloud overlap. Detecting multi-layered cloud systems from space is often difficult since the higher clouds may be optically thick and obscure the presence of a lower cloud layer. Conversely, higher level clouds may be too thin to be detected in the presence of a thicker lower cloud from

4 3 current passive remote sensing observations. Two cloud layers may also have an insufficient vertical separation, making it difficult to distinguish the multi-layer cloud system from a single cloud layer using thermal emission signatures. Nevertheless, several techniques for classifying multi-layered cloud systems have been presented in the literature. Baum et al. (1995) utilized a multispectral, multiresolution technique for detecting regions of cloud overlap with the Advanced Very High Resolution Radiometer (AVHRR) and the High Resolution Infrared Radiometer Sounder (HIRS/2). Further, Ou et al. (1996) utilized a variety of spectral tests with AVHRR data for several scenes over the Southern Great Plains of the United States to detect cloud overlap. Baum et al. (1997) used a fuzzy logic classification scheme to detect the presence of cloud overlap using AVHRR data. Baum and Spinhirne (2000) presented a bispectral grouping approach that utilized data from the 1.63 µm and 11 µm bands of the MODIS (Moderate Resolution Imaging Spectroradiometer) airborne simulator (MAS). Each approach has its advantages and disadvantages. The goal of this work is to create effective, efficient, and globally applicable algorithms for detecting cloud overlap. In this paper, two algorithms for detecting multi-layered cloud systems with satellite data are presented. The first algorithm is partially based on the work of Ou et al. (1996) and utilizes data in the 0.65 µm, 11 µm, and 12 µm regions of the spectrum which are available on the AVHRR instrument (the AVHRR algorithm) on board the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites. The second algorithm utilizes data from the same spectral regions as the first algorithm in addition to data from the 1.38 µm and 1.65 µm spectral regions that are currently available on the MODIS and will be available on the 16 channel National Polar-orbiting Operational Environmental Satellite System (NPOESS) Visible/Infrared Imager/Radiometer Suite (VIIRS) that will be on board the next generation of

5 4 polar-orbiting satellites (the VIIRS algorithm). Because MODIS offers channels with similar spectral and spatial resolution as the AVHRR/VIIRS, MODIS data is used in this study exclusively. The development of both algorithms is critical. The AVHRR algorithm can be used to process over 20 years of AVHRR data for long-term studies while the VIIRS algorithm represents the capabilities for at least the next 20 years. Comparison of the results from the two algorithms provides guidance on potential continuity of a combined AVHRR/VIIRS cloud overlap climatology. In this paper, each algorithm will be evaluated qualitatively by examining three MODIS scenes and quantitatively through comparisons with cloud radar data collected in association with Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The theoretical data from radiative transfer models that were used to develop each algorithm are also presented along with sensitivity studies aimed at diagnosing the strengths and weaknesses of each algorithm. In all of the following sections cloud overlap is defined as being a semi-transparent ice cloud overlapping a cloud composed of liquid water droplets and all spectral bands referenced coincide with MODIS channel center wavelengths. 2. ALGORITHM DESCRIPTION a. AVHRR Algorithm The AVHRR algorithm utilizes 0.65 µm reflectance and brightness temperatures from the infrared window region of the spectrum (11 µm and 12 µm). The premise of the AVHRR algorithm is that for a single layer cloud, the 0.65 µm reflectance and the 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

6 5 temperature difference 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 split window brightness temperature difference. 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 will generally result in a split window brightness temperature difference that is much larger than that predicted by plane parallel theory for a single-layer cloud with a similar reflectance. The detection of overlap in the AVHRR algorithm is fundamentally a detection of this deviation from plane parallel behavior. Simulations were performed in order to better understand the relationship between the 0.65 µm reflectance and the split window brightness temperature difference. The radiative transfer model Streamer (Key and Schweiger, 1998) was used to simulate channel 1 (0.65 µm), channel 31 (11 µm), and channel 32 (12 µm) MODIS data. Simulations of an ice cloud overlapping a lower water cloud were performed using a variety of solar zenith angles, viewing zenith angles, and atmospheric profiles. Over 100 atmospheric profiles were randomly selected from the 1761 available in the Television Infrared Observation Satellite (TIROS) initial guess atmospheres (TIGR-2) database (Moine et al., 1987). The visible optical depth of both clouds was also varied. The cloud top pressure of both clouds was kept constant, with the water cloud top located at 700 mb and the ice cloud top positioned at 300 mb. The cloud effective particle size was set to 10 µm for water particles and 30 µm for ice particles. These are the same values used in the International Satellite Cloud Climatology Project (ISCCP) data set processing (Rossow et al., 1996). Liquid cloud droplets were taken to be spherical and Mie scattering was assumed. In the shortwave, the optical properties for solid hexagonal columns derived by Key et al. (2002) were used in the ice cloud simulations. In the longwave,

7 6 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 longwave behave as spheres may be flawed (Takano and Liou, 1989; Schmidt et al., 1995), but scattering in the longwave is secondary to absorption. The cloud liquid/ice water content was set to 0.2 gm -3 for the water cloud and 0.07 gm -3 for the ice cloud, though these values should have little impact since the visible cloud optical depth is being specified directly. The relative azimuth was set to a constant value of 80 o. Only the 0.65 µm reflectance will be dependent on the relative azimuth, so it was kept constant in order to simplify the approach. All cloud simulations were performed using a water surface reflectance model based on Briegleb et al. (1986). The sensitivity of the algorithm to various parameters will be discussed in Section 3. Figure 1 shows the results of model simulations for a single atmospheric profile, solar zenith angle, and viewing zenith angle. Calculations for an atmospheric profile typical of the mid-latitudes in the summer and a solar zenith angle of 30 o and a viewing zenith angle of 11 o are shown. The threshold function used for this particular solar zenith angle and viewing zenith angle is also shown. Threshold functions were determined by fitting a fourth degree polynomial to model output. The functions were constructed in such a manner that visually provided the optimal boundary between the cloud overlap results and the single layer cloud results so that most of the region where the results were ambiguous resided on the single cloud layer side of the threshold function. It is true that lowering the threshold function will increase the number of pixels that are flagged as containing cloud overlap; however, the number of pixels that are misclassified will also increase. For instance, single layer thin clouds in the presence of sun glint or over a relatively bright surface will more often be mistaken as cloud overlap. Cloud edges will also be misclassified more often. This is the reason that a more conservative approach was

8 7 taken when developing the AVHRR algorithm threshold function. Also, threshold functions were determined for 56 different combinations of solar zenith angle and viewing zenith angle. It should be noted that the threshold function displayed in Figure 1 was derived using all of the atmospheric profiles, not just the profile associated with this set of model calculations. Complete threshold function information can be found at When the algorithm is applied to satellite data, the solar zenith and viewing zenith angles are checked and the appropriate threshold function is used for a given satellite pixel. If the observed split window brightness temperature difference (SWBTD) is greater than the threshold determined for a given 0.65 µm reflectance, cloud overlap may then be present. A few other restrictions are applied and will be discussed later. As can be seen in Figure 1, the cloud overlap solutions differ most from the single layered cloud solutions when the visible optical depth of the high ice cloud is equal to 1 or 2 and the optical depth of the lower water cloud is 5 or greater. According to Wylie and Menzel (1999), most of the clouds located above 6 km in the tropics and mid-latitudes were found to have a visible optical depth that was between 0.5 and 1.8. In addition, most lower level clouds were found to have a visible optical depth greater than 6. Thus, the AVHRR algorithm should be effective at detecting a typical cirrus cloud overlying a typical water cloud. When the visible optical depth of the ice cloud is less than 0.5 or greater than 3, the distinction between the cloud overlap and either of the single-layered cases is not great, regardless of the optical depth of the lower water cloud. Other constraints are also applied to the algorithm. In order to limit thin cirrus clouds that overlie a surface which is bright at 0.65 µm (such as a desert) from being classified as cloud overlap, the 0.65 µm reflectance must be at least 0.30 (30%). Also, a single layer of

9 8 broken water clouds or the edge of a water cloud often will have a large split window brightness temperature difference and a visible reflectance greater than 0.30 (30%). However, such a cloud layer will often have an 11 µm brightness temperature that is greater than 270 K. Thus, cloud overlap is not allowed for unless the 11 µm brightness temperature is less than 270 K. This condition may sometimes cause high thin cirrus overlying low water cloud to be missed. Another problem occurs when a thin cirrus cloud overlies a snow or ice surface. Both the visible reflectance and the split window brightness temperature difference will often be large enough to pass the cloud overlap test. This problem can potentially be solved with the use of the 1.65 µm channel, which is effective at discriminating between snow/ice and water clouds (Heidinger et al., 2003; Kriebel et al., 2003). However the use of the 1.65 µm channel over snow/ice surfaces is not investigated here. Thus, thin cirrus clouds that overlie a snow or ice surface may be misclassified as cloud overlap. This is obviously not a problem in the tropics and much of the mid-latitudes. Further, cloud overlap is not checked for if the solar zenith angle is greater than 80 o. The AVHRR algorithm is summarized in Table 1. b. VIIRS Algorithm The VIIRS algorithm includes all of the tests associated with the AVHRR algorithm and an additional group of tests discussed in this section. Each of the two series of tests are applied separately. If a given pixel passes either group of tests, then it is assumed that cloud overlap is present. The following additional 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 (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

10 9 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 and the 1.38 µm reflectance 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. Model simulations were performed in order to determine the difference in the relationship between 1.65 µm reflectance and 1.38 µm reflectance for a single layer water cloud, a single layer ice cloud, and an ice cloud overlapping a water cloud. The model used to conduct the simulations discussed in this section 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 absorption by H2O, CO2, O3, CO, CH4, O2, NO, and other trace gases (Bennartz and Fischer, 2001; Kratz, 1995). The spectral bands available are the same as those associated with the MODIS instrument. Streamer was not used to simulate the 1.38 µm reflectance and the 1.65 µm reflectance when building the algorithm because the bandwidths used in Streamer are too broad compared to the MODIS bandwidths for these two channels. Both water and ice cloud particles were taken to be spheres at all wavelengths and Mie scattering was assumed. A sensitivity study was conducted to determine the potential effect of assuming spherical ice particles on the near-infrared reflectance algorithm using Streamer. Even though the bands that include 1.38 µm and 1.65 µm in Streamer are too broad to be used in algorithm development, it should be useful for performing qualitative comparisons. The results indicate that the distinction between single layer water and ice clouds and cloud overlap is slightly greater when ice particles were modeled as solid hexagonal spheres as opposed to spheres. This is an indication that the near-infrared reflectance algorithm may be able to detect

11 10 more cloud overlap cases than tests using spherical particles suggest. A standard mid-latitude summer profile based on data in Ellingson et al. (1991) was used for all simulations, although calculations were performed for various viewing and illumination geometry configurations. In addition, two different surface types were used, water and grass because the 1.65 µm surface reflectance will be much larger in association with non-snow/ice land surfaces than water or snow/ice surfaces. All other variables were set to the same values used in the Streamer simulations discussed in Section 2a. The sensitivity of the 1.65 µm and 1.38 µm reflectance to the atmospheric profile and various cloud parameters will be discussed in Section 3. Figure 2 shows the model results when a water surface directional hemispherical reflectance model from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Spectral Library is used for a given scattering angle (31.4 o in this case). The scattering angle, Θ, is defined as Θ = cos -1 (cosθ sun cosθ sat + sinθ sun sinθ sat cosφ), (1) where θ sun is the solar zenith angle, θ sat is the satellite zenith angle, and φ is the relative azimuth angle (where 0 o is looking toward the sun and 180 o is looking away from the sun). In this case the solar zenith angle is 30 o, the viewing zenith angle is about 10 o, and the relative azimuth angle is 90 o. The relative azimuth is now taken into consideration since both the 1.65 µm and 1.38 µm reflectance will be dependent on its value. Calculations were performed for many different combinations of viewing and illumination angles so that the 1.38 µm and 1.65 µm thresholds that define cloud overlap may be defined for many values of scattering angle. Bins of 10 o were chosen, so there are 18 different functions that determine the 1.65 µm reflectance threshold with respect to the 1.38 µm reflectance. The 1.38 µm reflectance threshold was set to a constant value

12 11 of (2.5%) for all scattering angles over a water surface. The threshold functions were determined in the same manner as for the SWBTD test. A somewhat conservative approach was used to determine the 1.38 um threshold to help prevent the false detection of cloud overlap. For instance, upon analyzing several MODIS scenes it was found that the 1.38 um reflectance of many single layer mid-level clouds will often fall in the % range. Also, even low clouds can have a 1.38 um reflectance of 2.0% or greater if the overlying atmosphere is sufficiently dry. This especially occurs at higher latitudes. For complete threshold function information, see The model results shown in Figure 2 indicate that the near-infrared reflectance (NIRR) test should be effective at detecting cloud overlap when the ice cloud has a visible optical depth (τ) so that 1 τ 8 (i.e. the 1.65 µm reflectance and the 1.38 µm reflectance both exceed the threshold values depicted by the two solid lines). When the lower water cloud has an optical depth of 10 or greater, cloud overlap can be detected even if the ice cloud has an optical depth of 8. These model results suggest that the NIRR test should be more effective at detecting cloud overlap for a greater number of optical depth combinations than the SWBTD test over a dark surface. Figure 3 is the same as Figure 2 with the exception that a bidirectional and directional reflectance model (from the ASTER Spectral Library) for a grass surface, which reflects significantly more radiation at 1.65 µm than a water surface, was utilized. A constant 1.38 µm reflectance threshold of (2.7%) is used over land surfaces. The results indicate that it is more difficult to distinguish between cloud overlap and a single layer ice cloud for certain optical depth combinations when a land surface that is reflective at 1.65 µm is used. Since the surface and a given water or ice cloud may now have a similar 1.65 µm reflectance, the

13 12 distinction between single layered clouds and cloud overlap is somewhat blurred. In summary, the NIRR test will be more effective over water/snow/ice surfaces than over other land surfaces, especially, when the optical depth of the ice cloud is 5 or greater. In addition to the 1.65 µm and 1.38 µm thresholds imposed, a few other tests must be passed if cloud overlap is going to be deemed to be present in a given satellite pixel using the NIRR test. The 1.65 µm reflectance and the 1.38 µm reflectance may exceed the thresholds set when a single layer mid-level cloud is present. Thus, when the 1.38 µm reflectance is less than 0.08 (8%), the SWBTD test (without additional tests discussed in Section 2a) is applied. The threshold of 8% was chosen based on the analysis of many MODIS scenes. Thus the biggest difference between the VIIRS and AVHRR algorithm performance will be found when an ice cloud with a visible optical thickness of 4 or greater overlaps a water cloud with an optical depth of at least 3 because the SWBTD test is not used as a constraint when the 1.38 µm reflectance is greater than 8%. In this scenario, the VIIRS algorithm should detect the presence of overlap while the AVHRR algorithm should not detect it. Further, if the 1.38 µm reflectance is greater than 0.40 (40%), then the NIRR test is not applied. This threshold was determined through model simulations using various viewing and illumination geometry configurations. It was found that the 1.38 um reflectance rarely exceeded 40%, except occasionally for ice clouds with an optical depth of at least 20. Thus there was no need to define the cloud overlap detection threshold function beyond 40%. Also, to prevent a single layer of low-level water cloud or thin mid-level cloud in the presence of a dry mid and upper atmosphere, from passing the NIRR test, the 11 µm brightness temperature must be less than 280 K. One final condition is imposed, the ratio of 1.65 µm reflectance and 0.65 µm reflectance must be less than 1.0. This reduces the chance of thin cirrus clouds present over a bright 1.65 µm surface from being mistakenly labeled

14 13 as cloud overlap. It should be noted though that both the VIIRS and AVHRR algorithms may not work well over bright deserts since both the 0.65 µm reflectance and the 1.65 µm reflectance are large. The VIIRS algorithm is summarized in Table ALGORITHM SENSITIVITIES Additional modeling studies were performed in order to test the sensitivity of the SWBTD test to several assumptions that were made when building them. It would also be useful to gain a better understanding of algorithm performance under various conditions and to theoretically assess the potential that each algorithm has to falsely detect cloud overlap when none is present. The sensitivity calculations for the SWBTD test were performed using Streamer. All parameters that are not being tested were set to the baseline values described in Section 2a. The sensitivity of the SWBTD test was investigated with respect to the relative azimuth angle, cloud effective particle size, and the temperature difference between the top of the ice cloud and the top of the water cloud. The NIRR test was further studied by conducting model simulations aimed at determining under which conditions the test will perform best. The sensitivity calculations for the NIRR test were performed using the same model and baseline parameters described in Section 2b. The solar zenith angle was set to 30 o, the viewing zenith angle was set to 10 o, and the relative azimuth angle was set to 90 o for all calculations. The sensitivity of the NIRR to atmospheric profile, effective cloud particle size, and the separation between the ice cloud and the water cloud in the two cloud layer scene is examined. a. Relative Azimuth Angle Figure 4 shows the sensitivity of the SWBTD test to the relative azimuth angle. Each contour depicted in Figure 4 represents where the simulated split window brightness

15 14 temperature difference is equal to the value given by the threshold function derived for a solar zenith angle of 30 o and a viewing zenith angle of 11 o. Results are shown for three different relative azimuth angles. As can be seen, the relative azimuth angle should have a minor impact on the results. It appears as though slightly less overlap will be detected when the optical depth of the water cloud is less than 5 for a relative azimuth angle of 40 o or 120 o compared to the baseline value of 80 o. One of the extra constraints applied to the SWBTD test was that the 0.65 µm reflectance has to be at least 0.30 (30%). In these simulations, the 0.65 µm reflectance was less than 0.30 (30%) when the optical depth of the water cloud was less than 3, thus the difference in the results shown for the three different relative azimuth angles will largely be negated by this extra constraint. b. Effective Particle Size Model simulations were also performed to assess the sensitivity of the SWBTD test to effective cloud particle radius. Similar to Figure 4, each contour depicted in Figure 5 represents where the simulated split window brightness temperature difference is equal to the value given by the threshold function derived for a solar zenith angle of 30 o, a viewing zenith angle of 11 o, and a relative azimuth angle of 80 o for different particle size combinations. For each optical depth combination inside of each of those contours, the simulated split window brightness temperature difference is greater than the cloud overlap threshold value. When a smaller particle size, compared to the baseline values (10 µm and 30 µm, respectively), is used for either the water cloud or the ice cloud, the SWBTD test is passed for a wider range of cloud optical depth pairs. When the effective particle radius of the water cloud is increased from 10 µm to 15 µm and the effective particle radius of the ice cloud is set to the baseline value (30 µm), the split window brightness temperature test is passed for fewer combinations of cloud optical

16 15 depth. Simulations were also done using an effective ice particle radius of 40 µm and greater and a constant effective water particle radius of 10 µm. These results are not shown since the SWBTD test is never passed when the effective ice particle radius is 40 µm or greater. This is because the difference in absorption by ice particles at 11 µm and 12 µm decreases for larger ice particles. The use of spherical particles in deriving the algorithm may also play a role. The use of spheres to model ice particles in the infrared has been demonstrated by past studies such as (Giraud et al., 1997). If non-spherical particles were used to model the ice clouds, the range of effective radii where this algorithm is most sensitive would probably change; however, the use of non-spherical particles should not alter the qualitative shape of Figure 5. A given contour in Figure 6 represents where the simulated 1.65 µm reflectance is equal to the value given by the threshold function derived for a scattering angle between 30 o and 40 o when the 1.38 µm reflectance is greater than the threshold value of (2.5%) (i.e. the NIRR test is passed). For each optical depth combination inside of each of those contours, both the 1.38 µm reflectance and the 1.65 µm reflectance are greater than the threshold values. So if all other auxiliary tests are passed, any point that falls inside of those lines would be considered to be cloud overlap. In Figure 6, various water and ice particle sizes are examined using a water surface reflectance model. The results indicate that whenever either water or ice particles that are larger than the baseline values of 10 µm (water) and 30 µm (ice) are used, less cloud overlap will be detected. This is because smaller water and ice particles will reflect more incoming radiation at 1.38 µm and 1.65 µm than larger particles. However, this result is an improvement compared to the SWBTD test, which will have difficulty detecting any cloud overlap when ice particles with an effective radius of greater than 40 µm are present in a given scene. The NIRR test will also be much more effective than the SWBTD test when the optical depth of the ice

17 16 cloud is larger and the optical depth of the water cloud is smaller. When a grass surface reflectance model is utilized, the results are similar, although less cloud overlap will be detected compared to those shown in Figure 6 since the NIRR test does not work as well over nonsnow/ice land surfaces. c. Cloud Location and Atmospheric Profile A series of model simulations aimed at determining the necessary separation between the water cloud top and the ice cloud top in order for the SWBTD test to be effective were performed. Figure 7 shows the difference between the simulated split window brightness temperature difference and the threshold values contoured as a function of the actual temperature difference between the cloud top of the water cloud and the cloud top of the ice cloud and the visible optical depth of the ice cloud. Positive values indicate that the SWBTD test was passed and negative values indicate that it was failed. The results shown in Figure 7 are for a constant water cloud optical depth of 20 and a solar zenith angle of 30 o, a viewing zenith angle of 11 o, and a relative azimuth angle of 80 o. The cloud top pressure of the water cloud was varied between 900 mb and 500 mb within a given atmospheric profile and the cloud top pressure of the ice cloud was kept constant at 300 mb in these simulations. In order for the SWBTD test to be effective, the actual temperature difference between the water cloud top and the ice cloud top should be at least 35 K (~5 km, assuming a lapse rate of 6.5 K/km). Figure 7 also indicates that the algorithm will be effective over the greatest range of cloud top temperature differences when the visible optical depth of the ice cloud is about 2. This behavior is consistent with other studies that have shown that the SWBTD is largest for cirrus with optical depths of around 2 (Baum et al., 1994). In addition, the greater the temperature difference between the cloud top of the water cloud and the cloud top of the ice cloud, the greater the range of cloud optical thickness of the ice

18 17 cloud for which the algorithm is effective. A similar sensitivity study was performed for the NIRR test as well. Figure 8, which is analogous to Figure 6, shows when the simulated 1.65 µm reflectance is equal to the value given by the threshold function when the 1.38 µm reflectance is greater than the threshold value of (2.5%) contoured as a function of the ice cloud and water cloud optical depths for different combinations of cloud top pressure. When the optical depth of the ice cloud is less than 2, the algorithm will be most effective when the ice cloud top is located at a pressure of 300 mb or less and the water cloud top is less than 700 mb. The radiative signal from the ice cloud at 1.38 µm will increase when the cloud top pressure of the ice cloud decreases and the secondary contribution by the water cloud is greater when the cloud top pressure of the water cloud is smaller. This secondary contribution is just large enough to help push the 1.38 µm reflectance over the threshold value when the ice cloud has an optical depth of less than 2. Thus, when water cloud is located closer to the surface, the algorithm will not be as effective at detecting cloud overlap when the optical depth of the ice cloud is less than 2. However, the SWBTD test that is used in addition to the NIRR test would likely be effective at detecting cloud overlap when the separation between the ice cloud and water cloud is greater. In addition, when the optical depth of the ice cloud is greater than 2, the vertical location of each cloud becomes less important. The general sensitivity to cloud vertical location is similar if a grass surface reflectance model is used. To study how the NIRR test performs when temperature and humidity profiles differ from the standard mid-latitude summer profile used to derive the 1.65 µm and 1.38 µm thresholds, calculations were performed for mid-latitude winter, subarctic summer and tropical standard profiles (Ellingson et al., 1991). Figure 9 is analogous to Figures 6 and 8. According to

19 18 Figure 9, the greatest differences occur when the visible optical depth of the ice cloud is less than 1. For an atmosphere that is colder and drier there is a greater number of cloud optical depth combinations for which the NIRR test is passed. This is because when the atmosphere is dry the 1.38 µm reflectance will be larger when a thin ice cloud overlaps a water cloud with an optical depth of at least 3 since the ice cloud and the lower water cloud will now contribute more significantly to the 1.38 µm reflectance causing it to exceed (2.5%). Overall, the difference between each profile is relatively small when the optical depth of the ice cloud is 2.0 or greater. d. Risks for False Overlap Detection Various simulations were also performed to determine under which conditions it is possible for cloud overlap to be falsely detected. A summary of the results is given here. When an effective water particle radius smaller than 10 µm is used, a single layer water cloud passes the SWBTD test for optical depths of 10 or less. However, many of these cases are screened out by the additional constraint applied to the SWBTD test that the 11 µm brightness temperature must be less than 270 K. Further, when the effective particle radius of the single layer ice cloud is decreased from 30 µm to 25 µm, the SWBTD test is also passed when the visible optical depth of the ice cloud is 5 or less. However, since the 0.65 µm reflectance falls below the 0.30 (30%) threshold when the optical depth of the ice cloud is 4 or less, this effect should be limited to ice clouds that have a small effective particle radius (~25 µm or less) and a visible optical depth of around 5. Thin single layer water or ice clouds located in the middle troposphere can also pass the SWBTD test. However, the 0.65 µm reflectance for such clouds is commonly less than the 0.30 (30%) threshold value, so single layer clouds located in the mid-troposphere should rarely

20 19 be misclassified as being cloud overlap by the SWBTD test when the additional constraints are applied. Modeling studies were also performed to check for the possibility that either a single layer water cloud or a single layer ice cloud would be classified as cloud overlap by the NIRR test. It was found that a single layer ice cloud, with a cloud top pressure of 300 mb does not pass the NIRR test regardless of the atmospheric profile used. When a single layer water cloud with a cloud top pressure of 700 mb is put into a drier atmosphere, such as a standard midlatitude winter atmosphere, the NIRR test is passed when the optical depth of the water cloud is 3 or greater. However, the 1.38 µm reflectance for this particular water cloud is always less than 0.08 (8%), so the SWBTD test is also applied. The application of this test should largely prevent single layer water clouds in a dry atmosphere from being mistakenly flagged as being cloud overlap. It is possible that when a water cloud with an effective particle radius less than 10 µm is located below a dry atmosphere, the 1.38 µm reflectance will be greater than 0.08 (8%) and the 1.65 µm reflectance will be greater than the threshold value. Since the 1.38 µm reflectance is greater than 0.08 (8%), the SWBTD test is not performed, so such a cloud may mistakenly be classified as cloud overlap as long as the 11 µm brightness temperature is less than 280 K and the 1.65 µm/ 0.65 µm reflectance ratio is less than 1. A cloud composed largely of liquid water with a cloud top pressure of around 500 mb (i.e. a mid-level cloud) and an optical depth of about 10 or greater may be misclassified by the NIRR test (with additional constraints) as being cloud overlap if the ratio of 1.65 µm reflectance and 0.65 µm reflectance is less than 1.0 for such a case. This is especially true in dry atmospheres.

21 20 4. ALGORITHM PERFORMANCE Three different examples of how each algorithm performs are shown in this section. 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 three scenes are located in the following regions: the Eastern Pacific Ocean well off the coast of Southern Mexico (July 2, 2002 at 1940 UTC), the South Central Plains region of the U.S. (November 23, 2000 at 1730 UTC), and the South Central region of the U.S. (April 21, 2002 at 1705 UTC). More specific information about each scene is given in Table 2. Images for multiple spectral channels are shown to make more readily apparent the cloud overlap in each scene. Displayed in Figures (A-D) are a 1.65 µm image, a 1.38 µm image, a 0.65 µm, and an 11 µm image respectively. The 1.65 µm image is useful in that water clouds will appear much brighter than ice clouds. Only high altitude clouds can be seen in the 1.38 µm image. Thus when both the 1.65 µm image and the 1.38 µm image are relatively bright, there is a strong possibility that an ice cloud and a water cloud are simultaneously present at a given location. In addition, it can be visually inferred from the 0.65 µm and 11 µm images that cloud overlap is present throughout much of this scene. The results of the AVHRR cloud overlap algorithm are shown in Figures E while the VIIRS algorithm results are shown in Figures F. The regions deemed to be cloud overlap by each algorithm are highlighted in black. Other parts of the image simply show the 0.65 µm reflectance of a given pixel where no overlap was detected. Table 3 shows the percent of cloudy pixels in each scene that were classified as cloud overlap for each algorithm. a. Scene 1 The clouds shown in Figures 10 (A-F) are mainly associated with ascent in a region

22 21 of convergence of tropical moisture. The VIIRS algorithm detects about 9 percentage units (pu) more cloud overlap than the AVHRR algorithm. The VIIRS algorithm is able to detect more cloud overlap largely because the sensitivity of the 1.65 µm and 1.38 µm spectral regions is such that overlap can be detected over a larger range of optical thicknesses of the high ice cloud. The results from this scene prove that cloud overlap can be effectively detected even if neither cloud layer is uniform. Further, it is difficult to find areas where cloud overlap was falsely identified. The limitations of both algorithms are also evident in that once the optical depth of the ice cloud becomes greater than some value (probably near 8, as indicated by the modeling results), the lower water cloud is masked by the ice cloud. An example of this can be found near pixel 630 and line 1770 in Figures 10 (A-F). b. Scene 2 The cloud features in this scene are associated with a mid-latitude baroclinic zone (Figures 11 (A-F)). The 1.38 µm image shows that high cloud is present throughout much the scene. Further, the 1.65 µm reflectance image indicates that water cloud is also prevalent over a large portion of this scene. The results of both cloud overlap algorithms once again show that more cloud overlap is detected by the VIIRS algorithm (Figure 11 F), especially in regions where the high cloud is optically thicker near the left side and the top of the scene. The VIIRS algorithm detects a total of 3 pu more overlap than the AVHRR algorithm, although, the results from this scene illustrate that both algorithms are able to detect cloud overlap when both the ice cloud and the water cloud are relatively uniform in structure. c. Scene 3 The main region of cloud cover in this scene is associated with a synoptic-scale cold front (Figures 12 (A-F)). The region near the cold front is characterized by a band of lower

23 22 water cloud which is overlapped in smaller regions by high cloud, probably composed mostly of ice particles. Both algorithms are able to locate much of the regions where cloud overlap is present. The VIIRS algorithm is only able to detect 2.4 pu more overlap than the AVHRR algorithm in this particular scene. Some of the cloud overlap identified by both algorithms may actually be a single layer cloud. The band of clouds just to the west of the main band associated with the cold front appears to only be a single layer cloud (see Figure 12 D). This feature is likely mid-level cloud. As discussed in Section 3d, there are a few circumstances for which single layer mid-level clouds can be misclassified as cloud overlap. On the whole, the results from the AVHRR algorithm and the VIIRS algorithm are comparable for this scene. 5. VALIDATION In an effort to quantify the performance of each algorithm, millimeter cloud radar data was used to infer cloud boundaries. A total of 180 Terra-MODIS overpasses encompassing a wide range of dates and cloudy conditions are used. Roughly half of the scenes include the ARM Southern Great Plains (SGP) site and the other half includes either the ARM Tropical West Pacific Manus site or the ARM Tropical West Pacific Nauru site. All of the scenes were chosen based upon one criteria: there must be either a single layer or multiple cloud layers present within 15 minutes of the Terra overpass indicated by the millimeter cloud radar merged moments product (Clothiaux et al., 2000). The millimeter cloud radar images were obtained from the University of Utah. Each algorithm was applied to MODIS pixels that were within a 15 km radius of the cloud radar location. The 30 minute time interval and the 15 km radius were chosen to account for cloud movement. Each radar image was examined for the presence of multiple cloud layers within 15 minutes of the Terra overpass. Each scene was then characterized as having only a single layer cloud the entire 30 minute interval, multiple cloud

24 23 layers less than 50% of the 30 minute interval (minor cloud overlap cases), or multiple cloud layers at least 50% of the 30 minute interval (major cloud overlap cases). A cloud overlap fraction was then calculated as the number of pixels within 15 km of the radar location divided by the total number of cloudy pixels as given by the MODIS cloud mask. Histograms of the calculated cloud overlap fraction given by both algorithms were then created for single layer cloud cases, minor cloud overlap cases, and major cloud overlap cases. There were a total of 78 single layer cases, 48 minor cloud overlap cases, and 54 major cloud overlap cases. Each of the 180 cases is listed in Tables 4-6. Figures 13 and 14 show the validation results for the AVHRR algorithm and the VIIRS algorithm respectively. As would be expected, when only a single layer cloud is present, no cloud overlap is detected by either algorithm for nearly 80% of the cases. The VIIRS algorithm is slightly more likely to falsely detect cloud overlap than the AVHRR algorithm simply because the VIIRS algorithm consists of two separate spectral tests, each of which can potentially produce false identifications of cloud overlap. The mean percent cloud overlap found for minor and major cloud overlap cases is 12.1% and 45.8% respectively with the AVHRR algorithm and 13.8% and 48.7% with the VIIRS algorithm. No cloud overlap was found within 15 km of the cloud radar for 45.8% of the minor cloud overlap cases and for 13.0% of the major cloud overlap cases with the AVHRR algorithm. Those numbers are 43.8% and 11.1% for the VIIRS algorithm. The major cloud overlap cases for which no cloud overlap was detected were generally characterized by at least one thin cloud layer or multiple cloud layers that were separated by small distances or by the presence of a geometrically thick upper-most layer cloud, as indicated by the radar data. As the modeling results presented in the paper show, neither algorithm is expected to perform well for those type

25 24 of scenes. In addition, many of the minor cloud overlap cases were characterized by thin cirrus overlapping small-scale boundary layer water cloud which is difficult to detect with either algorithm and difficult to validate due to the small-scale nature of the cloud overlap. Overall, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection. Finally, it should be noted that statistics presented in this validation study cannot be used to assess the absolute accuracy of either algorithm since the results also depend on the characteristics of multilayer cloud systems which vary in both time and space. 6. CONCLUSION Multi-layered cloud systems are commonly observed in many regions of the world. In this paper, two multi-spectral algorithms were presented for identifying cloud overlap from space, where cloud overlap is defined as a semi-transparent ice cloud overlapping a water cloud. The first algorithm (the AVHRR algorithm) utilizes information from the 0.65 µm, 11 µm, and 12 µm regions of the spectrum. The second algorithm (the VIIRS algorithm) incorporates additional information from the 1.65 µm and 1.38 µm portions of the spectrum. The AVHRR algorithm makes use of a split window brightness temperature difference test that is supplemented with a few additional single channel tests. Cloud overlap can be detected by two different methods in the VIIRS algorithm. The first method is the same used in the AVHRR algorithm and the second is a near-infrared reflectance test that is constrained using additional spectral information. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the optical depth of the ice cloud is greater than 3. In addition, both algorithms will work best when the visible optical depth of the water cloud is greater than 5. The VIIRS algorithm will be most effective over a surface that is dark at 1.65

26 25 µm. Comprehensive model sensitivity studies were also performed in order to assess the sensitivity of each algorithm to cloud particle size and the vertical location of each cloud layer. The results indicate that the split window brightness temperature difference test used in the AVHRR and VIIRS algorithms will not perform well when the effective ice particle radius exceeds 40 µm (at least when spherical ice particles are used), whereas the VIIRS near-infrared reflectance test is not as sensitive to cloud particle size. It was also found that the split window brightness temperature test will perform better than the near-infrared reflectance test when the separation between the ice cloud and the water cloud is relatively large. When the separation between the ice cloud and the water cloud is not as large, the near-infrared reflectance test will perform better. Each algorithm was tested on three different MODIS scenes. In each scene, regions of cloud overlap were clearly present. In all three cases, the VIIRS algorithm was able to detect more cloud overlap than the AVHRR algorithm. The millimeter cloud radar merged moments product from the ARM Southern Great Plains site and both of the ARM Tropical West Pacific Sites was used to help validate both cloud overlap detection algorithms. In general, the VIIRS algorithm should be more effective than the AVHRR algorithm, although, it was shown that both algorithms will be useful for identifying cloud overlap from satellite. Neither algorithm exhibited a strong propensity for mistaking a single cloud layer as cloud overlap; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection. Only two cloud layers are discussed in this paper, although both algorithms should be useful for detecting multilayer cloud systems in general as long as an ice cloud located in the upper troposphere is present over a cloud layer composed mostly of liquid water droplets. The

27 26 AVHRR cloud overlap algorithm is currently implemented in the extended Cloud from AVHRR (CLAVR-x) processing system run by NOAA. The CLAVR-x cloud typing results including the cloud overlap detection is available globally in real-time at the pixel level and mapped to a resolution of 50 km. In addition, the AVHRR algorithm has been applied to the historic AVHRR data record and will be included in future versions of the AVHRR Pathfinder Atmospheres datasets (Jacobowitz et al., 2003). The preliminary results are encouraging in that the most cloud overlap is seen in the convectively active regions of the tropics and in the mid-latitude storm tracks. This result is supported by the work of Hahn et al. (1982, 1984) and Tian and Curry (1989), which was mainly based on surface observations of cloud cover. Future work will include the processing of MODIS data on a global scale so that the VIIRS cloud overlap detection algorithm can be used to determine the presence of cloud overlap on a global scale. Additional global cloud overlap studies will also be performed using the long AVHRR data record. With the launch of the CloudSat (Cloud Satellite) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) missions in 2004 (Stephens et al., 2002), coincident and co-located data from a spaceborne radar and lidar will provide direct estimates of cloud overlap that will be used to better characterize the performance of the algorithms developed here.

28 27 Acknowledgments We would like to thank Bryan Baum for insightful discussions. provided software used to help process the MODIS data used in this study. Liam Gumley The surface reflectance models used in the development of the near-infrared cloud overlap algorithm were obtained from the ASTER Spectral Library through the courtesy of the Jet Propulsion Laboratory. The MODIS data were obtained from the National Aeronautic and Space Administration (NASA) Distributed Active Archive Center (DAAC). The millimeter cloud radar data that was used in algorithm validation was obtained from the University of Utah ( We appreciate the helpful suggestions from the three anonymous reviewers. This research was funded by the NOAA Integrated Program Office (IPO) (Federal Fund: NA07EC0676).

29 28 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, Baum, B. A., R. F. Arduini, B. A. Wielicki, P. Minnis, and S-C. Tsay, 1994: Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis. J. Geophys. Res., 99, Baum, B. A., T. Uttal, M. Poellot, T. P. Ackerman, J. M. Alvarez, J. Intrieri, D. O'C. Starr, J. Titlow, V. Tovinkere, and E. Clothiaux, 1995: Satellite remote sensing of multiple cloud layers. J. Atmos. Sci., 52, 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, Baum, B. A. and J. D. Spinhirne, 2000: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS, 3, Cloud Overlap. J Geophys. Res., 105, 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, Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison, 1986: Comparison of regional clearsky albedos inferred from satellite observations and model computations. J. Clim. Appl. Meteorol., 25,

30 29 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, Ellingson, R. G., J. Ellis, and S. Fels, 1991: The intercomparison of radiation codes used in climate models: long wave results, J. Geophys. Res., 96(D5), 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, Giraud, V., J. C. Fouquart, Y. Parol, and G. Seze, 1997: Large-scale analysis of cirrus clouds from AVHRR data: Assessment of both a microphysical index and the cloud-top temperature. J. Appl. Meteor., 36, Gupta, S. K., W. L. Darnell, and A. C. Wilber, 1992: A parameterization for longwave surface radiation from satellite data: Recent improvements. J. Appl. Meteor., 31, 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 STR, 212 pp. [NTIS PB ] 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 STR, 214 pp. Heidinger, A. K., R. Frey, and M. J. Pavolonis, 2003: Relative Merits of the 1.6 and 3.75 µm channels of the AVHRR/3 for cloud detection. Canadian J. Rem. Sen., In Press.

31 30 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), Jacobowitz, H., L. L. Stowe, G. Ohring, A. K. Heidinger, K. Knapp, N. R. Nalli, 2003: The Advanced Very High Resolution Radiometer Pathfinder Atmosphere (PATMOS) Climate Dataset: A Resource for Climate Research. Bull. Amer. Meteor. Soc., 84 (6), Key, J. R. and A. Schweiger, 1998: Tools for atmospheric radiative transfer: Streamer and FluxNet. Comput. Geosci., 24, Key, J. R., P. Yang, B. A. Baum, and S. L. Nasiri, 2002: Parameterization of shortwave ice cloud optical properties for various particle habits. J. Geophys. Res., 107, (AAC). Kratz, D. P., 1995: The correlated k-distribution technique as applied to the AVHRR channels. J. Quant. Spectrosc. Radiat. Transfer, 53, Kriebel, K. T., G. Gesell, M. Kaestner, and H. Mannstein, 2003: The cloud analysis tool APOLLO: Improvements and validations. J. Rem. Sens., 24, Liang, X. Z. and W. C. Wang, 1997: Cloud overlap effects on general circulation model climate simulations. J. Geophys. Res., 102, 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, Morcrette, J. J. and C. Jakob, 2000: The response of the ECMWF Model to changes in the cloud overlap assumption. Mon. Wea. Rev., 128,

32 31 Ou, S. C., K. N. Liou, and B. A. Baum, 1996: Detection of multilayer cirrus cloud systems using AVHRR data: Verification based on FIRE II IFO composite measurements. J. Appl. Meteor., 35, Pilewskie, P., and S. Twomey, 1987: Cloud phase discrimination by reflectance measurements near 1.6 and 2.2 µm. J. Atmos. Sci., 44, Rossow, W. B., A.W. Walker, D. E. Beuschel, and M. D. Roiter, 1996: International Satellite 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, Stephens, G. L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O'Conner, W. B. Rossow, S. L. Durden, S. D. Miller, R. T. Austin, A. Benedetti, C. Mitrescu, The CloudSat Science Team, 2002: The CloudSat mission and the A-Train, Bull. Amer. Meteor. Soc., 83(12), 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, Tian, L. and J. A. Curry, 1989: Cloud Overlap Statistics. J. Geophys. Res., 94, Warren, S. G., C. J. Hahn, and J. London, 1985: Simultaneous occurrence of different cloud types. J. Climate and Appl. Meteor., 24, Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to planet earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76,

33 32 Wiscombe, W. J., 1977: The Delta-M Method: Rapid yet accurate radiative flux calculations for strongly asymmetric phase functions, J. Atmos. Sci.., 34, Wylie, D. P. and W. P. Menzel, 1999: Eight years of high cloud statistics using HIRS. J. Climate, 12,

34 33 FIGURE CAPTIONS Figure 1: Calculations of 0.65 µm reflectance and the brightness temperature difference between 11 µm and 12 µm for a single layer water cloud as a function of visible optical depth, a single layer ice cloud as a function of visible optical depth, and an ice cloud overlapping a water cloud that is shown as a function of the visible optical depth of the water cloud for seven different optical depths of the ice cloud. The optical depth of the single layer water cloud and the water cloud used in the cloud overlap simulations ranges from 1.0 to The optical depth of the single layer ice cloud ranges from 0.1 to The bold line without symbols represents the function used to determine the split window brightness temperature difference threshold used in the cloud overlap detection algorithm. A reflectance model for a water surface was used. Figure 2: The same as Figure 1, except calculations of 1.65 µm reflectance and 1.38 µm reflectance are shown. The bold lines without symbols represent the functions used to determine 1.38 µm and 1.65 µm thresholds used in the cloud overlap detection algorithm. A reflectance model for a water surface was used. Figure 3: The same as Figure 2 except a reflectance model for a grass surface is used. Figure 4: The sensitivity of the split window brightness temperature difference (SWBTD) test to the relative azimuth angle. Each contour represents where the simulated SWBTD is equal to the value given by the threshold function derived for a solar zenith angle of 30 o and a viewing zenith angle of 11 o. For each optical depth combination inside of those contours, the simulated split window brightness temperature difference is greater than the cloud overlap threshold value. For instance, for the solid curve, the SWBTD test is passed for any optical depth combination inside the shaded region. Figure 5: The same as Figure 4, except the sensitivity of the SWBTD test is examined with

35 34 respect to both the effective particle size of the water cloud and the ice cloud. Figure 6: The sensitivity of the near-infrared reflectance (NIRR) test is examined with respect to the effective particle size of both the water cloud and the ice cloud. Each contour represents where the simulated 1.65 µm reflectance is equal to the value given by the threshold function derived for a scattering angle between 30 o and 40 o when the 1.38 µm reflectance is also greater than the threshold value of (2.5%). For each optical depth combination inside of each of those contours, both the 1.38 µm reflectance and the 1.65 µm reflectance are greater than the threshold values. For instance, for the solid curve, the NIRR test is passed for any optical depth combination inside the shaded region. The results shown are for a scattering angle 31.4 o and a water surface. Figure 7: Shown is the difference between the simulated split window brightness temperature difference (SWBTD) and the threshold values contoured as a function of the actual temperature difference between the cloud top of the water cloud and the cloud top of the ice cloud and the visible optical depth of the ice cloud. Positive values indicate that the SWBTD test was passed and negative values indicate that it was failed. The results shown are for a constant water cloud optical depth of 20.0 and a solar zenith angle of 30 o, a viewing zenith angle of 11 o, and a relative azimuth angle of 80 o. Figure 8: The same as Figure 6, except the sensitivity of the NIRR test is examined with respect to the vertical location of both the water cloud and the ice cloud. Figure 9: The same as Figures 6 and 8, except the sensitivity of the NIRR test is examined with respect to atmospheric profile. Figure 10: Terra-MODIS images for July 2, 2002 at 1940Z: (A) 1.65 µm reflectance, (B) 1.38 µm reflectance, (C) 0.65 µm reflectance, (D) 11 µm brightness temperature (K), (E) AVHRR

36 35 algorithm cloud overlap results, and (F) VIIRS algorithm cloud overlap results. In (E) and (F), black areas indicate where cloud overlap is detected; elsewhere, 0.65 µm reflectance is shown. Figure 11: Same as Figure 10, except for November 23, 2000 at 1730Z. Figure 12: The same as Figures 10 and 11, except for April 21, 2002 at 1705Z. Figure 13: The distribution of the fraction (given as a percent) of cloudy Terra-MODIS pixels within 15 km of the millimeter cloud radar instrument at either the ARM Southern Great Plains Site, Tropical West Pacific Site in Manus, or the Tropical West Pacific Site in Nauru that were flagged as cloud overlap using the AVHRR algorithm when the cloud radar data indicated only a single layer cloud was present within 15 minutes of the Terra overpass time (solid line). Also given is the distribution for situations when multiple cloud layers were present for less than 50% (but greater than 0%) (dashed line) and at least 50% (dashed-dot line) of the 30 minute time interval centered on the Terra overpass time. The number of cases (N), the mean, the standard deviation (Sdev), and the percent of cases for which no cloud overlap was detected (Percent Zero) are also displayed for each classification. Figure 14: The same Figure 13, except for the VIIRS algorithm.

37 36 Figure 1: Calculations of 0.65 µm reflectance and the brightness temperature difference between 11 µm and 12 µm for a single layer water cloud as a function of visible optical depth, a single layer ice cloud as a function of visible optical depth, and an ice cloud overlapping a water cloud that is shown as a function of the visible optical depth of the water cloud for seven different optical depths of the ice cloud. The optical depth of the single layer water cloud and the water cloud used in the cloud overlap simulations ranges from 1.0 to The optical depth of the single layer ice cloud ranges from 0.1 to The bold line without symbols represents the function used to determine the split window brightness temperature difference threshold used in the cloud overlap detection algorithm. A reflectance model for a water surface was used.

38 37 Figure 2: The same as Figure 1, except calculations of 1.65 µm reflectance and 1.38 µm reflectance are shown. The bold lines without symbols represent the functions used to determine 1.38 µm and 1.65 µm thresholds used in the cloud overlap detection algorithm. A reflectance model for a water surface was used.

39 38 Figure 3: The same as Figure 2 except a reflectance model for a grass surface is used.

40 39 Figure 4: The sensitivity of the split window brightness temperature difference (SWBTD) test to the relative azimuth angle. Each contour represents where the simulated SWBTD is equal to the value given by the threshold function derived for a solar zenith angle of 30 o and a viewing zenith angle of 11 o. For each optical depth combination inside of those contours, the simulated split window brightness temperature difference is greater than the cloud overlap threshold value. For instance, for the solid curve, the SWBTD test is passed for any optical depth combination inside the shaded region.

41 Figure 5: The same as Figure 4, except the sensitivity of the SWBTD test is examined with respect to both the effective particle size of the water cloud and the ice cloud. 40

42 41 Figure 6: The sensitivity of the near-infrared reflectance (NIRR) test is examined with respect to the effective particle size of both the water cloud and the ice cloud. Each contour represents where the simulated 1.65 µm reflectance is equal to the value given by the threshold function derived for a scattering angle between 30 o and 40 o when the 1.38 µm reflectance is also greater than the threshold value of (2.5%). For each optical depth combination inside of each of those contours, both the 1.38 µm reflectance and the 1.65 µm reflectance are greater than the threshold values. For instance, for the solid curve, the NIRR test is passed for any optical depth combination inside the shaded region. The results shown are for a scattering angle 31.4 o and a water surface.

43 42 Figure 7: Shown is the difference between the simulated split window brightness temperature difference (SWBTD) and the threshold values contoured as a function of the actual temperature difference between the cloud top of the water cloud and the cloud top of the ice cloud and the visible optical depth of the ice cloud. Positive values indicate that the SWBTD test was passed and negative values indicate that it was failed. The results shown are for a constant water cloud optical depth of 20.0 and a solar zenith angle of 30 o, a viewing zenith angle of 11 o, and a relative azimuth angle of 80 o.

44 43 Figure 8: The same as Figure 6, except the sensitivity of the NIRR test is examined with respect to the vertical location of both the water cloud and the ice cloud.

45 Figure 9: The same as Figures 6 and 8, except the sensitivity of the NIRR test is examined with respect to atmospheric profile. 44

46 Figure 10: Terra-MODIS images for July 2, 2002 at 1940Z: (A) 1.65 µm reflectance, (B) 1.38 µm reflectance, (C) 0.65 µm reflectance, (D) 11 µm brightness temperature (K), (E) AVHRR algorithm cloud overlap results, and (F) VIIRS algorithm cloud overlap results. In (E) and (F), black areas indicate where cloud overlap is detected; elsewhere, 0.65 µm reflectance is shown. 45

47 Figure 11: Same as Figure 10, except for November 23, 2000 at 1730Z. 46

48 Figure 12: The same as Figures 10 and 11, except for April 21, 2002 at 1705Z. 47