Application of cloud vertical structure from CloudSat to investigate MODIS-derived cloud properties of cirriform, anvil, and deep convective clouds

JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 4689 4699, doi:10.1002/jgrd.50306, 2013 Application of cloud vertical structure from CloudSat to investigate MODIS-derived cloud properties of cirriform, anvil, and deep convective clouds Alisa H. Young, 1 John J. Bates, 1 and Judith A. Curry 2 Received 23 August 2012; revised 26 February 2013; accepted 27 February 2013; published 29 May 2013. [1] CloudSat cloud vertical structure is combined with the CALIPSO Lidar and Collection-5 Level 2 cloud data from Aqua s Moderate Resolution Imaging Spectroradiometer (MODIS) to investigate the mean properties of high/cirriform, anvil, and deep convective (DC) clouds. Cloud properties are sampled over 30 S 30 N for 1 year and compared to existing results of Collection-4 Aqua MODIS high-level cloud observations where cloud types were categorized using the International Satellite Cloud Climatology Project (ISCCP) cloud classification scheme. Results show high/cirriform sampled in this study have high biases in cloud top pressure and temperature due to CloudSat s sensitivity to thin high clouds. Mean cloud properties of DC show reasonable agreement with existing DC results notwithstanding mean cloud optical thickness which is ~23% higher due to the exclusion of thick cirrus and anvil clouds. Anvil cloud properties are a mix between high/cirriform and DC according to ISCCP cloud optical thickness thresholds whereby ~80% are associated with high/cirriform and the other 20% are associated with DC. The variability of cloud effective particle radii was also evaluated using DC with 5 dbz echoes at and above 10 km. No evidence of larger cloud effective particle radii are given despite considering higher reaching echoes. Using ISCCP cloud optical thickness thresholds, ~25% of DC would be classified as cirrostratus clouds. These results provide a basis to evaluate the uncertainty of the ISCCP cloud classification scheme and MODIS-derived cloud properties using active satellite observations. Citation: Young, A. H., J. J. Bates, and J. A. Curry (2013), Application of cloud vertical structure from CloudSat to investigate MODIS-derived cloud properties of cirriform, anvil, and deep convective clouds, J. Geophys. Res. Atmos., 118, 4689 4699, doi:10.1002/jgrd.50306. 1. Introduction [2] Cloud radiative forcing depends on the extent of clouds and their optical and microphysical properties [Liu and Curry, 1999]. Improved predictions of global climate change from general circulation models (GCMs) sensitive to clouds therefore require an enhanced understanding of and appropriate parameterizations for different kinds of cloud systems. Among them, tropical deep convection (DC) remains of great importance. This is due to radiative-convective interactions and the critical role DC plays in the distribution of rainfall and the transport of moisture and heat from the planetary boundary layer to the free troposphere. 1 NOAA National Climatic Data Center, Asheville, North Carolina, USA. 2 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA. Corresponding author: A. H. Young, NOAA National Climatic Data Center, 151 Patton Avenue, Asheville, NC 28801, USA. (alisa.young@noaa.gov) 2013. American Geophysical Union. All Rights Reserved. 2169-897X/13/10.1002/jgrd.50306 [3] Yet it is also known that anvil clouds comprise the majority of DC cloud area exposed to the earth s surface and to space [Houze and Betts, 1981; Machado and Rossow, 1993; Cetrone and Houze, 2009; Yuan and Houze, 2010]. While anvil clouds and more vertically developed clouds including deep convective cores/turrets [e.g., May and Rajopadhyaya, 1999; Lin et al., 2006; Luo et al., 2011] are both constituents of DC, these two cloud fractions have distinguishing cloud properties and dynamical regimes. Using satellite, in situ, and ground-based observations, it has been demonstrated that DC cores are associated with the cloud s most vigorous vertical extent and heaviest precipitation. Alternatively, anvil clouds are unsupported by strong vertical uplift and have lighter precipitation [Yuter and Houze, 1995]. Other key differences are related to their evolution [Rickenbach et al., 2008], lifetimes [Machado and Rossow, 1993], and radiative heating profiles [Jensen and Del Genio, 2003]. Such differences are an impetus to consider DC and anvil clouds separately. Under conditions when these two cloud types are not parsed, studies intending to capture properties of DC may be biased towards the properties of anvil clouds since anvil clouds represent a larger extent of the DC cloud system. 4689

[4] Thus, the unique purpose of this paper is to use cloud vertical structure (CVS) from CloudSat to more accurately sample anvil and other high-level clouds including high/ cirriform and deep convection. Using CVS, the properties of high-level clouds are evaluated, and the advantages and disadvantages of using CVS for such investigations are explored. This work complements studies such as Hong et al., [2007] who assessed the properties of tropical cirriform and DC clouds using 3 years of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites. Without using CVS, they report that the mean optical thickness of DC is 38.3 and show slight differences of ~0.3 mm in the effective mean radii of optically thin cirriform and DC. In another study, Yuan and Li [2010] evaluated the general macrophysical and microphysical properties of DC from MODIS and address the impacts of anvil clouds by restricting conditions for cloud optical thickness and cloud top temperature. Their approach most likely reduces the extent to which anvil clouds impact the general properties of DC; however, this impact was not statistically quantified, and like the study by Hong et al. [2007], Yuan and Li [2010] rely solely on passive satellite observations. Although larger mean optical thicknesses are demonstrated by Yuan and Li [2010], they also show that ice crystal effective radii are comparable to the 3-year mean value given by Hong et al. [2007]. However, evidence of larger precipitation size particles at altitudes upward of 10 km have been documented in the analysis of DC presented in other studies [e.g., Alcala and Dessler, 2002; Liu and Zipser, 2005]. [5] It is possible that the evaluation of cloud properties given by Yuan and Li [2010] and Hong et al. [2007] is somewhat pre-conditioned since both studies rely on passive satellite observations and largely identify DC using criteria given by (and/or inferred from) the International Satellite Cloud Climatology Project (ISCCP) cloud classification scheme [Rossow and Schiffer, 1999]. In the ISCCP scheme, thresholds for cloud optical thickness (t) and cloud top pressure are used to classify clouds as one of nine types including cumulus (Cu), stratocumulus (Sc), stratus (St), altocumulus (Ac), altostratus (As), nimbostratus (Ns), cirrus (Ci), cirrostratus (Cs), and DC. In this context, the DC category includes all clouds with cloud top pressure 440 hpa and t 23. DC clouds characterized in this way may show a statistical bias in their properties due to clouds that meet specifications at the lower bound of the ISCCP DC cloud criteria but do not physically or morphologically correspond to DC. [6] While the ISCCP scheme has remarkably streamlined the capability to recognize different cloud types, this scheme relies on passive satellite observations with limited capability to observe cloud vertical structure (CVS). Although cloud optical thickness and cloud top pressure, temperature, and height are useful in determining cloud types, it is useful to group cloud classes using an approach that is less dictated by the ISCCP cloud classification scheme [e.g., Williams et al., 1995; Duchon and O Malley, 1999] and more dependent upon CVS. Analyses of this type may determine if the CVS-based approach to aggregating clouds produces differences in the mean optical and microphysical properties when compared to studies that aggregate clouds according to the more widely used ISCCP approach. In so doing, such work also creates a pathway to evaluate the extent of overlap among all ISCCP cloud classes according to ISCCP cloud classification thresholds and lays the groundwork to assess the uncertainty of the ISCCP cloud classification scheme. [7] Given these details, this study uses 1 year of observations from CloudSat Cloud Profiling Radar (CPR) to sample tropical high-level clouds according to CVS as determined by the CloudSat cloud mask. Data are then combined with measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO) Lidar and Aqua MODIS to evaluate mean macrophysical, optical, and microphysical properties. While the unique properties of anvil clouds are specifically addressed, the study also evaluates the properties of high-level clouds including cirriform and deep convection and compares these results with the earlier work of Hong et al. [2007]. 2. Materials and Methods [8] Anvil clouds are evaluated with high/cirriform (Hi/Ci) and deep convective clouds for the year 2007 and over the spatial domain between 30 Nto30 S. The main data set used in this study is the 2B-CLDCLASS product which is derived using observations from the CloudSat CPR. Specifications of the CPR are given by Stephens et al. [2008] and include a sensitivity of 29 dbz, a vertical resolution of ~250 m, and along-track and across-track dimensions of 2.5 km and 1.4 km, respectively. Incorporation of CPR data into the algorithm for the 2B-CLDLCASS product relies on two main steps. As described by Sassen and Wang [2008], the first step involves a cloud clustering analysis whereby the CPR cloud mask is used to detect cloudy fractions or range gates of each CPR profile, cloud layers, and cloud clusters which are defined as aggregates of connected cloud layers. In the second step of the algorithm, cloud clusters are grouped as specific cloud types. Cloud properties including cloud height, maximum reflectivity (dbz), temperature, and the presence (or absence) of precipitation apparently reaching the surface are inputs to the product s rule based classification scheme. Clouds are then passed to high, middle, or low classifiers according to their mean cloud height and temperature, cloud base and top height variability, reflectivity, and spatial variability. Following this approach, eight different cloud types (e.g., Hi/Ci, Ac, As, Sc, St, Ns, Cu, and DC) are classified. [9] Since anvil clouds are not a categorical cloud type provided in this scheme, refer to Figure 1. In this image, the deep convective core and anvil cloud/canopy are demonstrated with several vertical range gates (not to scale) associated with the 250 m vertical resolution of the CPR. Numbers given in each range gate identify how various parts of the DC cloud are classified by the 2B-CLDCLASS product. Using this illustration as an example, anvil clouds are sampled when Hi/Ci clouds are connected to (within 33 profiles) a vertical DC cloud cluster that extends from near the surface to more than ~7.5 km, and the region below the Hi/Ci cloud is cloud free or only partially filled ( 15% of the total column) by single layer, low-level clouds. Using these criteria, the anvil cloud, may contain cloud types other than Hi/Ci in the thicker regions of the anvil cloud. As demonstrated in Figure 1, anvil cloud bands have in some cases contained altostratus clouds (identified in Figure 1 as cloud class 2). However, 4690

1 High/Cirriform 2 Altostratus 8 Deep Convection 0 No Cloud Figure 1. Illustration of deep convective cloud according to 2B-Geoprof and 2B-CldClass products. this specific cloud type is not directly used to identify anvil clouds in this approach. [10] As previously described, the two other cloud types of interest in this study are categorically classified by the 2B-CLDCLASS product. These two cloud types include Hi/Ci and DC clouds that are respectively defined as cloud classes 1 and 8. Observations of these cloud types are only sampled when present in at least ~90% of the cloud column, defined here as the total number of cloudy range gates in each vertical profile. In addition, samples of DC clouds must extend from near the surface to at least ~7.5 km, and samples of Hi/Ci clouds must have cloud base heights of at least 7.5 km. Evidence of cloud top and base height is gathered from the CloudSat 2B-GEOPROF-Lidar product which combines CloudSat CPR and CALIPSO Lidar observations to provide cloud base and top heights for up to five different cloud layers in any one atmospheric column [Mace et al., 2009]. [11] The step-by-step approach to sample and separate Hi/Ci, anvil, and DC clouds is given in the flowchart provided in Figure 2. After samples are obtained for each of the three cloud groups, the CloudSat 2B-GEOPROF product is used to gather respective radar reflectivity profiles, and all observations are recorded with date, time, geolocation, and radar-lidar cloud top and base heights. [12] Once these data are gathered, differences in the viewing geometries between Aqua MODIS and CloudSat are parallax corrected following Wang et al. [2011]. The microphysical and optical properties of Hi/Ci, anvil, and DC clouds are gathered from the Moderate Resolution Infrared Sounder (MODIS) onboard the Aqua satellite, using wide swath collocated CloudSat/Aqua MODIS MAC03S1 and MAC06S1 products that respectively contain full resolution geolocation and Level 2 cloud data [Platnick et al., 2003]. Cloud properties sampled from the MAC06S1 product include cloud (ice) effective particle radius, cloud top pressure (CTP), cloud top temperature (CTT), cloud brightness temperature (CBT) at 11 mm, effective emissivity, and cloud optical thickness (COT). Each parameter is provided at a 5-km resolution, which is the highest resolution available from the MAC06S1 for all cloud variables evaluated. Cloud top properties including pressure, temperature, and effective emissivity are generated using the CO 2 cloud slicing algorithm that corrects for possible cloud semitransparency. This method takes advantage of the partial absorption in Aqua MODIS infrared bands within the 15-mm CO 2 absorption region [Menzel et al. 2008; Platnick et al. 2003] and is particularly useful in retrieving the heightdependent characteristics of thin high clouds. The optical thickness and effective particle radius are derived from the MODIS water-absorbing bands at central wavelengths of 1.6, 2.1, and 3.7 mm in conjunction with the non-absorbing bands at central wavelengths of 0.65, 0.86, and 1.24 mm. Although cloud fraction is an important cloud property, it has been described as being ill-defined since it heavily depends on the spatial resolution of the sensor. Given the very narrow fields of view associated with CloudSat and the CALIPSO Lidar that do not observe large scenes, this property is not evaluated in this study. [13] Aqua MODIS results use Collection 5 observations which have improved ice libraries and treatments of ice clouds compared to Collection 4 observations used in the comparison study by Hong et al. [2007]. As reported by 4691

CloudSat 2B-CLDCLASS No If column contains no other clouds Find cloud profiles with Cirriform (1) and DC (8) Hi/Ci Evaluate if horizontal cloud band is connected to vertical column of DC using ±33 CloudSat Profiles from 2B-CLDCLASS product Yes Sample observation as Cirriform Cloud DC If at least 90% of cloud column is classified as DC Yes Yes Yes Evaluate segment of cloud column below Cirriform cloud connected to DC If segment is cloud free or partially filled ( 15% of the total column) by single layer low-level clouds Sample observation as AC Sample observation as DC Gather reflectivity profiles from CloudSat 2B-Geoprof Product Sample L2 Cloud Optical and Microphysical Properties from Aqua MODIS (MAC03S1 and MAC06S1) Add parallax correction for differences in viewing geometries between CloudSat and Aqua MODIS [Wang et al., 2011] Figure 2. Algorithm flowchart for sampling high/cirriform, anvil clouds, and deep convection (DC). King et al. [2006], changes between Collections 4 and 5 data may lead to slightly smaller estimates of effective radii and ice COT that may be slightly larger. However, these differences are not expected to produce drastic impacts on mean cloud properties for Collection 5 observations used here. It is also important to note that Collection 5 will soon be replaced by Collection 6 (Baum et al., 2012) wherein improved depiction of high thin clouds was demonstrated. 3. Results 3.1. CloudSat Observations of CVS and MODIS Level 2 Cloud Properties [14] The sampling technique described in section 2 is demonstrated in Figure 3 using images of CloudSat reflectivity (dbz) to highlight CVS and corresponding Aqua MODIS Level 2 cloud properties sampled at a 5-km resolution for four cases of DC and anvil clouds with details given in Table 1. Specific cloud properties evaluated include COT, cloud (ice) effective particle radius (CEPR), CBT, and CTT. In each case, the image of CloudSat reflectivity is delineated to highlight areas where the anvil cloud was successfully parsed from the DC event and sampled. Details of the sampled profiles are also given by the distribution of filled and unfilled grid spaces that correspond to individual CPR profiles located beneath each CloudSat image with anvil regions delineated in black and DC regions delineated in blue. [15] In general, the four cases show regions where thin (e.g., Case 3) and thick (e.g., Case 2) anvil cloud profiles are retrieved in the presence and absence of single layer low-level clouds. As demonstrated in Cases 1 and 4, a limitation of the technique is due to the 33 profiles (~46 km) distance the anvil cloud sampling scheme observes away from each vertical cluster of DC. In some cases, this constraint prevents the entire leading edge of the anvil cloud from being sampled (e.g., Case 4). However, this constraint is included to avoid oversampling cirriform clouds that are tightly connected to DC events but not directly generated by DC storms, as in the case of anvil clouds. In addition, it limits the computational expense of the sampling scheme that utilizes a pixel-based approach to evaluate the spatial patterns contained in the CVS data. Other limitations of the scheme are exhibited when anvil clouds are above mid-level clouds and in atmospheric columns with more than two cloud layers as in Case 4. These constraints are intended to reduce the probability that lower (and/or higher) level clouds will impact the macrophysical, microphysical, and/or optical properties of the anvil cloud. [16] For all four cases, observations of Aqua MODIS cloud properties at the 5-km resolution show good variability with the higher resolution CloudSat product which demonstrates CVS. For the isolated DC event in Case 1, COT does not appear to directly correspond to CVS. This is likely due to the coarser resolution of the MODIS product compared to observations from CloudSat. Better agreement is displayed in cases of larger systems (see Cases 2 and 4). Vertically developed DC clouds do have COT as low as 23 (e.g., Case 1). However, the occurrence of relatively low COT is only observed during instances of small isolated systems. For larger DC events like Case 2, COT shows values greater than 60. In conditions of larger mesoscale convective complexes, several analyses of COT and CEPR show missing values. A statistically large number of similar cases with missing data could bias statistical results for COT and CEPR towards small isolated events. 4692

Figure 3. Four cases of CloudSat images from the 2B-Geoprof product that demonstrate the capability of the anvil sampling technique. The details of each delineated region are given in Table 1. Collocated observations of cloud properties from Aqua MODIS appear below each CloudSat image with primary Y axis in blue secondary in black. Figure 3. (Continued) [17] In the evaluation of CBT and CTT, Cases 1 4 show that CBT is typically warm in comparison to CTT especially for cases where COT is relatively low (i.e., <20). Although in situ studies [e.g., Lawson et al., 2010; Heymsfield et al., 2002] and satellite based analysis from the Tropical Rainfall Monitoring Mission (TRMM) Precipitation Radar indicate 4693

Table 1. Details of CloudSat Granule ID, Profile Range, Number of Sampled Profiles, and Latitude and Longitude for the Anvil Clouds Sampled From Cases 1 4 are Provided in Figure 3 Case # Granule ID Profile Range No. of Sampled Profiles StartLat StartLon EndLat EndLon R1 R2 R3 R4 Total 1 03608 17225 17325 19 10 5 13 47 12.98 158.73 12.01 158.52 2 03608 18945 19045 18 33 n/a n/a 51 3.65 158.16 4.61 154.96 3 03659 1432 1532 32 n/a n/a n/a 32 13.84 164.57 15.18 164.27 4 03659 19165 19265 32 3 2 20 57 5.78 26.06 4 26.26 that, in some cases, relatively large particles (~275 mm) do exist at the top of DC clouds [e.g., Alcala and Dessler, 2002; Liu et al., 2005], no cases of high CEPR (>70 mm) and high COT (>70) were observed. As demonstrated in Figure 3, the case studies show larger values of CEPR occur at the edge of cirrus-anvil cloud fractions rather than near the center of DC storms. Although the four cases given in Figure 3 represent a limited sample of the DC and anvil cloud volume, they do provide a general description regarding the capabilities of the anvil sampling technique and demonstrate results of the MODIS Level 2 cloud product with CloudSat CVS. [18] Further characterization of the technique is demonstrated by three Contoured Frequency by Altitude Diagrams (CFADs) provided in Figure 4. These diagrams represent the CVS of Hi/Ci, anvil, and DC clouds for which optical and microphysical properties will be described (section 3.2). According to Figure 4a, Hi/Ci cloud profiles are consistent with the results of other studies [e.g., Zhang et al., 2007]. More specifically, the Hi/Ci CFAD exhibits high frequencies of low reflectivity values (< 5 dbz) at altitudes between ~7 and 15 km. Maximum frequencies occur at reflectivities < 25 dbz and at an altitude of ~12 km. A strong distribution of low reflectivity also exists at lower levels below ~7 km. This is due to sampling conditions that constrain profiles of Hi/Ci to be practically free of any other cloud layers and types. In comparison to Hi/Ci clouds, anvil clouds have high frequencies at stronger reflectivities and at lower levels (Figure 4b). The highest frequencies are broadly present between 10 and 10 dbz and at altitudes between ~6 and 11 km. The anvil cloud CFAD compares well with Cetrone and Houze [2009] who also used CloudSat to evaluate thin and thick anvil clouds observed over West Africa, the Maritime Continent, and the Bay of Bengal. The DC CFAD in Figure 4c shows that CVS extends from near the surface to the upper levels of the troposphere. This cloud type exhibits maximum frequencies at reflectivities between 3 and 14 dbz and at altitudes from near the surface to ~6 km. The DC distribution also shows good qualitative comparison with height-dbz histograms of DC given by Zhang et al. [2007] and the CFAD of very high reaching deep convection given by Young et al. [2012]. 3.2. Mean Properties of High/Cirriform, Anvil, and DC Clouds [19] Mean cloud properties for 1 year of data (2007) are provided in Table 2 for tropical (30 Nto30 S) Hi/Ci, anvil clouds, DC, and a subcategory of tropical DC with reflectivities 5 dbz above 10 km. In addition, the mean cloud properties of Hi/Ci and DC given by Hong et al. [2007] who evaluated 3 years of MODIS Aqua and Terra a) Altitude (km) Altitude (km) Altitude (km) b) c) 30.00 21.67 13 5.00 3 11.67 20.00 CloudSat CPR Reflectivity (dbz) 30.00 21.67 13 5.00 3 11.67 20.00 CloudSat CPR Reflectivity (dbz) 30.00 21.67 13 5.00 3 11.67 20.00 CloudSat CPR Reflectivity (dbz) Figure 4. Contoured Frequency by Altitude Diagram (CFAD) of CloudSat reflectivity profiles classified as (a) high/cirriform, (b) anvil clouds, and (c) DC. observations are provided. Data for each cloud type are distinguished by collection number and cloud classification scheme (i.e., CloudSat CVS compared with ISCCP). As shown in Table 2, each cloud type exhibits distinctions in mean cloud properties including CTP, CBT, and COT. [20] More specifically, CloudSat-CVS observed that Hi/Ci clouds (CVS_HC) have a mean CTP of 471 hpa and mean radar-lidar cloud top and base heights of 1 km and 9.9 km, respectively. In general, differences between mean CTP and radar-lidar CTH for CVS_HC indicate that the radiometric cloud top detected by Aqua MODIS is well below the hydrometeor cloud top observed by CloudSat and CALIPSO. This issue has been demonstrated in case studies by Weisz et al. [2007] who show that Aqua MODIS cloud top heights have a bias of 3.0 km and 1.2 km compared with the CALIPSO Lidar and CloudSat and Holz et al. [2008] who report similar findings for CTH/CTP differences between Aqua MODIS and the CALIPSO Lidar. Altitude (km) Altitude (km) Altitude (km) 4694

Table 2. Mean Optical and Microphysical Properties of High Clouds Derived From Collection-5 MODIS L2 Cloud Data Sampled Along the CloudSat Orbital Track Over 30 S 30 N for 1 year of Observations (2007) and Existing Results of High Cloud Properties From Collection-4 Observations Given by Hong et al. [2007] for 3 years of Observations Aqua MODIS Collection-5/CVS Cloud Properties High/Cirriform Anvil DC DC w/ 5 dbz echoes 10 km Aqua MODIS Collection-4 /ISCCP Cirriform [Hong et al., 2007] DC [Hong et al., 2007] No. CloudSat Profiles 2,977,451 864,552 1,644,862 605,950 Top Temperature (K) 247.8 221.5 223.0 211.0 231 234.9 Cloud Brightness Temperature (K) 268.5 236.3 228.5 214.3 9999 9999 Top Pressure (hpa) 471.2 229.9 228.0 176.2 281.4 22 Optical Thickness 5.9 16.0 46.3 56.1 7.7 37.5 Effective Radius (mm) 25.2 27.8 26.4 26.0 26.5 26.4 Effective Emissivity 7 0.80 0.87 0.90 0.64 0.9 Radar-lidar CTH (km) 1 13.9 12.4 15.3 9999 9999 Radar-lidar CBH (km) 9.9 7.2 1.0 0.9 9999 9999 [21] The estimate of mean CTP for CVS_HC is also high compared to the mean CTP of the ISCCP based Hi/Ci (ISCCP_HC) cloud group, which has an estimated mean CTP of 282 hpa. However, CVS_HC is located at relatively high altitudes as indicated by the CFAD distribution in Figure 4a. Large differences in mean CTP for the two Hi/Ci cloud groups are likely due to the optical depth sensitivities of CloudSat CPR which is used to obtain CVS_HC compared to Aqua MODIS which is used to obtain ISCCP_HC. As reported by Haladay and Stephens [2009], CloudSat misses thin cirrus clouds with optical depths (uncorrected for multiple scattering) less than 0.3, while Ackerman et al. [2008] report that Aqua MODIS is insensitive to clouds with COT smaller than 0.4. Another factor associated with differences in CTP is given by Baum et al. [2003] who report that in cases of optically thin clouds, the CO 2 cloud slicing algorithm may not converge to an acceptable solution, and the retrieval of cloud top properties reverts to a water vapor corrected 11 mm window brightness temperature retrieval which is known to significantly overestimate CTP. In addition, differences in spatial resolution between CloudSat an Aqua MODIS products also have quantitative impacts. [22] High estimates of CTP for CVS_HC also correspond to a high (or warm) estimate of mean CTT compared to the ISCCP_HC cloud group where mean CTTs correspond to 248 K and 231 K, respectively. The mean COT and effective emissivity for CVS_HC is 5.9 and 7 compared with 7.7 and 0.64 for ISCCP_HC. On average, the comparison between CVS_HC and ISCCP_HC suggests that Hi/Ci clouds obtained with the CloudSat-CVS scheme are optically thinner and less emissive than Hi/Ci obtained with the ISCCP scheme. Another important characteristic of CVS_HC is that it has a low mean CBT of ~269 K and has a difference in mean CTT and CBT greater than 20 K. [23] Compared to CVS_HC, anvil clouds have higher mean emissivity and COT of 0.8 and 16.0 and lower mean CTP and CTT of 230 hpa and 221.5 K. These characteristics suggest that anvil clouds represent much colder and higher clouds. However, the mean radar-lidar CTH of anvil clouds and CVS_HC only differ by 0.6 km. For the case of anvil clouds, mean estimates of CTP from Aqua MODIS are also more comparable to radar-lidar cloud top height estimates provided by CloudSat/CALIPSO. Table 2 anvil cloud properties also show a lower mean CBT of 236.3 and smaller differences (~15 K) between mean CTT and CBT when evaluated against CVS_HC results. [24] In comparison to anvil clouds, Collection 5 CloudSat- CVS based DC (CVS_DC) show lower mean cloud top height and pressure of 12.4 km and 228.5 hpa but have higher mean COT, CTT, and effective emissivity with estimates corresponding to 46.3, 223 K, and 0.87. The lower mean CTH of CVS_DC at 12.4 km is likely due to garden variety /lower-level DC events compared with anvil clouds which have cloud tops that are notably capped/produced by the strong temperature inversion at the tropopause. Estimates of mean CTT and CTP for anvil and CVS_DC show comparable values, while estimates of CBT for these two cloud groups show larger differences. Differences in CBT are somewhat explained by mean effective emissivity, which differs by 0.07 for the two cloud groups. [25] Compared with Collection 4 ISCCP based DC (ISCCP_DC), CVS_DC have lower mean CTT and effective emissivity although mean COT is ~23% higher. This larger mean COT in CVS_DC is likely due to the exclusion of optically thinner high/cirriform-anvil cloud fractions that have been parsed from the CVS_DC cloud volume. The mean CTT of CVS_DC is lower (or colder) than ISCCP_DC; a difference that may also be due to cloud parsing. Although CVS_DC mean cloud top and base heights of 12.4 and 1.0 km indicate strong vertical development as demonstrated in Figure 4c cloud base heights reported for CVS_DC are likely to have a low bias due to precipitation observed by the radar. Other details of CVS_DC cloud properties show differences between CTT and CBT that are further reduced compared to CVS_HC and anvil clouds. Hanna et al. [2008] suggest that differences between CTT and CBT may be due to underestimates of deep convective clouds by CBT. Data presented in this study suggest that large differences between CBT and CTT are based on differences in effective emissivity where Hi/Ci and anvil clouds have particularly low effective emissivities compared to DC cloud groups. In response to Hanna et al. [2008], large differences between CTT and CBT could be due to anvil cloud fractions with lower effective emissivities are considered in the DC cloud group. [26] As demonstrated in Table 2, high-level clouds with progressively higher emissivities have smaller differences between CTT and CBT values. CBT and effective emissivity have a relatively high anti-correlated relationship of 0.68 4695

which generally demonstrates that decreasing values of CBT occur with increasing values of emissivity. The correlation for CBT with COT is much weaker at 0.47. On the other hand, CTT correlations with emissivity and COT are 0.012 and 0.018 demonstrating that estimates of CTT are not linearly dependent on emissivity and COT. [27] As previously documented by the results of Hi/Ci and DC clouds given by Hong et al. [2007], cloud properties given for CVS_HC, anvil, and CVS_DC continue to show rather small differences in CEPR. CVS_HC and CVS_DC have differences in CEPR of 1.2 mm compared with ISCCP_HC and ISCCP_DC which differ by 0.1 mm. It is also important to mention that in comparison to CVS_HC and CVS_DC, which have mean CEPR of 25.2 mm and 27.4 mm, anvil clouds have the highest mean CEPR of 27.8 mm. To further evaluate CEPR as observed from Aqua MODIS, the study investigates CVS_DC with reflectivities 5 dbz (CVS_5DC) at altitudes 10 km. Since CloudSat reflectivities of this magnitude are indicative of high reaching cloud and precipitation size particles, this subset of CVS_DC is specifically evaluated to determine if these unique observations reveal any significant changes in CEPR. As demonstrated in Table 1, the mean CEPR of CVS_5DC is practically unchanged from the larger CVS_DC volume and is reportedly 26.0 mm. In contrast, all other cloud properties provided show considerable differences. For example, CVS_5DC is optically thicker, higher reaching, and colder clouds compared to the CVS_DC cloud group. Distributions of High/Cirriform, Anvil, and DC Cloud Properties [28] The normalized distribution for Hi/Ci, anvil, and DC clouds for cloud properties obtained from Aqua MODIS is provided in Figure 5 to more fully demonstrate how cloud properties are distributed about their means. Although the mean CTP for CVS_HC is 471 hpa, Figure 5a shows that this mean value is based on a bimodal distribution of observations with one mode having CTP values that are comparable to Hong et al., [2007] and another mode with much larger CTP values that deviate from the Hong et al. [2007]Hi/Cimean CTP estimate. [29] Observations of anvil, CVS_DC, and CVS_5DC show similar CTP distributions that have positive skew but vary in peakedness. In Figure 5b, the distribution of CVS_HC is dominated by low COT. This is also the case for the COT distribution of anvil clouds. For CVS_DC and CVS_5DC, these results correspond with Yuan and Li [2010], having two peaks in COT where the secondary peak lies at much higher values. In their study, Yuan and Li [2010] argue that the latter peak is due to the upper limit in Figure 5. Normalized distributions of MODIS cloud properties for Hi/Ci, Anvil, DC, and DC with >5 dbz echoes above 10 km for cloud properties including cloud (a) top pressure, (b) optical thickness, (c) top temperature, (d) brightness temperature, (e) effective emissivity, and (f) effective particle radius. 4696

Table 3. Overlap of CVS_HC, Anvil, and CVS_DC Clouds According to Several COT Thresholds Utilized by the ISCCP Cloud Classification Scheme and CBT Values Often Used to Distinguish/Classify High-level Clouds a ISCCP Category Ci Cs DC Optical Thickness 0 3.6 9.4 23.0 46.3 Hi/Ci (CVS_HC) 48.5% 33.4% 13.5% 4.6% 0% Anvil 9.1% 32.2% 35.5% 15.7% 7.6% DC (CVS_DC) 0% 0% 25.4% 34.7% 39.9% CBT 300 275 250 225 210 Hi/Ci (CVS_HC) 58.5% 39.9% 16.4% 3.0% 0.3% Anvil 5.9% 18.4% 41.4% 26.3% 7.9% DC (CVS_DC5) % 13.0% 4% 31.0% 14.9% a ISCCP high-level cloud categories include Ci = Cirrus, Cs = Cirrostratus, and DC = Deep Convection. COT as defined by the MODIS product and to thick anvil clouds that cover large areas. Since the present study has carefully parsed anvil clouds from DC, anvil clouds are obviously not the cause of high COT for CVS_DC and CVS_5DC distributions. Furthermore, the distribution of COT for anvil clouds does not contain a significant contribution at high COT values so that these results do not fully support the explanation that Yuan and Li [2010] provide. [30] The distribution of CTT given in Figure 5c shows a bimodal distribution for CVS_HC while CVS_5DC shows a rather distinct distribution at low CTT similar to its CBT distribution in Figure 5d. Evaluation of effective emissivity given in Figure 5e shows that Hi/Ci clouds have a broad distribution across the spectrum of effective emissivity values while anvil, Hi/Ci, and DC clouds are negatively skewed and exhibit higher values with similar means. This is also the case for the distribution of effective particle radius given in Figure 5f, which demonstrates marked differences of CVS_HC compared to anvil, CVS_DC, and CVS_5DC. As given, CVS_HC show a broader spectrum of effective particle sizes particularly at the low end of the distribution. 3.4. Overlap Among Cloud Classes According to Cloud Optical Thickness and CBT [31] Overlap among high-level cloud classes (i.e., CVS_HC, anvil, and CVS_DC) is evaluated at various thresholds of COT and CBT. These two cloud properties are not derived from the CO 2 cloud slicing method and represent common properties used to classify clouds as demonstrated by the ISCCP cloud classification scheme [Rossow and Schiffer, 1999] and other studies [e.g., Fu et al., 1995]. [32] According to Table 3, ~95% of CVS_HC have COT <23. Although results from Table 2 show that CVS_HC generally represent optically thinner clouds with a mean COT of 5.9, approximately 5% of CVS_HC have COT large enough to be classified as DC using the ISCCP criteria. On the other hand, no CVS_HC observations have optical thicknesses greater than the mean COT of CVS_DC (46.3) given in Table 2. CVS_HC estimates of CBT show that % (0.3%) of high/cirriform have CBT 225 K (210 K). These characteristics suggest that Hi/Ci clouds are rarely found with very low cloud brightness temperatures and show that CBT can be used to successfully parse high/cirriform clouds from DC cloud fractions although a significant fraction of presumably lower-level DC clouds will also be missed at such low temperatures. [33] For anvil clouds, Table 3 also shows that ~80% have COT < 23. Thus, the majority of anvil clouds evaluated in this study would be classified as cirriform clouds while the remaining ~20% would be DC according to the ISCCP scheme. CBT and COT thresholds indicate ~8% of anvil clouds have CBT 210 K and ~6% have COT < 46.3. Approximately 75% of CVS_DC have COT 23 and the remaining fraction has COT between 9.6 and 23. CVS_DC in this range would be categorized as high/cirriform or more specifically cirrostratus using the ISCCP scheme. DC events with COT below the ISCCP DC threshold of 23 are likely associated with small isolated events that may be partially represented by Aqua MODIS pixel level cloud data as in Case 1 of Figure 1. 4. Conclusions [34] This study used CloudSat CVS to investigate the properties of high/cirriform (Hi/Ci), anvil, and DC clouds. It also evaluated if the CVS-based approach to aggregating clouds produced any differences in MODIS-derived mean optical and microphysical properties compared to Hong et al. [2007] who aggregated clouds from MODIS using the ISCCP cloud classification scheme. In so doing, the study demonstrates the advantages of using CloudSat CVS. These include the capability to (1) more accurately identify cloud types based on vertical cloud structure, (2) better understand the limitations of MODIS-derived cloud properties based on optical depth sensitivity and retrieval methods, and (3) further investigate the uncertainty of the widely used ISCCP cloud classification scheme. Since MODIS cloud properties are provided at a 5-km resolution, they do not provide a strict representation of each CloudSat footprint. This issue is most problematic in estimating cloud properties in cases of small isolated systems and non-homogeneous MODIS/ CloudSat fields of view. Thus, the study interfaces a common problem in data combination and intercomparison efforts, which are often challenged by differences in technology, horizontal and vertical resolution, and so forth. [35] In comparing with Hong et al. [2007], mean cloud properties of Hi/Ci and DC clouds show considerable differences. These differences are greatest for the two Hi/Ci cloud groups (CVS_HC and ISCCP_HC) due to CloudSat s higher sensitivity to detect Hi/Ci clouds with low effective cloud amounts compared to Aqua MODIS. Clouds fitting this description likely lead to increased errors in the CO 2 cloud slicing technique. In such cases, the MODIS cloud retrieval algorithm reverts to a water vapor corrected 11 mm window brightness temperature retrieval, used in many other techniques, that is known to have less ability to infer the CTP of optically thin cirrus compared to techniques that 4697

use infrared absorbing channels such as those in the 15 mm CO 2 band [Heidinger et al., 2010]. [36] Although high biases in mean CTT and CTP may limit the use of CVS_HC mean cloud properties presented in this study, results provided herein indicate the potential for using CloudSat and CALIPSO to better understand the extent of semi-transparency for which the CO 2 cloud slicing method is most useful [e.g., Zhang and Menzel, 2002; Holz et al., 2008]. In addition, the results may also be used to determine the capability of space-borne passive remote sensing observations to provide thorough cloud climatologies especially considering that Haladay and Stephens [2009] show that more than 30% of high/cirriform clouds have optical thicknesses that are not detectable by passive instruments such as MODIS. As a consequence, much work still remains to capture the mean optical and microphysical properties of Hi/Ci clouds detectable by instruments of varying optical depth sensitivities including, for example, CloudSat, CALIPSO, and Aqua MODIS. [37] In the evaluation of the deep convective cloud groups (CVS_DC and ISCCP_DC), mean cloud properties show reasonable agreement with the exception of CVS_DC mean COT which is 23% higher compared to ISCCP_DC. Anvil and thick cirrus with COT 23 are most likely included in the ISCCP_DC cloud volume causing it to have a lower mean COT. Such differences in DC mean cloud properties demonstrate a challenge of the ISCCP scheme and show that for high-level clouds, a COT value of 23 does not strictly identify DC clouds of characteristic vertical depth. These observations warrant improvements that could be made to produce a more-value added ISCCP cloud classification scheme. Possible improvements include an uncertainty analysis of all ISCCP cloud classes according to independent observations of vertical cloud structure, CTP, and COT to quantify the skill and uncertainty of the ISCCP cloud classification model. [38] It is also concluded that although there is overlap among CVS_DC and anvil cloud properties, anvil clouds demonstrate some distinction in COT. The complete set of mean anvil cloud properties may serve to better target observations of this specific cloud group as intended by studies such as Sherwood and Wahrlich, 1999, Tian et al. [2004], and Zhang et al. [2008]. Yet it is important to mention that categorically pinpointing anvil clouds will continue to be difficult. Although infrared thresholds associated with cloud brightness temperatures cannot distinguish between DC, thick cirrus, and anvil clouds; the breakdown of CVS_HC, anvil, and CVS_DC cloud fractions given at various CBT thresholds typify the extent to which these cloud types may statistically be found according to the given criteria. Such statistics also show that COT is a better discriminator among high level clouds than CBT. [39] Finally, it is also noted that CVS_DC with 5 dbz echoes 10 km did not show any evidence of larger size particles according to estimates of ice CEPR, although high reaching echoes are indicative of more intense storms that are able to loft larger size particles to higher altitudes. Due to the sensitivity of the TRMM Precipitation Radar to detect larger size particles, TRMM observations may prove more useful than CloudSat to further evaluate the variability of MODIS estimates of CEPR. 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