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- W4366202922 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Single layer nonprecipitating warm clouds are integral to Earth’s climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud profiling capabilities. However, a significant fraction of clouds are missed by CloudSat, because they are either too thin or too close to the earth’s surface. We find that the CloudSat 2B-CWC-RVOD product misses about 73 % of nonprecipitating liquid cloudy pixels, and about 63 % of total nonprecipitating liquid cloud water content, compared to coincident MODIS observations. Those percentages increase to 84 % and 69 %, respectively, if MODIS “partly cloudy” pixels are included. We develop a method, based on adiabatic parcel theory but modified to account for the fact that observed clouds are often subadiabatic, to estimate profiles of cloud liquid water content based on MODIS observations of cloud top effective radius and cloud optical depth combined with CALIPSO observations of cloud top height. We find that, for cloudy pixels that are detected by CloudSat, the resulting subadiabatic profiles of cloud water are similar to what is retrieved from CloudSat. For cloudy pixels that are not detected by CloudSat, the subadiabatic profiles can be used to supplement the CloudSat profiles, recovering much of the missing cloud water and generating realistic-looking merged profiles of cloud water. Adding this missing cloud water to the CWC-RVOD product increases the mean cloud liquid water path by 228 % for single layer nonprecipitating warm clouds. This method will be included in a subsequent reprocessing of the 2B-CWC-RVOD algorithm." @default.
- W4366202922 created "2023-04-19" @default.
- W4366202922 date "2023-04-18" @default.
- W4366202922 modified "2023-10-14" @default.
- W4366202922 title "Comment on amt-2023-49" @default.
- W4366202922 doi "https://doi.org/10.5194/amt-2023-49-rc1" @default.
- W4366202922 hasPublicationYear "2023" @default.
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