What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations

• Published in: Atmospheric measurement techniques 2023-07, Vol.16 (14), p.3531-3546
• Main Authors: Schulte, Richard M, Lebsock, Matthew D, Haynes, John M
• Format: Article
• Language: English
• Subjects: Adiabatic; Algorithms; Climate; Climate feedback; Climate feedbacks; Climate models; Cloud models; Cloud optical depth; Cloud water; Clouds; Data processing; Earth surface; Estimates; Lidar; Lidar observations; Liquid water content; MODIS; Moisture content; Optical analysis; Optical thickness; Pixels; Precipitation; Profiles; Radar; Radiation; Remote sensing; Reprocessing; Satellites; Spectroradiometers; Water; Water content; Water depth
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-16-3531-2023

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 is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquid cloudy pixels and about 63 % of total nonprecipitating liquid cloud water content compared to coincident Moderate Resolution Imaging Spectroradiometer (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 lidar 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.