Publications

Schulte, RM; Lebsock, MD; Haynes, JM (2023). What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations. ATMOSPHERIC MEASUREMENT TECHNIQUES, 16(14), 3531-3546.

Abstract
Single-layer nonprecipitating warm clouds are integral toEarth's climate, and accurate estimates of cloud liquid water content forthese clouds are critical for constraining cloud models and understandingclimate feedbacks. As the only cloud-sensitive radar currently in space,CloudSat provides very important cloud-profiling capabilities. However, asignificant fraction of clouds is missed by CloudSat because they areeither too thin or too close to the Earth's surface. We find that theCloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquidcloudy pixels and about 63 % of total nonprecipitating liquid cloudwater content compared to coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Those percentagesincrease to 84 % and 69 %, respectively, if MODIS partly cloudypixels are included. We develop a method, based on adiabatic parcel theorybut modified to account for the fact that observed clouds are oftensubadiabatic, to estimate profiles of cloud liquid water content based onMODIS observations of cloud-top effective radius and cloud optical depthcombined with lidar observations of cloud-top height. We find that, forcloudy pixels that are detected by CloudSat, the resulting subadiabaticprofiles of cloud water are similar to what is retrieved from CloudSat. Forcloudy pixels that are not detected by CloudSat, the subadiabatic profilescan be used to supplement the CloudSat profiles, recovering much of themissing cloud water and generating realistic-looking merged profiles ofcloud water. Adding this missing cloud water to the CWC-RVOD productincreases the mean cloud liquid water path by 228 % for single-layernonprecipitating warm clouds. This method will be included in a subsequentreprocessing of the 2B-CWC-RVOD algorithm.

DOI:
10.5194/amt-16-3531-2023

ISSN:
1867-8548