Estimation of Warm Rain using Information from the Proposed Advanced Baseline Imager (ABI): A Pre-launch Study with A-Train Satellite Data

Warm rain from low-level liquid water clouds accounts for a significant portion of total precipitation. Traditional infrared (IR) rainfall detection algorithms generally fail to detect the presence of precipitation in warm clouds because they depend on the cloud-top temperature and assume that only cold clouds of ice crystals produce rain. Comprising many state-of-art passive and active instruments, the NASA A-Train series of satellites provide comprehensive simultaneous information about warm cloud and its precipitation processes. To improve detection and estimation of warm rain for the proposed Advanced Baseline Imager (ABI) on NOAA's Geostationary Operational Environmental Satellite-Series R (GOES-R) satellites, this study uses A-Train satellites data to investigate the rain contribution from warm clouds and the potential for using cloud microphysical parameters for warm rain estimation. By analyzing one month data from the MODerate resolution Infrared Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on the Aqua satellite and the cloud profiling radar on the CloudSat satellite, it is shown that warm rain underneath low-level liquid clouds significantly contributes to global precipitation. The cloud microphysical parameters (i.e., liquid water path and droplet effective radius) are found to have potential for detecting warm rain events and estimating its amount. We will examining key parameters for estimating warm rain with the cloud microphysical parameters derived from the future GOES-R ABI.