Remote Sensing of Cloud and Precipitation of Warm Clouds by Passive and Active Sensors aboard A-train Satellite

Though warm rain from low-level liquid clouds contributes significantly to the global precipitation and water cycle, it has been missed or underestimated by satellite remote sensing techniques. IR techniques miss all warm rain because they rely on cloud top temperature. Over land, passive microwave techniques miss all warm rain because they rely on ice scattering at high frequency channel. Over ocean, as revealed in this study, passive microwave techniques underestimate warm rain by nearly 48%, and most of the underestimation happens for clouds with top height less than 3.5 km. Using NASA’s A-train satellites data, this study attempts to estimate rain rate by warm clouds by investigating the relationship between warm rain and cloud microphysical parameters. Analyzing the Aqua AMSR-E rain estimates, rain estimates from CloudSat CPR and AMSR-E, we determine the percentage of warm rain and the performance of space-borne passive microwave observation on warm rain estimation over ocean. For single-layer clouds, rain from warm clouds (top temperature higher than 0 C) contributes 28.8% of rain occurrences and 17.6% of rain amounts over global ocean. The potential of cloud microphysical parameters on warm rain estimation is explored with the MODIS estimates of cloud microphysical parameters and the coincident CloudSat CPR warm rain estimates. Among various cloud microphysical parameters under study, liquid water path calculated from the retrieval of the profile of cloud particle size determined by the algorithm of Chang and L (2005) is found to have the best potential for both detecting warm rain and estimating warm rain amounts.