Snowfall estimation from space-borne active and passive microwave observations

In this study, an algorithm to estimate snowfall from passive and active microwave observations is formulated and analyzed using both simulated and real observations. A high resolution cloud resolving model (CRM) is used to simulate a snowfall event and space-borne radar and radiometer observations similar to those of the future Global Precipitation Mission (GPM) are synthesized from the CRM data. Then a combined radar- radiometer similar to that of Grecu et al. (2004) is applied to the synthetic data. It is found that in spite of dual-frequency radar and millimeter-wave radiometer observations, snow retrievals from GPM-like observations are subject to various uncertainties. Simple parameterizations are devised to minimize these uncertainties. The combined radar-radiometer, modified to account for differences between the instruments deployed in Wakasa Bay Experiment and the GPM instruments, is applied to real data from the Wakasa Bay Experiment. Results show the algorithm's feasibility.