Improving the Quality of Extreme Precipitation Estimates Using Satellite Passive Microwave Rainfall Retrievals

Satellite microwave rainfall retrievals currently base their land algorithms on an observed mean relationship between high frequency brightness temperature depression and rainfall rate. Sensitivity of the retrieval to changes in the amount of ice relative to rainfall in the cloud results in regional biases of rainfall estimates. To address these biases we investigate how the environmental conditions in the storm environment can be linked to this ice vs. rainfall relationship. Variables such as CAPE, wind shear, and vertical humidity profiles are found to be capable of predicting this ratio and removing up to 30% of the rainfall biases over regions of Amazon and central-west Africa. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel, characterized by strong Tb depressions and amounts of ice aloft significantly above the globally observed average value. As a consequence, microwave retrieval algorithms misinterpret these systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. These regional differences correspond well with a map of TRMM radar to radiometer biases of rainfall as well as with differences seen between mean dBZ profiles of raining systems in the two regions. Deeper understanding of the influence of environmental conditions on defining this ice to rain ratio provides a foundation for addressing biases seen in extreme precipitation events globally. Mapping global spatio-temporal ice-scattering to rainfall rate relationship will improve satellite microwave rainfall retrievals in GPM era and our understanding of cloud microphysics.