Empirical Hydrometeor Type Identification from GMI Brightness Temperature Measurements

Abstract

Brightness temperatures (TB) from GMI and related sensors contain information about the types and amounts of precipitation particles in a column. Approaches like GPROF or machine learning are good for providing precipitation estimates and other quantified information, but it can be difficult to understand why / how a given set of TBs traces to a given retrieval solution. Outlier TBs that are not well-represented in a training sample can lead to dubious solutions. Our goal is to use the scattering signatures at multiple frequencies to determine whether hail is present, or otherwise graupel, or otherwise snow, or otherwise liquid rain without a substantial precipitation ice component. (A hierarchical approach is employed, so a prediction of hail can include the presence of all other categories, but a prediction of snow implies a lack of graupel or hail, and a prediction of liquid rain implies a lack of any detectable precipitation ice.)