Global model-based monthly mean rainfall climatology

Abstract

Nowcasting of daily rainfall using physical initialization provides a dynamical and thermodynamical structure consistent with respect to the imposed 'observed' rainfall. This procedure provides a very high skill for the modeled rain as compared to the observed rainfall totals. This same procedure is extended over periods of months to illustrate the very high skill for recovering the 'observed rain' from the model by using physical initialization within a month long data assimilation. This procedure also provides details of the temperature, wind, humidity and surface pressure field that are consistent with the month mean rainfall field. Such data sets are useful for the validation of global climate models. In this note, we present the impact of physical initialization on rainfall climatology. We compare the Florida State University (FSU) rainfall climatology with the European Centre for Medium Range Weather Forecast (ECMWF) and the National Center for Environmental Prediction (NCEP) reanalysis.