Improving Assimilated Global Datasets Using TMI Rainfall and Columnar Moisture Observations

Abstract

A global analysis that optimally combines observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. In this study it is demonstrated that assimilating precipitation and total precipitable water (TPW) derived from the Tropical Rainfall Measuring Mission Microwave Imager (TMI) can significantly improve the quality of global analysis. It is shown that assimilating the 6-h averaged TMI rainfall and TPW retrievals improves not only the hydrological cycle, but also key climate parameters such as clouds, radiation, and the large-scale circulation produced by the Goddard Earth Observing System (GEOS) data assimilation system (DAS). Notably, assimilating TMI rain rates improves clouds and radiation in areas of active convection, as well as the latent heating distribution and the large-scale motion field in the Tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Assimilating these data also improves the instantaneous wind and temperature fields in the analysis, leading to better short-range forecasts in the Tropics. Ensemble forecasts initialized with analyses incorporating TMI rain rates and TPW yield smaller biases in tropical precipitation forecasts beyond 1 day, better 500-hPa geopotential height forecasts up to 5 days, and better 200-hPa divergent winds up to 2 days. These results demonstrate the potential of using high quality spaceborne rainfall and moisture observations to improve the quality of assimilated global data for climate analysis and weather forecasting applications.