Improving assimilated global datasets using TMI rainfall and columnar moisture observations

A global analysis that optimally combines observations from diverse sources with physical models of atmospheric and land processes can provide a compr ehensive description of the climate systems. Currently, such data products contain significant errors in primary hydrological fields such as precipita tion and evaporation, especially in the Tropics. In this study it is demons trated that assimilating precipitation and total precipitable water (TPW) d erived 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, assimilati ng TMI rain rates improves clouds and radiation in areas of active convecti on, as well as the latent heating distribution and the large-scale motion f ield in the Tropics, while assimilating TMI TPW retrievals leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Assimi lating these data also improves the instantaneous wind and temperature fiel ds 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 d ay, better 500-hPa geopotential height forecasts up to 5 days, and better 2 00-hPa divergent winds up to 2 days. These results demonstrate the potentia l of using high quality spaceborne rainfall and moisture observations to im prove the quality of assimilated global data for climate analysis and weath er forecasting applications.