Improving global analysis and short-range forecast using rainfall and moisture observations derived from TRMM and SSM/I passive microwave sensors

As a follow-on to the Tropical Rainfall Measuring Mission (TRMM) the Nation al Aeronautics and Space Administration in the United States, the National Space Development Agency of Japan, and the European Space Agency are consid ering a satellite mission to measure the global rainfall. The plan envision s an improved TRMM-like satellite and a constellation of eight satellites c arrying passive microwave radiometers to provide global rainfall measuremen ts at 3-h intervals. The success of this concept relies on the merits of ra infall estimates derived from passive microwave radiom eters. This article offers a proof-of-concept demonstration of the benefits of using rainfall a nd total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imag er (TMI) and two Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources wi th physical models of atmospheric and land processes can provide a comprehe nsive description of the climate systems. Currently, such data analyses con tain significant errors in primary hydrological fields such as precipitatio n and evaporation, especially in the Tropics. It is shown that assimilating the 6-h-averaged TMI and SSM/I surface rain rate and TPW retrievals improv es not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper-tropospheric moisture in the analysis prod uced by the Goddard Earth Observing System Data Assimilation System, as ver ified against radiation measurements by the Clouds and the Ear-th's Radiant Energy System instrument and brightness temperature observations by the Te levision Infrared Observational Satellite Operational Vertical Sounder inst ruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the Tropics, while TPW assimilation leads to reduced moisture biases and i mproved radiative fluxes in clear-sky regions. Ensemble forecasts initializ ed with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and pr ecipitation in the Tropics. These results were obtained using a variational procedure based on a 6-h ti me integration of a column model of moist physics with prescribed dynamical and other physical tendencies. The procedure estimates moisture tendency c or rctions at observation locations by minimizing the least squares differe nces between the observed TPW and rain rates those generated by the column model over a 6-h analysis window. These tendency corrections are then appli ed during the assimilation cycle to compensate for errors arising from both initial conditions and deficiencies in model physics. Our results point to the importance of addressing deficiencies in model physics in assimilating data types such as precipitation, for which the forward model based on con vective parameterizations may have significant systematic errors. This study offers a compelling illustration of the potential of using rainf all and TPW information derived from passive microwave instruments to signi ficantly improve the quality of four-dimensional global datasets for climat e analysis and weather forecasting applications.