Assimilation of precipitation-affected microwave radiances in a cloud-resolving WRF ensemble data assimilation system

In the last decade the progress in satellite precipitation estimation and the advance in precipitation assimilation techniques proved to have positive impact on the quality of atmospheric analyses and forecasts. Direct assimilation of rain-affected radiances presents new challenge to optimal utilization of satellite precipitation observations in numeric weather and climate predictions. Current operational and research methodologies are generally limited to relatively coarse resolution models and prescribed static error statistics, and commonly require tangent linear model and adjoint model for the highly non-linear cloud and precipitation physics. To address some of these challenges, a WRF ensemble data assimilation system (Goddard-WRF-EDAS) at cloud-resolving scales has been developed jointly by NASA/GSFC and Colorado State University (CSU). The system employs the Weather Research and Forecasting (WRF) model with NASA Goddard microphysics schemes, and the Maximum Likelihood Ensemble Filter (MLEF). Precipitation affected radiances are assimilated with Goddard Satellite Data Simulator Unit (SDSU) as the observation operator. In addition to the boundary forcing constructed from operational global analysis, NCEP operational data stream is also assimilated to ensure realistic representation of dynamic circulation in the regional domains. Using the ensemble assimilation approach, the forecast error-statistics is updated by ensemble forecasts, and information is extracted from precipitation observations along with other types of data to produce dynamically consistent precipitation analyses and forecasts. We present experimental results of assimilating precipitation-affected microwave radiances over land in middle latitudes. The results demonstrate the data impact to the downscaled precipitation short term forecasts and information propagation from precipitation data to dynamic fields. The error statistics of microphysical control variables and their relationship to the observable innovations in radiance space are examined. The evaluation of background error covariance, in particular the cross-covariance between microphysical and dynamical variables will also be discussed.