NCAR ensemble RTFDDA: real-time operational forecasting applications and new data assimilation developments

RTFDDA (Real-time Four-Dimensional Data Assimilation and forecasting system), jointly developed by NCAR and ATEC (Army Test and Evaluation Command), was originally aiming at support of the Army routine test and evaluation activities at the Army ranges. Since the emergence of the first MM5 based RTFDDA system deployed at Dugway Proving Ground, Utah in late 2000, the model system has been deployed for real-time operational support of broad weather-critical applications. Continue improvements, transitions from MM5 to WRF and from deterministic to ensemble modeling, and application-specific refinements have been carried out in the last 10 years. In this presentation, we first provide an overview of the major currently-running real-time operational RTFDDA systems, including the nested-grid meso-gamma scale RTFDDA systems for the seven Army test ranges across different weather-climate regions in contiguous United States and Alaska, a CONUS-scale 4-KM RTFDDA system for AirDat LLC., that assimilates full TAMDAR observations, a high-resolution RTFDDA system for wind energy forecasting covering Xcel Energy wind farms, and an explicit forecasting system over the Eastern Mediterranean regions for the Government of Israel. Among these four applications, a 30-member ensemble RTFDDA system has been deployed and has been operating for the Dugway Proving Ground since August 2007 and for probabilistic wind energy forecasting for Xcel Energy since June 2010. Following the introduction of the operational forecasting systems, we describe the on-going key new developments that span from incremental improvements (e.g. observation data processing and QC, data weighting function and model physics optimization, etc.), to the major upgrades (e.g. RTFDDA-3DVAR hybrid for assimilation of radar data and satellite data and integration of EnKF into E-RTFDDA,), and to the development of the next-generation four dimensional relaxation ensemble Kalman filter (4DREKF) data assimilation and probabilistic prediction capabilities.