The NCAR 4DREKF ensemble data assimilation and forecasting system

A Four-Dimensional Relaxation Ensemble Kalman Filter (4DREKF) modeling system for mesoscale analysis and forecasting is being developed at NCAR. 4DREKF is built upon the multi-model (MM5 and WRF), multi-approach (perturbations), and multiscale (nested-grid) E-RTFDDA (Ensemble Real-Time Four-Dimensional Data Assimilation and forecasting system) that was developed jointly by NCAR and ATEC (Army Test and Evaluation Command). E-RTFDDA has been deployed for operational support at Army test ranges since August 2007, and for wind energy prediction for Xcel Energy since May 2010. 4DREKF is a next-generation capability of this mesoscale ensemble forecasting system, which is formulated to integrate and add value to the recent advances in ensemble Kalman Filter data assimilation. In a nutshell, 4DREKF is implemented by replacing the spatial weight functions in standard Newtonian-relaxation station-nudging formulations with the Kalman gains, computed with a local ensemble transform Kalman Filter (LETKF) approach using the E-RTFDDA short forecasts as prior. This approach retains and also combines the advantages of both Newtonian-relaxation and Ensemble Kalman Filter data assimilation. It eliminates the ad-hoc specification of spatial weight functions in present station-nudging formulations, and removes the limitation of typical “nudging” schemes that they cannot assimilate non-standard observations, such as Doppler radar radial winds and reflectivity. In contrast, the 4DREKF allows the WRF model to ingest the EnKF analysis increments dynamically in a time-synchronization mode. In this presentation, we will briefly describe the 4DREKF scheme, including the theoretical aspects, the key technical components, and the implementation challenges. Results from test runs with a synoptic weather case and a local-scale circulation case will illustrate the advantages of the scheme.