Dynamical Ensemble Prediction

Background

Given the chaotic nature of the atmosphere and the imperfections of numerical weather prediction (NWP) models, probabilistic forecasts are imperative for applications. To address this need, RAL developed an Ensemble Real-Time Four-Dimensional Data Assimilation (E-RTFDDA) and forecasting system. The system is built upon WRF. The first E-RTFDDA system was deployed to support the US Army Dugway Proving Ground in 2007 and is known as E-4DWX. Since then the WRF core, data assimilation scheme, ensemble perturbation approaches, and ensemble output post-processing have been continuously improved. A second system was developed to support Xcel Energy for real-time wind energy prediction. And most recently, in working with the Chinese Electric Power Research Institute (CEPRI), the third system, a 30-member 9km-grid E-RTFDDA model that covers the China-national domain, has been developed. 

Unlike most other mesoscale ensemble systems, E-RTFDDA is a multi–model, multi–scale, and rapidly cycling data assimilation and prediction system with multiple perturbation approaches. The continuous cycling mechanism of E-RTFDDA allows the model to produce accurate nowcasts and short-term forecasts. E-RTFDDA also contains an innovative ensemble data assimilation algorithm known as four-dimensional relaxation ensemble Kalman filter (4D-REKF). It replaces the simpler Cressman-type “observation-nudging” FDDA in E-RTFDDA with a flow-dependent weighting. The research and development of E-RTFDDA is currently conducted under the sponsorship of the Army Test and Evaluation Command, Xcel Energy and China State Grid projects, whose broad objectives and progress are also discussed in this annual report.

FY2016 Accomplishments

Achievements in E-RTFDDA development include further enhancement of the four-dimensional relaxation ensemble Kalman filter (4D-REKF) algorithms, the WRF version upgrades that involve evaluating and adopting the community WRF advances, implementation of an analog-based bias correction algorithm and a quantile-regression-based probability calibration scheme for statistical processing of the Army E-4DWX system, and a deployment of an E-RTFDDA system to support the power grid operation of the State Grid Corporation of China. The SGCC E-RTFDDA system contains 30 WRF members and its domain cover the whole China region at grid intervals of 9km. The system downscales ECMWF/IFS, NCEP/GFS, Canada/GEM and JMA/JSM global model forecasts to the WRF grids in combination of WRF physics and data assimilation perturbations. (Fig. 1). The system runs four forecast cycles per day and each cycle produces 72hour forecasts. 

Figure 1: Design of the NCAR-SGCC China-national E-RTFDDA ensemble forecasting system.

FY2017 Plans

Development of E-RTFDDA will continue with efforts focused in the areas of ensemble-based 4D-REKF FDDA data assimilation, the model output statistical post-processing using the analog bias-correction and quantile regression calibration approach, including joint probability of specific variables and indices desired by end-users. The WRF SKEP scheme for dynamical ensemble perturbation and DART-EnKF for WRF initial condition perturbation strategies will be further evaluated and integrated for real-time E-RTFDDA operation. And finally, E-RTFDDA technology will be an essential element in new proposals U.S. and international sponsors.