Mesoscale Ensemble Data Assimilation and Prediction

BACKGROUND

Figure 1: A description of E-RTFDDA framework
Figure 1: A description of E-RTFDDA framework 

Given the chaotic nature of the atmosphere and the imperfections of numerical weather prediction (NWP) models, probabilistic forecasts are imperative for mesoscale weather forecasting. In the last 10 years, 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 US Army Dugway Proving Ground in 2007, known as E-4DWX. Since then the WRF core, data assimilation scheme, ensemble perturbation approaches, and ensemble output post-processing have been continuously improved. The second system was developed in 2010 to support Xcel Energy for real-time wind energy prediction. 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. 

Technologies

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 it to produce accurate nowcasts and short-term forecasts that are highly desirable by many weather-critical applications. One of the unique advantages of the E-RTFDDA system is that it is capable of integrating and dynamically downscaling the top-ranked global model forecasts, including the forecast data from ECMWF/IFS (Europe), NCEP/GFS (USA), EC/GEM (Canada), NASA/GEOS (USA), JMA/GSM(Japan), CMA/GRAPES (China) etc., to the same mesoscale application grids, and thus effectively takes in the information and uncertainties of these global models and produce more accurate mesoscale probability forecasts. Fig. 1 presents a high-level description of the E-RTFDDA technology. 

Another E-RTFDDA core component is its innovative ensemble data assimilation algorithm, four-dimensional relaxation ensemble Kalman filter (4D-REKF). In the ensemble modeling, 4D-REKF permits a flow-dependent data weighting capability that improves the simpler Cressman-type “observation-nudging” FDDA. 4D-REKF computes Kalman gains using the multi-model E-RTFDDA forecasts which are ingested into E-RTFDDA models to replace the simple distance-dependent observation weighting functions in the original nudging model. A Local Ensemble Kalman Filter (LEKF) approach is employed to take account of multi-observations. 4D-REKF retains and leverages the advantages of both traditional Newtonian-relaxation and Ensemble Kalman Filter data assimilation schemes. It eliminates the shortcoming of empirical specification of spatial weight functions in the current station-nudging FDDA formulation. On the other hand, it extends the traditional (intermittent) EnKF data assimilation method to a 4D continuous data assimilation paradigm that greatly mitigates the dynamic shocks associated with the intermittent EnKF processes. Furthermore, 4D-REKF reduces the critical dependency on the background error covariance inflation required by the traditional EnKF and permits effective assimilation of all observations that may be available at irregular locations and times.  Fig.2 describes the general formulation of the 4D-REKF FDDA scheme. 4D-REKF enhances both the accuracy of model initial conditions and also the initial condition perturbation approach, and thus improves the overall capabilities of E-RTFDDA ensemble prediction.

Figure 2: A description of Obs-nudging and 4D-REKF FDDA formulations.
Figure 2: A description of Obs-nudging and 4D-REKF FDDA formulations. 

The research and development of E-RTFDDA is currently conducted under the sponsorship of the Army Test and Evaluation Command, the State Grid Corporation of China (SGCC), Xcel Energy, and the State Power Investment of China (SPIC) for supporting military tests, electric power grid weather safety and wind power forecast respectively. 

FY2018 ACCOMPLISHMENTS

The E-RTFDDA WRF model has been upgraded to the newest community WRF release Version 3.9.1 and 4.0. The upgrades involved 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 post-processing. The Army E-4DWX system was redesigned to have a cloud-resolvable 3-km grid domain for four test ranges respectively and one week of test runs were completed.  An E-RTFDDA system to support the power grid operation of the State Grid Corporation of China (SGCC) and wind power forecasts for three wind farms by the State Power Investment of China (SPIC). The SGCC E-RTFDDA system contains 30 WRF members and its domain cover the whole China region at grid intervals of 9-km. The system runs four forecast cycles per day and each cycle produces 72-hour forecasts.  For the SPIC wind power forecasting, a 30-member 3-km grid E-RTFDDA system is developed for the SPIC wind farms over a region with high-complex terrain. The system runs 3-hour cycles, producing 93-hour forecasts.  

4D-REKF was enhanced for assimilation of Doppler radar radial velocity measurements. This work involves a major change of the data structures that can handle the radar observation data and Kalman gains effectively. Unlike the radial velocity assimilation, radar reflectivity measurements are assimilated with hydrometeor and latent heat nudging (HLHN) scheme.  A convective weather case, during PECAN-2015 field campaign, that contains a supercell severe convection storm and three mesoscale convective systems, was selected to performing the radar data assimilation experiments. Numerical experiments were designed to test the validity of the scheme and its implementation. Preliminary results show welcoming potentials for using the scheme to improve short-range forecasting of convection systems.   

PLANS for FY2019

E-RTFDDA will be further improved in 4D-REKF FDDA data assimilation, the model output statistical post-processing using the analog bias-correction and quantile regression calibration schemes, and producing joint probability for important variables and indices desired by end-users. The WRF stochastic kinematic energy backscattering (SKEB) 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 is proposed to transfer to new U.S. and international promising sponsors.

4D-REKF is still at its early stage of initial operating capability. Further evaluation and enhancement are necessary to fully exploit the power of the technology.  The limited representativeness of Kalman gains computed from mesoscale ensemble forecasts of excessively small number of ensemble members is still the main challenge for effectively taking the advantage of 4D-REKF.  Empirical algorithms will be explored to address the fact that ensemble mesoscale forecasts often lead to formation of sporadic, unrepresentative local structures in the Kalman gains that introduce noise and lessen the effectiveness of data assimilation. Refinement to 4D-REKF with cross-variable (covariance) “observation-nudging” capabilities for assimilating Doppler radar radial velocities should be studied. Strategies for nudging hydrometeors (rain, snow, and graupel mixing ratios) and radar reflectivity and lightning observations for cloud-resolvable ensemble forecast of convective systems will be studied. Finally, 4D-REKF for assimilating wind farm data to improve wind-power forecasts will be conducted in 2019.