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Advances in Meteorology
Volume 2013, Article ID 512656, 12 pages
Research Article

The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation

1Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USA
2NOAA/National Severe Storm Laboratory, National Weather Center 120 David L. Boren Boulevard, Norman, OK 73072, USA
3School of Meteorology, University of Oklahoma, Norman, OK 73072, USA

Received 17 May 2013; Revised 15 September 2013; Accepted 25 September 2013

Academic Editor: Kun Zhao

Copyright © 2013 Jidong Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational analysis is performed using an equal weighting of static and flow-dependent error covariance as derived from ensemble forecasts. The method is first applied to the assimilation of simulated radar data for a supercell storm. Results obtained using 3DVAR (with static covariance entirely), hybrid 3DVAR-EnKF, and the EnKF are compared. When data from a single radar are used, the EnKF method provides the best results for the model dynamic variables, while the hybrid method provides the best results for hydrometeor related variables in term of rms errors. Although storm structures can be established reasonably well using 3DVAR, the rms errors are generally worse than seen from the other two methods. With two radars, the results from 3DVAR are closer to those from EnKF. Our tests indicate that the hybrid scheme can reduce the storm spin-up time because it fits the observations, especially the reflectivity observations, better than the EnKF and the 3DVAR at the beginning of the assimilation cycles.