The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation
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-depende...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2013/512656 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564740685037568 |
|---|---|
| author | Jidong Gao Ming Xue David J. Stensrud |
| author_facet | Jidong Gao Ming Xue David J. Stensrud |
| author_sort | Jidong Gao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3582d4d0aef147698009039ed1964fc9 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-3582d4d0aef147698009039ed1964fc92025-02-03T01:10:15ZengWileyAdvances in Meteorology1687-93091687-93172013-01-01201310.1155/2013/512656512656The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data AssimilationJidong Gao0Ming Xue1David J. Stensrud2Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USACenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USANOAA/National Severe Storm Laboratory, National Weather Center 120 David L. Boren Boulevard, Norman, OK 73072, USAA 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.http://dx.doi.org/10.1155/2013/512656 |
| spellingShingle | Jidong Gao Ming Xue David J. Stensrud The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation Advances in Meteorology |
| title | The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation |
| title_full | The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation |
| title_fullStr | The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation |
| title_full_unstemmed | The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation |
| title_short | The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation |
| title_sort | development of a hybrid enkf 3dvar algorithm for storm scale data assimilation |
| url | http://dx.doi.org/10.1155/2013/512656 |
| work_keys_str_mv | AT jidonggao thedevelopmentofahybridenkf3dvaralgorithmforstormscaledataassimilation AT mingxue thedevelopmentofahybridenkf3dvaralgorithmforstormscaledataassimilation AT davidjstensrud thedevelopmentofahybridenkf3dvaralgorithmforstormscaledataassimilation AT jidonggao developmentofahybridenkf3dvaralgorithmforstormscaledataassimilation AT mingxue developmentofahybridenkf3dvaralgorithmforstormscaledataassimilation AT davidjstensrud developmentofahybridenkf3dvaralgorithmforstormscaledataassimilation |