The development of a hybrid EnKF-3DVAR algorithm for storm-scale data assimilation

Jidong Gao, Ming Xue, David J. Stensrud

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number512656
JournalAdvances in Meteorology
Volume2013
DOIs
StatePublished - Dec 16 2013

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assimilation
Kalman filters
Kalman filter
data assimilation
forecasting
Radar
radar
hydrometeors
supercell
radar data
dynamic models
reflectivity
Dynamic models
reflectance
cycles
method
prediction
predictions

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Pollution
  • Atmospheric Science

Cite this

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abstract = "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.",
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The development of a hybrid EnKF-3DVAR algorithm for storm-scale data assimilation. / Gao, Jidong; Xue, Ming; Stensrud, David J.

In: Advances in Meteorology, Vol. 2013, 512656, 16.12.2013.

Research output: Contribution to journalArticle

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