Some observing system simulation experiments with a hybrid 3DEnVAR system for storm-scale radar data assimilation

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A hybrid three-dimensional ensemble-variational data assimilation (3DEnVAR) algorithm is developed based on the 3D variational data assimilation (3DVAR) and ensemble Kalman filter (EnKF) programs with the Advanced Regional Prediction System (ARPS). The method uses the extended control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The method is applied to the assimilation of simulated data from two radars for a supercell storm. Some sensitivity experiments are performed to answer questions about how flow-dependent covariance estimated from the forecast ensemble can be best used in the hybrid 3DEnVAR scheme. When the ensemble size is relatively small (with 5 or 10 ensemble members), it is found that experiments with a weaker weighting value for the ensemble covariance leads to better analysis results. Even when severe sampling errors exist, introducing ensemble-estimated covariances into the variational method still benefits the analysis. For reasonably large ensemble sizes (50-100 members), a stronger relative weighting (>0.8) for the ensemble covariance leads to better analyses from the hybrid 3DEnVAR. In addition, the sensitivity experiments also indicate that the best results are obtained when the number of the augmented control variables is a function of three spatial dimensions and ensemble members, and is the same for all analysis variables.

Original languageEnglish (US)
Pages (from-to)3326-3346
Number of pages21
JournalMonthly Weather Review
Volume142
Issue number9
DOIs
StatePublished - Jan 1 2014

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data assimilation
radar
simulation
supercell
experiment
Kalman filter
sampling
prediction
analysis
method
forecast
assimilation
programme

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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abstract = "A hybrid three-dimensional ensemble-variational data assimilation (3DEnVAR) algorithm is developed based on the 3D variational data assimilation (3DVAR) and ensemble Kalman filter (EnKF) programs with the Advanced Regional Prediction System (ARPS). The method uses the extended control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The method is applied to the assimilation of simulated data from two radars for a supercell storm. Some sensitivity experiments are performed to answer questions about how flow-dependent covariance estimated from the forecast ensemble can be best used in the hybrid 3DEnVAR scheme. When the ensemble size is relatively small (with 5 or 10 ensemble members), it is found that experiments with a weaker weighting value for the ensemble covariance leads to better analysis results. Even when severe sampling errors exist, introducing ensemble-estimated covariances into the variational method still benefits the analysis. For reasonably large ensemble sizes (50-100 members), a stronger relative weighting (>0.8) for the ensemble covariance leads to better analyses from the hybrid 3DEnVAR. In addition, the sensitivity experiments also indicate that the best results are obtained when the number of the augmented control variables is a function of three spatial dimensions and ensemble members, and is the same for all analysis variables.",
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Some observing system simulation experiments with a hybrid 3DEnVAR system for storm-scale radar data assimilation. / Gao, Jidong; Stensrud, David Jonathan.

In: Monthly Weather Review, Vol. 142, No. 9, 01.01.2014, p. 3326-3346.

Research output: Contribution to journalArticle

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