The impact of covariance localization for radar data on EnKF analyses of a developing MCS

Observing system simulation experiments

Ryan A. Sobash, David Jonathan Stensrud

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18km in the horizontal and 3, 6, and 12kmin the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallestRMSEfor most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal ocalization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that ere similar to the horizontal and vertical length scales of the prior state correlations, especially for observationstate correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF xperiments that assimilate radar data are discussed.

Original languageEnglish (US)
Pages (from-to)3691-3709
Number of pages19
JournalMonthly Weather Review
Volume141
Issue number11
DOIs
StatePublished - Nov 1 2013

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Kalman filter
radar
simulation
experiment
convective system
wind field
reflectivity
merger
weather

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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abstract = "Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18km in the horizontal and 3, 6, and 12kmin the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallestRMSEfor most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal ocalization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that ere similar to the horizontal and vertical length scales of the prior state correlations, especially for observationstate correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF xperiments that assimilate radar data are discussed.",
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The impact of covariance localization for radar data on EnKF analyses of a developing MCS : Observing system simulation experiments. / Sobash, Ryan A.; Stensrud, David Jonathan.

In: Monthly Weather Review, Vol. 141, No. 11, 01.11.2013, p. 3691-3709.

Research output: Contribution to journalArticle

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