A hybrid nudging-ensemble Kalman filter approach to data assimilation in WRF/DART

Lili Lei, David R. Stauffer, Aijun Deng

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

A hybrid nudging-ensemble Kalman filter (HNEnKF), previously tested in the Lorenz three-variable model and a two-dimensional shallow-water model, is now explored in WRF/DART with real observations using a Cross Appalachian Tracer Experiment case from September 1983 (CAPTEX-83). The HNEnKF, effectively combining observation nudging and the ensemble Kalman filter (EnKF), achieves a more gradual data assimilation and greatly reduces the insertion noise compared to the EnKF. Three-hourly surface observations and 12-hourly rawinsonde observations from the World Meteorological Organization (WMO) are assimilated. These assimilated meteorological observations are used to verify the priors of the experiments. It is found that the HNEnKF generally obtains better priors than the EnKF. To independently verify the HNEnKF approach, the hourly WRF experiment results are also used to drive the Second-Order Closure Integrated Puff (SCIPUFF) model to predict surface tracer concentrations that are verified against the observed surface concentration data. The HNEnKF analyses driving SCIPUFF produce better statistics of the independent tracer data than the EnKF. Thus there appear to be some advantages in the hourly dynamic analyses produced by the continuous HNEnKF compared to the intermittent EnKF. The analyses of surface pressure tendency demonstrate that the HNEnKF is able to provide better temporal smoothness and dynamic consistency in the hourly analyses than the EnKF.

Original languageEnglish (US)
Pages (from-to)2066-2078
Number of pages13
JournalQuarterly Journal of the Royal Meteorological Society
Volume138
Issue number669
DOIs
StatePublished - Oct 2012

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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