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  • Author or Editor: Xuguang Wang x
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Therese E. Thompson
,
Louis J. Wicker
, and
Xuguang Wang

Abstract

Maximizing the accuracy of ensemble Kalman filtering (EnKF) radar data assimilation requires that the observation operator sample the model state in the same manner that the radar sampled the atmosphere. It may therefore be desirable to include volume averaging and power weighting in the observation operator. This study examines the impact of including radar-sampling effects in the Doppler velocity observation operator on EnKF analyses and forecasts. Locally substantial differences are found between a simple point operator and a realistic radar-sampling operator when they are applied to the model state at a single time. However, assimilation results indicate that the radar-sampling operator does not substantially improve the EnKF analyses or forecasts, and it greatly increases the computational cost of the data assimilation.

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Sijie Pan
,
Jidong Gao
,
David J. Stensrud
,
Xuguang Wang
, and
Thomas A. Jones

Abstract

In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellite-derived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating the TPW data has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment.

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