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Impact of Error Co-variance Matrix on the ARM Variational Analysis

Minghua Zhang State University of New York at Stony Brook
Shaocheng Xie Lawrence Livermore National Laboratory

Category: Modeling

The specification of error covariance of the atmospheric state variables at different stations plays a significant role in the variational adjustment of these variables for the derivation of advective tendencies and vertical velocity. This study investigates the structure of the error covariance matrices by using the operational analysis and sounding data. The objective is to improve the quality of the ARM continuous forcing data. We stratify the errors by using observed surface precipitation. The sensitivity of the forcing data, especially the vertical distribution of the variational analysis, to the error covariance is presented. A new algorithm is described. Verification study of the improvements is presented.

This poster will be displayed at ARM Science Team Meeting.

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