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• W3143413382 abstract "Abstract The accurate estimation of observation error statistics for high‐density remote‐sensing data is a fundamental step toward optimally utilizing such data, including radar data, for operational numerical weather prediction. Based on the high‐resolution (3 × 3 km) GRAPES model together with the three‐dimensional variational assimilation system (GRAPES_3DVAR) developed by the China Meteorological Administration, a set of assimilation processes for radar radial wind data is designed. First, a scheme is proposed to guarantee the quality of radar radial wind data by correcting the velocity ambiguity for the data. The observation error covariances of the radar data in the GRAPES_3DVAR assimilation system are re‐estimated by using a diagnostic method based on analysis residuals, showing that the re‐estimated radial wind observation error covariances increase with altitude. An optimal regularization parameter is selected by the L‐curve method to constrain the radar observations that were assimilated using inaccurate observation error covariances. Furthermore, to verify the assimilation effects of the refined radial velocity observation error covariances and the constrained observations by the regularization parameter on the analysis and forecasting, a numerical study is carried out on typhoon Yunque , which occurred on 3 August 2018. The results show that the modified Doppler radar wind data assimilation using the re‐estimated radial velocity observation error covariances enhances the wind analysis field and the water vapour transport, and accordingly obtains better short‐term precipitation estimates. The radar wind data assimilation results of using the constrained observations also indicate an enhanced wind analysis field and water vapour transport, which leads to better short‐term precipitation estimates." @default.
• W3143413382 created "2021-04-13" @default.
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• W3143413382 date "2021-04-01" @default.
• W3143413382 modified "2023-10-17" @default.
• W3143413382 title "Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning" @default.
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• W3143413382 doi "https://doi.org/10.1002/qj.4036" @default.
• W3143413382 hasPublicationYear "2021" @default.
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