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  1. 防災科研関係論文

Impact of direct assimilation of ground‐based microwave radiometer on numerical weather prediction: Accounting for interchannel observation error correlations

https://nied-repo.bosai.go.jp/records/7280
https://nied-repo.bosai.go.jp/records/7280
a7ffc4bb-a5b2-40f8-8e08-a5d25617fe92
Item type researchmap(1)
公開日 2025-07-28
タイトル
言語 en
タイトル Impact of direct assimilation of ground‐based microwave radiometer on numerical weather prediction: Accounting for interchannel observation error correlations
言語
言語 eng
著者 Yasutaka Ikuta

× Yasutaka Ikuta

en Yasutaka Ikuta

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Hiromu Seko

× Hiromu Seko

en Hiromu Seko

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Kouichi Yoshimoto

× Kouichi Yoshimoto

en Kouichi Yoshimoto

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Kentaro Yamamoto

× Kentaro Yamamoto

en Kentaro Yamamoto

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Takuya Kawabata

× Takuya Kawabata

en Takuya Kawabata

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Hiroshi Ishimoto

× Hiroshi Ishimoto

en Hiroshi Ishimoto

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Kentaro Araki

× Kentaro Araki

en Kentaro Araki

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Takuya Tajiri

× Takuya Tajiri

en Takuya Tajiri

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Shingo Shimizu

× Shingo Shimizu

en Shingo Shimizu

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抄録
内容記述タイプ Other
内容記述 This study clarifies the impact of directly assimilating the brightness temperature from a ground‐based microwave radiometer (GMWR) on the accuracy of numerical weather prediction. The study focuses on heavy rainfall caused by quasi‐stationary band‐haped precipitation systems in Japan. We used the four‐dimensional variational method to assimilate the brightness temperatures observed by the GMWR network of the Japan Meteorological Agency. To efficiently handle interchannel observation error correlations, the observation term of the cost function was reformulated into the sum of squares of independent variables through a variable transformation based on the eigen‐decomposition of the observation error covariance matrix. Variational quality control was also applied to these independent variables, enabling dynamic quality control. As a result of GMWR assimilation, the accuracy of 12‐hour lead time precipitation forecasts was significantly improved, with notable reductions in biases in the water vapor and temperature fields, particularly in the lower troposphere. These results demonstrate that proper assimilation of GMWR data improves the accuracy of numerical weather prediction, especially for extreme weather events such as heavy rainfall.
言語 en
書誌情報 en : Quarterly Journal of the Royal Meteorological Society

発行日 2025-07-21
出版者
言語 en
出版者 Wiley
ISSN
収録物識別子タイプ EISSN
収録物識別子 1477-870X
DOI
関連識別子 10.1002/qj.5067
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