Development of the Real-Time 30-s-Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar : Description and Preliminary Evaluation

Honda, T.; Amemiya, A.; Otsuka, S.; Lien, G. -Y.; Taylor, J.; Maejima, Y.; Nishizawa, S.; Yamaura, T.; Sueki, K.; Tomita, H.; Satoh, S.; Ishikawa, Y.; Miyoshi, T.

Development of the Real-Time 30-s-Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar : Description and Preliminary Evaluation

Honda, T.; Amemiya, A.; Otsuka, S. et al.

Journal of Advances in Modeling Earth Systems, 2022, 14(6), e2021MS002823

Permalink : https://hdl.handle.net/2115/86478

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/86478
タイトル	Development of the Real-Time 30-s-Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar : Description and Preliminary Evaluation
著者	Honda, T. ; Amemiya, A. ; Otsuka, S. ; Lien, G. -Y. ; Taylor, J. ; Maejima, Y. ; Nishizawa, S. ; Yamaura, T. ; Sueki, K. ; Tomita, H. ; Satoh, S. ; Ishikawa, Y. ; Miyoshi, T.
著者名 Honda, T.
著者名 Amemiya, A.
著者名 Otsuka, S.
著者名 Lien, G. -Y.
著者名 Taylor, J.
著者名 Maejima, Y.
著者名 Nishizawa, S.
著者名 Yamaura, T.
著者名 Sueki, K.
著者名 Tomita, H.
著者名 Satoh, S.
著者名 Ishikawa, Y.
著者名 Miyoshi, T.
言語	英語
キーワード	data assimilation
	phased-array weather radar
	numerical weather prediction
発行日	2022-06
出版者	American Geophysical Union
収録物名	Journal of Advances in Modeling Earth Systems
巻(号)	14(6)
ページ	e2021MS002823
抄録	We present the first ever real-time numerical weather prediction system with 30-s update cycles at a 500-m grid spacing for the prediction of convective precipitation in the subsequent 30 min using a new-generation multi-parameter phased array weather radar. The system comprises a regional atmospheric model known as the SCALE and the local ensemble transform Kalman filter (LETKF). To accelerate the SCALE-LETKF system, data transfer between the two aforementioned components is performed using a memory copy instead of a file I/O. A complete real-time workflow including domain nesting and observational data transfer is constructed. A real-time test in July and August 2020 showed that the system is fast enough for a real-time application of 30-s forecast-analysis cycles and 30-min prediction. The development includes a new thinning method considering the spatially correlated observation errors in the dense radar data. This new thinning method is effective in two past case studies in the summer of 2019.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.1029/2021MS002823