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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

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Title: 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
Authors: Honda, T. Browse this author
Amemiya, A. Browse this author
Otsuka, S. Browse this author
Lien, G. -Y. Browse this author
Taylor, J. Browse this author
Maejima, Y. Browse this author
Nishizawa, S. Browse this author
Yamaura, T. Browse this author
Sueki, K. Browse this author
Tomita, H. Browse this author
Satoh, S. Browse this author
Ishikawa, Y. Browse this author
Miyoshi, T. Browse this author
Keywords: data assimilation
phased-array weather radar
numerical weather prediction
Issue Date: Jun-2022
Publisher: American Geophysical Union
Journal Title: Journal of Advances in Modeling Earth Systems
Volume: 14
Issue: 6
Start Page: e2021MS002823
Publisher DOI: 10.1029/2021MS002823
Abstract: 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.
Type: article
URI: http://hdl.handle.net/2115/86478
Appears in Collections:理学院・理学研究院 (Graduate School of Science / Faculty of Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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