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■ページ数 7ページ



A Consideration of Heavy Rainfall Detection Method Using Multi-Parameter Phased Array Radar, Convolutional Neural Network, and Long Short-Term Memory Network

■著者名 後藤 翼(電気通信大学大学院情報理工学研究科),菊池 博史(電気通信大学宇宙・電磁環境研究センター),芳原 容英(電気通信大学大学院情報理工学研究科/電気通信大学宇宙・電磁環境研究センター),牛尾 知雄(大阪大学大学院工学研究科)
■著者名(英語) Tsubasa Goto (Graduate School of Informatics and Engineering, The University of Electro-Communications), Hiroshi Kikuchi (Center for Space Science and Radio Engineering, The University of Electro-Communications), Yasuhide Hobara (Graduate School of Informatics and Engineering, The University of Electro-Communications/Center for Space Science and Radio Engineering, The University of Electro-Communications), Tomoo Ushio (Graduate School of Engineering, Osaka University)
■価格 会員 ¥550 一般 ¥770
■書籍種類 論文誌(論文単位)
■グループ名 【A】基礎・材料・共通部門
■本誌 電気学会論文誌A(基礎・材料・共通部門誌) Vol.144 No.4 (2024) 特集:2023年基礎・材料・共通部門大会
■本誌掲載ページ 132-138ページ
■原稿種別 論文/日本語
■電子版へのリンク https://www.jstage.jst.go.jp/article/ieejfms/144/4/144_132/_article/-char/ja/
■キーワード 二重偏波フェーズドアレイ気象レーダ,畳み込みニューラルネットワーク,長・短期記憶ネットワーク  multi-parameter phased array weather radar,convolutional neural network,long short-term memory network
■要約(英語) In order to mitigate weather disasters caused by heavy precipitations, it is important to observe 3-dimensional precipitation structure in a storm with high temporal resolution. In recent years, the development of phased array weather radar is being promoted for high-speed precipitation observations. We propose an algorithm for predicting heavy rainfall using machine learning for the novel phased array weather radar (Multi-Parameter Phased Array Weather Radar: MP-PAWR) observation data. The algorithm predicts localized convective rainfall by extracting the vertical structure of storms observed by MP-PAWR for each precipitation cell. The proposed method with the combination of convolutional neural networks and long short-term memory networks were applied to various observation data from MP-PAWR with high spatial and temporal resolution to predict heavy rainfalls a few minutes later. The results showed that the use of specific differential phase data gave particularly accurate predictions for heavy rainfall compared to radar reflectivity factor and differential reflectivity data.
■版 型 A4
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