書誌事項
- タイトル別名
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- A Consideration of Heavy Rainfall Detection Method Using Multi-Parameter Phased Array Radar, Convolutional Neural Network, and Long Short-Term Memory Network
- キショウヨウ ニジュウ ヘンパ フェーズドアレイレーダ ト CNN オヨビ LSTM オ モチイタ ゴウウ ケンチ シュホウ
この論文をさがす
説明
<p>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.</p>
収録刊行物
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- 電気学会論文誌. A
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電気学会論文誌. A 144 (4), 132-138, 2024-04-01
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390862623771708544
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- NII書誌ID
- AN10136312
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- ISSN
- 13475533
- 03854205
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- NDL書誌ID
- 033438327
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可