気象用二重偏波フェーズドアレイレーダとCNNおよびLSTMを用いた豪雨検知手法

  • 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

書誌事項

タイトル別名
  • 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

抄録

<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

参考文献 (5)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ