NowcastNet を使って,極端な降雨を熟練して予測する
Yuchen Zhang1, Mingsheng Long2, Kaiyuan Chen1
1School of Software, BNRist, Tsinghua University, Beijing, China.
Nature
|July 5, 2023
まとめ
NowcastNetは新しい降水予測モデルで 物理とAIを組み合わせることで 極端な天候の予測を改善します 高解像度で正確な予測を 3時間前まで提供し 既存の方法よりも優れています
科学分野:
- 気象学
- 人工知能
- データサイエンス
背景:
- 極端な降雨は社会経済的損害を 引き起こします
- 現在の降水予測方法は 精度,解像度,リードタイムに問題があります
- 既存の物理モデルやデータモデルには 複雑な天候のダイナミクスを捉えるのに 限界があります
研究 の 目的:
- 極端な降水に対する新しい非線形予測モデルを開発する.
- 物理的進化のスキームと条件付き学習の方法を統合する
- エンドツーエンドの神経ネットワークのフレームワークを介して予測エラーを最適化します.
主な方法:
- ニューラルネットワークベースの非線形ナウキャストモデルNowcastNetを開発した.
- 統合された物理的進化と条件付き学習
- エンドツーエンドの予測エラーの最適化を採用した.
- 訓練と検証のために米国と中国のレーダー観測を利用した.
主要な成果:
- NowcastNetは,物理的に妥当な降雨のnowcastを生成し,鋭い多次元パターンを示します.
- このモデルは,広大な地理的領域 (2,048 km × 2,048 km) で高解像度を実現します.
- 熟練した予測は3時間までのリードタイムで作成され,主要な方法の71%を上回ります.
- アドベクティブまたはコンベクティブプロセスによる極端なイベントを含む,軽いから重い雨の割合について正確な予測が行われます.
結論:
- NowcastNetは 極端な降雨を予測する上で 大きな進歩を遂げました
- 統一されたアプローチは,以前の方法の限界を克服します.
- このモデルは,天候災害の社会経済的影響を軽減するための優れた性能と可能性を実証しています.
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