3Dニューラルネットワークによる正確な中距離地球天気予報
Kaifeng Bi1, Lingxi Xie1, Hengheng Zhang1
1Huawei Cloud, Shenzhen, China.
Nature
|July 5, 2023
まとめ
今や人工知能は 適度な世界の天気予報を 提供しています Pangu-WeatherはAIモデルで,従来の数値気象予測 (NWP) システムを精度とスピードで上回っています.
科学分野:
- 気象学
- 人工知能
- コンピュータ科学
背景:
- 数値天気予報 (NWP) は,正確な天気予報のための現在の標準ですが,計算が集中しています.
- 人工知能 (AI) の方法は,天気予報の加速を約束していますが,現在はNWPの精度が不足しています.
- 中期的な世界の天気予報は 科学的にも社会的にも 重要な課題です
研究 の 目的:
- 正確で中距離の世界的な天気予報のためのAIベースの方法を導入します.
- 天気予報のための地球特有の先例を持つ ディープラーニングモデルの有効性を実証する.
- 階層的な時間的集積戦略を使用して,中期予報の累積エラーを減らす.
主な方法:
- 地球特有のプリアで 3D ディープネットワークを利用した ディープラーニングモデルである Pangu-Weather を開発した.
- 誤差の蓄積を軽減するために階層的な時間的集約戦略を実装しました.
- 39年の世界の天気データで モデルを訓練した
主要な成果:
- Pangu-Weatherは,欧州中期予報センター (ECMWF) の運用統合予報システムと比較して優れた決定的予報結果を達成しました.
- AIモデルは,テストされたすべての変数に対して,中期予測で強いパフォーマンスを示しました.
- Pangu-Weatherは,極端な天気予報,アンサンブル予報,熱帯サイクロン追跡にも有効性を示しました.
結論:
- AIベースの方法,特にPangu-Weatherは,非常に正確な中期的な世界的な天気予報を達成できます.
- 地球特有の先駆けと階層的な時間的集約を持つ深層ネットワークは,複雑な気象パターンの分析に有効です.
- このAIアプローチは,従来のNWPシステムに対して,計算上効率的で正確な代替手段を提供します.
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