レーダーの深層生成モデルを用いた熟練した降水予測
Suman Ravuri1, Karel Lenc1, Matthew Willson1
1DeepMind, London, UK.
Nature
|September 30, 2021
まとめ
この研究は,降水予測の深層生成モデルを導入し,稀な大雨の予測の正確性と有用性を改善します. 新しいモデルは現実的で一貫した予測を提供し,専門家の評価において既存の方法を上回ります.
科学分野:
- 気象学 と 大気科学
- 人工知能と機械学習
- 地理空間データ分析
背景:
- 降水予測 (最大2時間までの予測) は,天候に依存するセクターにとって不可欠です.
- 現在のレーダーベースの方法では 流動的イニシアチブのような非線形現象に 苦労しています
- ディープラーニングモデルには物理的な制約がなく 予報は曖昧で 豪雨の場合のパフォーマンスは低下します
研究 の 目的:
- レーダーデータから確率的な降雨を予測する深層生成モデルを開発する.
- 既存の方法の限界に対処するため,特に稀な,中等から重度の雨が発生し,より長いリードタイムに対応するためです.
- 予報の質,一貫性,および運用天気予報の価値を向上させる.
主な方法:
- 確率的な降水予測のための深層生成モデルの開発.
- 高解像度で 時空的に一貫した予測に ラダーデータを活用する
- 統計的,経済的な,認知的な測定と専門的な気象学者の評価を用いて評価する.
主要な成果:
- ジェネラティブモデルは,広い領域 (最大1,536 km × 1,280 km) とリードタイム (5〜90分) で現実的で一貫した予測を生成します.
- 専門的な気象学者は,このモデルが,競合する2つの方法に対して,89%のケースで正確性と有用性で第一にランク付けされました.
- 量的な検証は,予測の価値と運用的有用性を向上させるため,曖昧さのない熟練した現在予測を示します.
結論:
- 深層生成型モデルは,正確で価値のある降水予測のための有望なアプローチを提供します.
- 提案されたモデルは,特に極端な天候現象に関して,既存の方法の限界を克服しています.
- 信頼性の高い高解像度,確率的な予測を提供することで,運営上の意思決定を向上させます.
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