[A-AS03] 大規模な水蒸気場と組織化した雲システム

[AAS03-09] Subseasonal prediction of Madden-Julian Oscillation using machine learning

神野 拓哉¹、三浦 裕亮¹、*中井 拳吾²、齊木 吉隆³、坂井 彩織¹、末松 環¹、高須賀 大輔⁴、米田 剛³ (1.東京大学、2.東京海洋大学、3.一橋大学、4.海洋研究開発機構)

キーワード：マッデン・ジュリアン振動、機械学習、季節内時系列予測

By employing machine learning techniques, we construct a model for predicting the time series of the Realtime multivariate MJO index, which is a proxy that detects the MJO as an entity of active convection coupled to large-scale circulation at the intraseasonal time scale. The number of days for which the prediction is valid is measured for each of the 16 different phases of initial data in the MJO phase space. The model was evaluated to have a capability of predicting the RMM time-series for more than one month by applying a widely accepted criteria for the predictability of the MJO.