2026-04-07 東京大学
提案手法「LEAS」の概念図。過去の予測データの中から、最新の予測実行時点のスキルに基づいてその一部を利用する。
＜関連情報＞
過去のアンサンブルデータを選択的に再利用することで、北米の最新の気温予報が改善される Selective reuse of prior ensemble data improves the latest air temperature forecast over North America
Daisuke Tokuda and Paul A. Dirmeyer
Proceedings of the National Academy of Sciences Published:April 8, 2026
DOI:https://doi.org/10.1073/pnas.2524516123
Significance
Subseasonal to seasonal (S2S) weather prediction—spanning the critical range of 2 to 5 wk—remains one of the hardest problems in atmospheric science. Forecast skill often collapses at these lead times because initial atmospheric information rapidly decays, while running larger ensembles is computationally prohibitive. We introduce a simple postprocessing method, lagged ensemble analog subselection (LEAS), that selectively chooses high-quality prior forecasts. Applied to multiple operational models, LEAS consistently improves daily temperature forecasts and extreme heat prediction over North America, even at multiweek horizons. By extracting more value from existing simulations without added cost, LEAS provides a practical and broadly applicable strategy to address one of the long-standing challenges in S2S prediction.
Abstract
Accurate subseasonal to seasonal (S2S) weather forecasts are critical for managing risks to society, yet improving forecast skill remains challenging. Ensemble forecasting mitigates atmospheric chaos but is limited by computational cost and by declining accuracy at longer lead times. Previous attempts to incorporate previous ensemble forecasts have yielded little improvement in the accuracy of the latest forecast because members from earlier initializations tend to degrade forecast quality. Here, we introduce a simple yet powerful postprocessing approach, lagged ensemble analog subselection (LEAS), which selectively chooses previous ensemble members that best predicted the most recent conditions. Using hindcasts of daily maximum 2-m air temperature over North America from four state-of-the-art S2S weather forecast models, we show that LEAS enhances both deterministic and probabilistic skill across multiple weeks, including for extreme heat events. The method reduces systematic bias as well as variance error, outperforming conventional lagged ensembles without requiring additional simulations or changes to model initialization. The improvement arises from filtering out poorly performing members and effectively emulating enhanced initialization of both atmospheric and land–surface states. LEAS combines principles of analog forecasting with lagged ensembles, extending their impact from short-term to multiweek predictions. Its simplicity and generality suggest broad applicability, not only to machine-learning–based weather forecasting but also to other predictive systems that rely on repeated initialization, such as hydrological, climate, and Earth system models. By extracting more value from existing forecast data, LEAS advances toward the upper limit of forecast skill achievable within current model frameworks while avoiding added computational burden.
