Scholarlink

<p>Data-driven post-processing of medium-range precipitation forecasts typically relies on forecasted predictors. However, incorporating observed predictors remains challenging because of the long temporal gap between present observations and future precipitation. This study proposes a physically interpretable approach that leverages atmospheric moisture transport, where source–receptor relationships persist until medium-range lead times. Moisture sources associated with each event and lead time were identified using FLEXPART, a Lagrangian-based particle transport model. The methodology pre-processes evaporation-related observed auxiliary predictors (surface temperature and wind speed) in the input feature dataset, retaining only features corresponding to the identified moisture sources, before training the machine learning model. The method was applied to spatial-mean daily precipitation forecasts (1–14-day lead times) over the Kyushu region of Japan during summers of 2016–2024 using eXtreme Gradient Boosting algorithm. The methodology demonstrates the potential to improve 4–6-day lead time forecasts, including an average improvement of Nash-Sutcliffe efficiency (NSE) of 15.1% relative to raw forecasts, with NSE transitioning into the positive regime at mid-medium-range lead times. However, under operational conditions, the NSE improvement decreased to 5.6%. These findings highlight the importance of remote moisture sources in precipitation forecasting.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Moisture Source–Receptor Relationships for Post-Processing Medium-Range Precipitation Forecasts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

查看全文
  • Muditha Madusanka Dantanarayana,
  • Shinjiro Kanae

摘要

Data-driven post-processing of medium-range precipitation forecasts typically relies on forecasted predictors. However, incorporating observed predictors remains challenging because of the long temporal gap between present observations and future precipitation. This study proposes a physically interpretable approach that leverages atmospheric moisture transport, where source–receptor relationships persist until medium-range lead times. Moisture sources associated with each event and lead time were identified using FLEXPART, a Lagrangian-based particle transport model. The methodology pre-processes evaporation-related observed auxiliary predictors (surface temperature and wind speed) in the input feature dataset, retaining only features corresponding to the identified moisture sources, before training the machine learning model. The method was applied to spatial-mean daily precipitation forecasts (1–14-day lead times) over the Kyushu region of Japan during summers of 2016–2024 using eXtreme Gradient Boosting algorithm. The methodology demonstrates the potential to improve 4–6-day lead time forecasts, including an average improvement of Nash-Sutcliffe efficiency (NSE) of 15.1% relative to raw forecasts, with NSE transitioning into the positive regime at mid-medium-range lead times. However, under operational conditions, the NSE improvement decreased to 5.6%. These findings highlight the importance of remote moisture sources in precipitation forecasting.