Scholarlink

<p>Mesoscale convective systems generate heavy-tailed extreme rainfall that conventional numerical weather prediction and standard deep learning architectures consistently underestimate, creating persistent forecasting bottlenecks over coastal urban zones. This study develops ConvScaleCast, a physics-informed scale-bridging framework generating 0–24 h precipitation predictions through integrated multi-scale atmospheric information fusion. The architecture unifies adaptive multi-band radar synthesis, the vertically constrained GVIP network for short-range radar extrapolation, and a novel PODA autoencoder paired with focal ordinal regression to correct systematic NWP biases. Unlike MSE-optimized models that oversmooth intense rainfall signatures, PODA prioritizes hard-to-characterize extreme samples to reconstruct the full skewed statistical distribution of heavy precipitation. Validation against multi-year observational records from Shanghai shows that the framework outperforms ECMWF operational forecasts and mainstream baseline algorithms, resolving the critical 2–6 h prediction gap while retaining coherent convective spatial structures. This multi-scale fusion architecture delivers a potential technical solution for skillful prediction of high-impact precipitation events.</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

ConvScaleCast: a physics-informed scale-bridging framework for 0–24 h urban convective precipitation forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Lei-Ming Ma,
  • Hai Chu,
  • Rui Wang,
  • Lei Chen,
  • Chunguang Yin,
  • Yuan Cao,
  • Fuchang Wang,
  • Li Guan

摘要

Mesoscale convective systems generate heavy-tailed extreme rainfall that conventional numerical weather prediction and standard deep learning architectures consistently underestimate, creating persistent forecasting bottlenecks over coastal urban zones. This study develops ConvScaleCast, a physics-informed scale-bridging framework generating 0–24 h precipitation predictions through integrated multi-scale atmospheric information fusion. The architecture unifies adaptive multi-band radar synthesis, the vertically constrained GVIP network for short-range radar extrapolation, and a novel PODA autoencoder paired with focal ordinal regression to correct systematic NWP biases. Unlike MSE-optimized models that oversmooth intense rainfall signatures, PODA prioritizes hard-to-characterize extreme samples to reconstruct the full skewed statistical distribution of heavy precipitation. Validation against multi-year observational records from Shanghai shows that the framework outperforms ECMWF operational forecasts and mainstream baseline algorithms, resolving the critical 2–6 h prediction gap while retaining coherent convective spatial structures. This multi-scale fusion architecture delivers a potential technical solution for skillful prediction of high-impact precipitation events.