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<p>Artificial intelligence-based models for global weather forecasting have advanced rapidly, but research on high-resolution data-driven limited area models remains scarce. Here we introduce a limited area artificial intelligence-based weather forecasting model with 3 km and 1 h resolutions, utilizing parallel global-local structures to capture multiscale meteorological features. The model is trained on high-resolution regional analysis data, and utilizes the global artificial intelligence-based model forecasts for the lateral boundary condition during prediction, and operates much faster than the dynamical forecast model. In two selected limited areas, it outperforms dynamical forecast models in surface wind speed forecasting but underperforms in surface temperature and pressure. Skills in surface temperature and pressure can be further improved comparable to the dynamical forecast model by providing better lateral boundary conditions. Issues related to lateral boundary conditions, such as selecting width of lateral boundary regions and combining finer and coarser resolution predictions in the regions, are also studied.</p>

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An artificial intelligence-based limited area model for forecasting of surface meteorological variables

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月15日

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  • Pengbo Xu,
  • Xiaogu Zheng,
  • Tianyan Gao,
  • Yu Wang,
  • Junping Yin,
  • Juan Zhang,
  • Xuanze Zhang,
  • San Luo,
  • Zhonglei Wang,
  • Zhimin Zhang,
  • Xiaoguang Hu,
  • Xiaoxu Chen

摘要

Artificial intelligence-based models for global weather forecasting have advanced rapidly, but research on high-resolution data-driven limited area models remains scarce. Here we introduce a limited area artificial intelligence-based weather forecasting model with 3 km and 1 h resolutions, utilizing parallel global-local structures to capture multiscale meteorological features. The model is trained on high-resolution regional analysis data, and utilizes the global artificial intelligence-based model forecasts for the lateral boundary condition during prediction, and operates much faster than the dynamical forecast model. In two selected limited areas, it outperforms dynamical forecast models in surface wind speed forecasting but underperforms in surface temperature and pressure. Skills in surface temperature and pressure can be further improved comparable to the dynamical forecast model by providing better lateral boundary conditions. Issues related to lateral boundary conditions, such as selecting width of lateral boundary regions and combining finer and coarser resolution predictions in the regions, are also studied.