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

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. The development of an artificial intelligence-based weather forecasting model named YingLong-weather, enables a spatial resolution of 3 km x 3 km and minimises boundary condition issues by smoothing with lateral boundary conditions