This invention discloses a spatial
downscaling method for weather forecast models based on a
terrain classification super-resolution model. The method first obtains
nested data at each layer through dynamic
downscaling of the weather forecast model, then trains a super-resolution model using data at different resolutions of the target area. Following the above process, super-resolution models are established for different terrains, and a
terrain classification model is trained using
terrain data. The super-resolution model and the
terrain classification model are combined to obtain a fused prediction result. This fused model is the SRBTC model, which considers
spatial correlation. The model outputs the similarity probability between the area to be predicted and various different terrains. The prediction results are weighted and summed to obtain the fused prediction result of the super-resolution model. During model training, a specified value scaling method is proposed to consider the differences between high- and low-resolution meteorological
simulation data. When applying the model, a splitting and merging method is proposed to consider the differences between multi-scale
simulation data, and the applicability of the model is specified.