The application belongs to the technical field of sub-seasonal prediction, and discloses a sub-seasonal prediction bias
correction method,
system, device and medium based on Swin
Transformer, to solve the problem of large prediction bias. The method comprises: obtaining sub-seasonal historical prediction data as to-be-corrected training data, and ERA5 reanalysis data and ground
observation data as true value reference data, and performing pretreatment; constructing a correction model by combining an autoregressive Swin
Transformer network with a
sliding time window, inputting the to-be-corrected training data of a preset time length in the past, and predicting the
bias field of a preset time length in the future; adopting a point-to-point residual correction form to output the corrected meteorological element field; comparing the corrected meteorological element field with the true value reference data, calculating the error by a composite
loss function, and optimizing the network weight by back propagation, to obtain an optimized correction model; and inputting the sub-seasonal future prediction data into the model, and outputting the corrected meteorological element field.