Meteorological numerical prediction correction method and device based on epfr multi-branch framework

Abstract

This application discloses a method and apparatus for correcting meteorological numerical forecasts based on the EPFR multi-branch framework. The method includes: acquiring initial meteorological data corresponding to each time granularity within a preset time period to obtain an initial meteorological dataset; preprocessing the initial meteorological dataset to generate a meteorological dataset; partitioning the meteorological dataset to generate a meteorological test dataset and a meteorological training sample set; training an EPFR multi-branch model based on the meteorological training sample set; and inputting the meteorological test dataset into the EPFR multi-branch model to obtain a meteorological numerical forecast correction information set. This implementation method relates to the field of meteorological forecast artificial intelligence application technology and significantly improves the accuracy of multivariate forecast correction.

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