Fuxi Model Precipitation Forecast Correction Algorithm Based on Matrix Transformation

Abstract

A precipitation area correction model is designed by using the ideas of matrix rotation and matrix translation. The errors of the precipitation forecasts of the Fuxi large - scale model are statistically learned, and the precipitation area deviation is corrected according to the statistical values. The test results show that the precipitation area error after correction is reduced by 2% compared with that before correction, and the deviation is reduced by 1%.