Fusion Correction Method for Numerical Weather Forecast Based on LGU-Net
ZHAO Yuheng
WANG Lu
WU Kunpeng
YUAN Jianfu
Abstract：Numerical weather forecast is the mainstream technique in modern weather forecasting,which has been developing towards higher resolution in recent years,while forecast errors remain unavoidable.This paper proposes a numerical forecast bias correction model,termed LSTM-GAM-UNet(LGU-Net),which introduces the Long Short-Term Memory(LSTM)structure and Global Attention Mechanism(GAM)based on the CU-Net model.The model further integrates various meteorological elements,terrain features derived from"Jilin-1"satellite data,and satellite cloud images,thereby constructing a multi-element fusion correction model.This model is also specifically optimized for meteorological forecasting.An experiment was conducted in northeastern China to correct the biases of the Global Forecast System(GFS)for the 2 m temperature(T2),2 m dew point temperature(D2),10 m wind components(U10,V10),and precipitation.Different models were compared and analyzed for bias correction experiments.By comparing with the original GFS forecasts,as well as the correction results from Anomaly Numerical-correction with Observation(ANO)method and the CU-Net method,it was shown that the LGU-Net model effectively improved the bias correction performance.In addition,the addition of cloud imagery data has a significant positive impact on precipitation correction,with an 80.76%and 76.04%improvement in RMSE and MAE compared to GFS,respectively.This paper provides new technical support for high-precision meteorological element forecasts.
Keywords：numerical weather predictiondeep learningLGU-Netbias correction
Publication Date：2025-12-31
Online Publishing Date：2026-01-12(First online date of this platform, not the publication date of the document)
Pages：13( 859-871 )