Optimized Forecasting and Verification of Low Visibility for Shanghai Stations Based on Machine Learning

XIA Yang; XIE Ying; WANG Xiaofeng; GAO Yanqing; GU Wen; FAN Hao

doi:10.16032/j.issn.1004-4965.2026.012

Abstract：Based on a machine learning(ML)algorithm,LightGBM,an optimized forecast model of visibility was established to correct the numerical weather prediction(NWP)at Shanghai stations.The model was trained based on the historical station observations(2019-2023)and hourly NWP output data from the numerical weather forecasting(CMA-SH9)and integrated weather-air quality forecasting(WARMS-CMAQ)models.In order to alleviate the problem of extremely unbalanced observational samples and improve the prediction skill for fog and other low-visibility events,visibility was classified into different levels,with the ML task framed as a classification problem.The influence of low-visibility samples was emphasized through data pre-cleaning and differentiated weight coefficients for different grades.Finally,the recall rate,precision,and comprehensive TS scores of the first two grades(≤1 km and 1-3 km)were used as evaluation criteria.The evaluation of the test dataset and subsequent independent operational phase show that the ML-based model significantly improves visibility forecasting skills compared to numerical models.In particular,the hit rate for low visibility events(≤1 km)increased from approximately 20%to nearly 60%,and the TS score improved to 0.3.In addition,the analysis of typical cases since December 2023 shows that the LGBM model performs good in forecasting heavy fogs,with better agreement with observations in terms of fog onset and dissipation.It's indicated that this ML-based model obviously alleviates the serious underprediction of low-visibility events(especially for fog)in the original numerical model,which proves the algorithm's feasibility and superiority.