Abstract:

Accurate forecast of dense fog (visibility less than or equal to 500 meters) is of great significance for ensuring people's safety and reducing economic losses. Based on the ground observation data of 31 national meteorological stations, environmental monitoring station data of southern Henan, and ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) from 2019 to 2021, the spatial distribution and physical characteristics of dense fog in this area were analyzed, and 30 dense fog forecast factors were selected. The visibility classification forecast (VCF) model is trained based on the LightGBM (Light Gradient Boosting Machine) machine learning method. By inputting the forecast field data by the ECMWF model at 08:00 every day and the PM_2.5 concentration monitoring at 08:00, the 3-hour graded visibility forecast products of the national stations in southern Henan are obtained. Through the prediction test of 17 dense fog days in southern Henan from January to March 2022, it was shown that the scores of the VCF model were generally better than the visibility forecast directly output by ECMWF model. The dense fog forecast product generated based on the VCF model for the period from 20:00 to 20:00 in southern Henan can provide important reference for forecasting.

Key words: LightGBM model, objective forecast method, dense fog forecast, southern Henan

摘要：

准确预报浓雾天气（能见度小于等于500 m）对保障人民生命安全和减少经济损失具有重要意义。利用2019—2021年豫南地区31个国家级气象站地面观测资料、环境监测站数据及欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）的ERA5再分析资料分析该地区浓雾的空间分布和物理量特征并选取30个浓雾预报因子，基于LightGBM（Light Gradient Boosting Machine）机器学习方法训练出豫南地区能见度分级预报（Visibility Classification Forecast，VCF）模型。通过输入ECMWF模式每日08：00（北京，下同）起报的预报场数据和08：00 PM_2.5质量浓度监测，得到豫南国家站逐3 h能见度分级预报产品。通过对2022年1—3月豫南17个浓雾日的预报检验显示，VCF模型各项评分总体来看优于ECMWF模式直接输出的能见度预报，基于该模型生成的20：00—20：00豫南浓雾预报产品可为当日夜间到次日上午的浓雾落区潜势预报提供重要参考。

关键词: LightGBM模型, 客观预报, 浓雾预报, 豫南