摘要：利用2019年8月—2021年7月期间的FY-4A卫星数据,结合江苏及周边地区自动气象站同期的能见度观测数据,建立了包括有海雾时次及非海雾的卫星像素集,利用随机森林算法构建了海雾识别模型,实现对江苏及周边区域的日间海雾识别。检验结果表明:与基于阈值法的海雾识别模型相比,训练得到的随机森林海雾识别模型具有较高的识别精度,该模型平均命中率、平均临界成功指数和平均误报率分别为83.46%、79.46%和5.7%,均优于阈值法的结果。两种识别模型对2021年4月12日发生在黄渤海区域海雾天气个例识别结果的对比表明,随机森林海雾识别模型能够更好地识别出发生海雾的区域。

关键词：FY-4A卫星；能见度观测数据；卫星像素集；随机森林；日间海雾识别

Abstract：Using the FY-4A satellite data from August 2019 to July 2021 combined with the visibility observation data of automatic weather stations in Jiangsu province and surrounding areas in the same period, a satellite pixel set including sea fog and non-sea fog is established, and the daytime sea fog identification in Jiangsu and surrounding areas is realized based on a sea fog recognition model that is constructed using random forest algorithm. The validation results show that the random forest sea fog recognition model trained in this paper has higher recognition accuracy compared with the sea fog recognition model based on threshold method, and the average hit rate, average critical success index and average false positive rate of the model are 83.46%, 79.46%and 5.7%, respectively. At the same time, by comparing the identification results of the two identification models for a sea fog weather case in the Yellow Sea and Bohai Sea on April 12, 2021, it shows that the random forest sea fog identification model can better identify the sea fog area.

Key words：WRF model; 3DVAR; typhoon simulation; HY-2B satellite; China-France Oceanography satellite; scatterometer sea surface wind

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