| 基于Himawari-8数据的夜间海雾识别 |
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| 引用本文： | 郝姝馨,郝增周,黄海清,牛瑞,潘德炉,顾吉星.基于Himawari-8数据的夜间海雾识别[J].海洋学报,2021,43(11):166-180. |
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| 作者姓名： | 郝姝馨 郝增周 黄海清 牛瑞 潘德炉 顾吉星 |
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| 作者单位： | 1.南京大学 地理与海洋科学学院，江苏 南京 210023 |
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| 基金项目： | 国家自然科学基金重大项目课题（61991454）；南方海洋科学与工程广东省实验室（广州）人才团队引进重大专项（GML2019ZD0602）；全球变化与海气相互作用专项（JC-PAC-YGST）；国家重点研发计划（2016YFC1401903） |
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| 摘 要： | 海雾是一种发生在海面的灾害性天气现象，掌握海雾的分布与生消变化，能有效地减少海雾带来的危害。卫星遥感观测具有近实时、大范围覆盖、连续观测等特点，特别是高时间分辨率的静止卫星观测系统，能够对海雾的发生?发展?消亡过程进行动态跟踪观测。本文以2018?2019年黄、渤海发生的海雾事件为样例，利用日本静止气象卫星Himawari-8（H-8）红外辐射数据，分析海雾的多通道红外亮温辐射特性，通过不同波段差和波段比组合，定义海雾和晴空水体分离指数、海雾和一般云系分离指数、多通道亮温差斜率指数以及中红外亮温纹理指数，提出基于多指数概率分布的夜间海雾监测算法；算法分别应用于H-8和韩国静止气象卫星GEO-KOMPSAT2A（GK-2A）数据，对2020年2?6月发生的6次海雾事件多时次卫星观测识别出的海雾位置分布和覆盖面积进行对比实现互验证，结果表明，本文提出的夜间海雾监测算法能有效地实现夜间海雾的识别；选择2020年4月29日夜间H-8和GK-2A 每10 min一次连续观测数据的监测结果，对海雾的发生区域进行跟踪分析，清晰地展现出此次海雾事件的发生、发展演变过程，说明算法能清楚地监测出各时段海雾的分布，跟踪海雾的发展变化，可为海上大雾的防灾减灾提供科学依据和决策基础。
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| 关 键 词： | 海雾 红外遥感 静止气象卫星 波段组合 |
| 收稿时间： | 2020-11-26 |
Nighttime sea fog recognition based on Himawari-8 data |
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| Institution: | 1.School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China2.State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China3.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China4.Yantai Marine Environmental Monitoring Center Station, State Oceanic Administration, Yantai 264006, China |
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| Abstract: | Sea fog is a kind of disastrous weather phenomenon which occurs on the sea surface. Mastering the distribution and dynamic changes of sea fog can effectively reduce the disasters caused by sea fog. Satellite remote sensing observation has the characteristics of near real time, wide coverage, continuous observation and so on. Especially the geostationary satellite remote sensing observation with high time resolution, which can continuously and dynamically track the occurrence, development and extinction of sea fog. The sea fog events in the Yellow Sea and Bohai Sea are taken from 2018 to 2019 as examples in this paper. Based on the analysis of the multi-channel bright temperature radiation characteristics of sea fog in the Yellow Sea and Bohai Sea by using Himawari-8 (H-8) geostationary satellite data, the separation index of sea fog and cloud, the separation index of sea fog and water, the slope index of multi-band brightness temperature difference and texture index of mid-infrared bright temperature are defined through the difference and ratio combination of different bands, and the night sea fog monitoring algorithm based on multi-exponential probability distribution is proposed to realize the automatic identification of sea fog at night. The algorithm is applied to H-8 and GEO-KOMPSAT2A (GK-2A) geostationary satellite data respectively. The position distribution and coverage area of sea fog identify by multi-time satellite observations of six sea fog events from February to June 2020 are compared to achieve mutual verification. The results show that the algorithm proposed in this paper can effectively recognize sea fog at night. The monitoring results every 10 minutes of continuous observations of H-8 and GK-2A at night on April 29, 2020 are selected to follow up and analyze the area where sea fog occurred, it shows the occurrence, development and evolution of the sea fog event clearly. It indicates that the algorithm can monitor the distribution of sea fog and track the development and change of fog. It can provide scientific basis and decision-making basis for the prevention and mitigation of sea fog. |
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