Nighttime sea fog recognition based on Himawari-8 data
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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。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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图 2 不同地物类型在7个通道的亮温分布
Fig. 2 Distribution of brightness temperature of different types of feature in 7 channels
图 3 不同典型地物样本分布区域（a）及其在亮温差（
$ {BT}_{3.89}-{BT}_{8.59} $ ）和亮温差（$ {BT}_{9.64}-{BT}_{10.41} $ ）上的散点分布（b）海雾区标识为区域1；晴空水体区标识为区域2；低云区标识为区域3和区域4
Fig. 3 Sample distribution areas of different typical features (a) and their scatter plot distribution on (
$ {BT}_{3.89}-{BT}_{8.59} $ ) and ($ {BT}_{9.64}-{BT}_{10.41} $ )(b)The sea fog area is identified as area 1; the water area is identified as area 2; the low cloud area is identified as area 3 and area 4
图 4 海雾与区域4的低云在亮度温度差（
${{{B}{T}}_{3.89}-{B}{T}}_{8.59}$ ）和亮度温度差（${{{B}{T}}_{3.89}-{B}{T}}_{9.64}$ ）上的散点分布Fig. 4 Scatter distribution of (
${{{B}{T}}_{3.89}-{B}{T}}_{8.59}$ ) and (${{{B}{T}}_{3.89}-{B}{T}}_{9.64}$ ) in sea fog and area 4
图 5 海雾和一般低云云体的
$ \mathrm{\sigma } \;\left({BT}_{3.89}\right) $ 频数分布Fig. 5
$ \mathrm{\sigma } \;\left({BT}_{3.89}\right) $ frequency distribution of sea fog and low cloud
图 6 基于多指数概率分布的夜间海雾监测算法
Fig. 6 Night sea fog monitoring algorithm based on multi-exponential probability distribution
图 7 海雾样本的
$ {BTD}_{\mathrm{c}} $ 频数分布直方图Fig. 7 Histogram of
$ {BTD}_{\mathrm{c}} $ distribution for sea fog
图 8 海雾样本的
$ {BTD}_{\mathrm{w}} $ 频数分布直方图Fig. 8 Histogram of
$ {BTD}_{\mathrm{w}} $ distribution for sea fog
图 10 H-8卫星与GK-2A卫星监测出的海雾覆盖面积对比
Fig. 10 Comparison of the coverage area of sea fog monitor results based on H-8 and GK-2A
图 11 海雾监测结果
a. 2020年5月1月11:00（UTC）基于H-8卫星数据的海雾监测结果；b. 2020年5月1日11:00（UTC）基于GK-2A卫星数据的海雾监测结果；c. 2020年4月30日11:00（UTC）基于H-8卫星数据的海雾监测结果；d. 2020年4月30日11:00（UTC）基于GK-2A卫星数据的海雾监测结果
Fig. 11 Sea fog monitoring result
a. Sea fog monitoring result based on H-8 at 11:00 (UTC) on May 1, 2020 ; b. sea fog monitoring result based on GK-2A at 11:00 (UTC) on May 1, 2020 ; c. sea fog monitoring result based on H-8 at 11:00 (UTC) on April 30, 2020; d. sea fog monitoring result based on GK-2A at 11:00 (UTC) on April 30, 2020
图 12 2020年4月9日基于H-8卫星数据的海雾监测结果
Fig. 12 Sea fog monitoring result based on data of H-8 on April 29, 2020
图 13 2020年4月9日基于GK-2A数据的海雾监测结果
Fig. 13 Sea fog monitoring result based on data of GK-2A on April 29, 2020
图 14 2020年4月29日黄、渤海域在1 000 hPa的风场、湿度场
a.11:00 (UTC) 的风场、湿度场；b. 14:00 (UTC) 的风场、湿度场；c. 17:00 (UTC) 的风场、湿度场；d. 点A（35ºN，122ºE）处11‒17时（UTC）的温度廓线分布
Fig. 14 Wind field and humidity field at 1 000 hPa of the Yellow Sea and Bohai Sea on April 29, 2020
a.Wind field and humidity field at 11:00 (UTC); b. wind field and humidity field at 14:00 (UTC); c. wind field and humidity field at 17:00 (UTC); d. temperature profile of sea fog occurrence and growth stage on April 29, 2020 at A (35ºN, 122ºE)
表 1 H-8及GK-2A波段特征
Tab. 1 Band characteristics of H-8 and GK-2A
通道 H-8 GK-2A 带宽/μm 中心波长/μm 星下点分辨率/km 带宽/μm 中心波长/μm 星下点分辨率/km 1 0.43～0.48 0.47 1 0.45～0.49 0.48 1 2 0.50～0.52 0.51 1 0.49～0.52 0.51 1 3 0.63～0.66 0.64 0.5 0.63～0.68 0.64 0.5 4 0.85～0.87 0.86 1 0.85～0.88 0.86 1 5 1.60～1.62 1.61 2 1.37～1.38 1.37 2 6 2.25～2.27 2.26 2 1.59～1.63 1.61 2 7 3.74～3.96 3.89 2 3.74～3.93 3.87 2 8 6.06～6.43 6.24 2 5.79～6.63 6.40 2 9 6.89～7.01 6.94 2 6.74～7.21 7.04 2 10 7.26～7.43 7.35 2 7.20-7.42 7.24 2 11 8.44～8.76 8.59 2 8.41～8.77 8.47 2 12 9.54～9.72 9.64 2 9.43～9.81 9.50 2 13 10.3～10.6 10.41 2 10.12～10.59 10.37 2 14 11.1～11.3 11.24 2 10.90～11.56 11.35 2 15 12.2～12.5 12.38 2 11.81～12.92 12.34 2 16 13.2～13.4 13.28 2 13.02～13.57 13.24 2 
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表 2 用于海雾红外辐射特性分析的卫星遥感影像数据
Tab. 2 Satellite remote sensing image data used for infrared radiation characteristics determination of sea fog
日期 H-8观测时间（UTC） 区域 1 2018年3月23日 18:00, 18:30, 19:00 黄海海域 2018年3月24日 11:00‒12:30 (间隔10 min) 黄海海域 2018年3月24日 13:00‒19:00 (间隔30 min) 黄海海域 2018年3月25日 11:00‒19:00 (间隔10 min) 黄海海域 2018年3月26日 11:00‒19:00 (间隔30 min) 黄海海域 2 2018年4月18日 17:00, 18:00, 19:00 黄海南部和东海北部海域 3 2018年5月8日 13:00‒19:00 (间隔30 min) 黄海中南部、山东南部沿海、辽宁东南部沿海 2018年5月9日 11:00‒19:00 (间隔30 min) 黄海中南部、山东南部沿海、辽宁东南部沿海 2018年5月10日 11:00‒15:00 (间隔30 min) 黄海中南部、山东南部沿海、辽宁东南部沿海 4 2018年6月19日 21:00 渤海、黄海中部和北部海域 5 2019年2月24日 18:00 黄海西部海域 6 2019年4月7日 11:00‒17:00 (间隔30 min) 黄海南部海域、浙江沿海及东海海域、福建及台湾海峡 2019年4月7日 17:20‒19:00 (间隔10 min) 黄海南部海域、浙江沿海及东海海域、福建及台湾海峡
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表 3 用于算法验证和结果分析的卫星遥感影像数据
Tab. 3 Satellite remote sensing image data used for algorithm validation and result analysis
日期 H-8观测时间（UTC） GK-2A观测时间 （UTC） 区域 1 2020年2月18日 18:00, 18:30, 19:00, 19:30 18:00, 18:30, 19:00, 19:30 渤海中部、西北部及陆地 2 2020年4月29日 12:30‒17:00 (间隔30 min)* 12:30‒17:00 (间隔30 min)* 辽东半岛东部沿海、渤海北部、黄海大部海域 2020年4月30日 12:00‒17:00 (间隔60 min) 12:00‒17:00(间隔60 min) 辽东半岛东部沿海、渤海北部、黄海大部海域 2020年5月1日 12:00‒14:00 (间隔60 min) 12:00‒14:00 (间隔60 min) 辽东半岛东部沿海、渤海北部、黄海大部海域 3 2020年5月16日 11:00 11:00 黄海中部和南部海域 4 2020年5月23日 12:00 12:00 黄海北部和南部、东海北部 5 2020年5月26日 19:00 19:00 渤海东北部、黄海北部 6 2020年6月3日 12:00 12:00 黄海北部及东南部海域 注：*代表当此时段用于海雾发生发展的跟踪分析时，时间间隔10 min。
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