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气象:2023,49(5):563-573
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基于FY-4红外资料和光流法的不同空间尺度云团的外推可预报性研究
史小康,程文聪,张文军,周著华,胡艳冰
(北京航空气象研究所,北京 100085)
Study on Extrapolation Predictability of Cloud Clusters in Different Space Scales Based on FY-4 Infrared Data and Optical Flow Method
SHI Xiaokang,CHEN Wencong,ZHANG Wenjun,ZHOU Zhuhua,HU Yanbing
(Beijing Aviation Meteorological Institute, Beijing 100085)
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投稿时间:2022-03-01    修订日期:2022-11-23
中文摘要: 为研究云图和不同尺度云团的外推可预报性特征,设计了一种适应于FY-4卫星红外云图(10.8 μm)的云检测方法,结合区域识别算法,剥离出不同尺度的云团;使用具有HS全局约束方案的光流法,开展外推敏感试验。对2020年不同月份的12个个例的统计结果表明: 对亮温云图进行外推预报时,可用光流信息接近6 h,第0.5、1和6小时亮温均方根误差分别约为4.4、7.1和16.7 K,外推预报准确率随着预报时长的增加呈指数趋势降低。为消除亮温日变化影响,对云检测结果即云团分布进行不同时长的外推预报,统计结果表明:云团位置预报偏差是外推预报在第0~1小时中误差快速增长的主要原因,云团面积和位置的预报偏差是第1~6小时中误差的主要组成。>2000 km、200~2000 km、20~200 km、<20 km等尺度云团的可参考外推时长分别不超过6、1.5、1 h和15 min;外推“所有”尺度云团的预报主要体现了>2000 km尺度云团的外推预报特征;外推预报准确率随着云团空间尺度的减小而降低,这与不同尺度云团的物理属性演变特性、生命史长短等要素密切相关。以上研究结果对红外云图外推预报业务应用有一定指导意义。
Abstract:In order to study the extrapolation predictability of cloud images or cloud clusters with different scales, two things were done. First, a cloud detection method suitable for FY-4 satellite infrared cloud images (10.8 μm) was designed, and the cloud clusters with different scales were separated by the regional recognition algorithm. Second, the optical flow method of HS global constraint scheme was used to carry out extrapolation test. The statistical results from 12 examples in 2020 show that in the extrapolation forecasting of brightness temperature, the available optical flow information is close to 6 h. The RMSE of bright temperatures in 0.5, 1 and 6 hours are about 4.4, 7.1 and 16.7 K respectively. The accuracy of extrapolation prediction decreases with the increasing of time length in forecasting. In addition, because of diurnal variations in brightness temperature, the cloud detection results are used in the following extrapolation tests. In the short-time (0-1 h) extrapolation forecast, the error increase is mainly caused by the deviations from cloud location. During the first to sixth hours, the main forecast error is caused by the prediction errors in cloud location and cloud area. The accuracy of extrapolation forecasting decreases as the spatial scale of the cloud cluster decreases. The useable time lengths of extrapolating cloud clusters in scales >2000 km, 200-2000 km, 20-200 km, and <20 km are shorter than 6 h, 1.5 h, 1 h and 15 min, respectively. The extrapolated results in all scales are similar with that in the scale >2000 km. The research results can give a significant guide in extrapolation forecasting of infrared cloud image in operational applications.
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基金项目:高分对地观测专项项目(GFZX0402180102)资助
引用文本:
史小康,程文聪,张文军,周著华,胡艳冰,2023.基于FY-4红外资料和光流法的不同空间尺度云团的外推可预报性研究[J].气象,49(5):563-573.
SHI Xiaokang,CHEN Wencong,ZHANG Wenjun,ZHOU Zhuhua,HU Yanbing,2023.Study on Extrapolation Predictability of Cloud Clusters in Different Space Scales Based on FY-4 Infrared Data and Optical Flow Method[J].Meteor Mon,49(5):563-573.