| 摘要: |
| 基于2005—2020年的中国气象局台风最佳路径数据集以及葵花（Himawari-8）和风云（FY-4）卫星云图数据，结合人工智能新技术，将深度学习模型应用于台风涡旋识别、台风定位定强、台风强度突变预测等方面，具体内容主要包括基于深度图像目标检测的台风涡旋识别模型、基于图像分类和检索的台风智能定强模型以及融合时空序列特征的台风快速增强判别模型，构建了一套台风智能监测和预报系统。通过对2020年全年样本进行了测试，结果显示：该系统对强热带风暴级及以上强度的台风涡旋正确识别率达90%以上，台风强度估测的MAE和RMSE分别为3.8 m/s和5.05 m/s，对全年独立样本强度快速加强预测的综合准确率达到65.3%，该系统实现了业务上利用高时空分辨率卫星图像实时对热带气旋进行自动识别、定位定强和智能追踪的功能，为进一步提高我国台风监测和预报预警的能力提供了有利支撑。 |
| 关键词: 深度学习 台风涡旋识别 智能定强 快速增强 卫星云图 |
| DOI：Doi：10.16032/j.issn.1004-4965.2022.043 |
| 分类号: |
| 基金项目: |
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| APPLICATION OF ARTIFICIAL INTELLIGENCE IN TYPHOON MONITORING AND FORECASTING |
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ZHOU Guanbo1,2, QIAN Qifeng1, LYU Xinyan1, LIU Chunxia3, XU Yajing4
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1. National Meteorological Center, Beijing 100081, China;2. State key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China;3. Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510641, China;4. Beijing University of Posts and Telecommunications, Beijing 100876, China
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| Abstract: |
| Based on the typhoon optimal path data set from China Meteorological Administration during 2005 and 2020, and the Himawari-8 and FY4 satellites cloud image data, combined with the new technology of artificial intelligence, this paper applies deep learning models to typhoon vortex identification, typhoon location and intensity determination, typhoon intensity change detection, etc. To be specific, the new technology has been applied to develop a typhoon vortex identification model based on depth image target detection, a typhoon intensity intelligent determination model based on image classification and retrieval, and a typhoon rapid enhancement discrimination model considering the characteristics of time and space series. Tests with all the samples of vortices occurred in 2020 show that the correct identification rate of typhoon vortex with intensity reaching strong tropical storm or stronger is over 90%, the mean absolute error and root mean squared error of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy rate of annual independent sample intensity rapid enhancement estimation is 65.3%. The system realizes the automatic identification, positioning, intensity determination, and real-time intelligent tracking of tropical cyclones by using satellite images with high spatial and temporal resolution. |
| Key words: deep learning typhoon vortex identification intelligent intensity determination rapid intensification satellite images |
