Application of artificial intelligence technology in typhoon monitoring and forecasting
In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerf...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.974497/full |
| _version_ | 1811188124504555520 |
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| author | Guanbo Zhou Xiang Fang Qifeng Qian Xinyan Lv Jie Cao Yuan Jiang Yuan Jiang |
| author_facet | Guanbo Zhou Xiang Fang Qifeng Qian Xinyan Lv Jie Cao Yuan Jiang Yuan Jiang |
| author_sort | Guanbo Zhou |
| collection | DOAJ |
| description | In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting. |
| first_indexed | 2024-04-11T14:14:12Z |
| format | Article |
| id | doaj.art-023bdafc12df4bd0abee240f412e343c |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-11T14:14:12Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-023bdafc12df4bd0abee240f412e343c2022-12-22T04:19:35ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-09-011010.3389/feart.2022.974497974497Application of artificial intelligence technology in typhoon monitoring and forecastingGuanbo Zhou0Xiang Fang1Qifeng Qian2Xinyan Lv3Jie Cao4Yuan Jiang5Yuan Jiang6National Meteorological Center, China Meteorological Administration, Beijing, ChinaNational Meteorological Center, China Meteorological Administration, Beijing, ChinaNational Meteorological Center, China Meteorological Administration, Beijing, ChinaNational Meteorological Center, China Meteorological Administration, Beijing, ChinaKey Laboratory of Meteorological Disaster (KLME), Ministry of Education & Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, ChinaEarth System Modeling and Prediction Centre (CEMC) of China Meteorological Administration, Beijing, ChinaState Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, ChinaIn recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting.https://www.frontiersin.org/articles/10.3389/feart.2022.974497/fulldeep learningtyphoon vortex identificationdetermining typhoon location and intensityrapid intensificationsatellite image |
| spellingShingle | Guanbo Zhou Xiang Fang Qifeng Qian Xinyan Lv Jie Cao Yuan Jiang Yuan Jiang Application of artificial intelligence technology in typhoon monitoring and forecasting Frontiers in Earth Science deep learning typhoon vortex identification determining typhoon location and intensity rapid intensification satellite image |
| title | Application of artificial intelligence technology in typhoon monitoring and forecasting |
| title_full | Application of artificial intelligence technology in typhoon monitoring and forecasting |
| title_fullStr | Application of artificial intelligence technology in typhoon monitoring and forecasting |
| title_full_unstemmed | Application of artificial intelligence technology in typhoon monitoring and forecasting |
| title_short | Application of artificial intelligence technology in typhoon monitoring and forecasting |
| title_sort | application of artificial intelligence technology in typhoon monitoring and forecasting |
| topic | deep learning typhoon vortex identification determining typhoon location and intensity rapid intensification satellite image |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.974497/full |
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