CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging
Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant sup...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/14/1/25 |
| _version_ | 1797446330837106688 |
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| author | Yuhang Jiang Wei Cheng Feng Gao Shaoqing Zhang Chang Liu Jingzhe Sun |
| author_facet | Yuhang Jiang Wei Cheng Feng Gao Shaoqing Zhang Chang Liu Jingzhe Sun |
| author_sort | Yuhang Jiang |
| collection | DOAJ |
| description | Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network’s 2-h prediction is 4.77 Kelvin. The network’s predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855. |
| first_indexed | 2024-03-09T13:39:05Z |
| format | Article |
| id | doaj.art-1e980c8d8fdb430bb91bc67b7dee3670 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T13:39:05Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-1e980c8d8fdb430bb91bc67b7dee36702023-11-30T21:08:20ZengMDPI AGAtmosphere2073-44332022-12-011412510.3390/atmos14010025CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation ImagingYuhang Jiang0Wei Cheng1Feng Gao2Shaoqing Zhang3Chang Liu4Jingzhe Sun5College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaBeijing Institute of Applied Meteorology, Beijing 100029, ChinaGeosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network’s 2-h prediction is 4.77 Kelvin. The network’s predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855.https://www.mdpi.com/2073-4433/14/1/25meteorological satelliteconvolutional neural networkimage prediction |
| spellingShingle | Yuhang Jiang Wei Cheng Feng Gao Shaoqing Zhang Chang Liu Jingzhe Sun CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging Atmosphere meteorological satellite convolutional neural network image prediction |
| title | CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging |
| title_full | CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging |
| title_fullStr | CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging |
| title_full_unstemmed | CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging |
| title_short | CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging |
| title_sort | csip net convolutional satellite image prediction network for meteorological satellite infrared observation imaging |
| topic | meteorological satellite convolutional neural network image prediction |
| url | https://www.mdpi.com/2073-4433/14/1/25 |
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