Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast
The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. He...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2021.760766/full |
| _version_ | 1819282357695283200 |
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| author | Wei Jin Wei Zhang Jie Hu Bin Weng Tianqiang Huang Jiazhen Chen |
| author_facet | Wei Jin Wei Zhang Jie Hu Bin Weng Tianqiang Huang Jiazhen Chen |
| author_sort | Wei Jin |
| collection | DOAJ |
| description | The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather. |
| first_indexed | 2024-12-24T01:14:18Z |
| format | Article |
| id | doaj.art-de34d873d5514dd6aa118811729e7229 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-24T01:14:18Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-de34d873d5514dd6aa118811729e72292022-12-21T17:22:48ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-01-01910.3389/feart.2021.760766760766Using the Residual Network Module to Correct the Sub-Seasonal High Temperature ForecastWei Jin0Wei Zhang1Jie Hu2Bin Weng3Tianqiang Huang4Jiazhen Chen5College of Computer and Cyber Security, Fujian Normal University, Fuzhou, ChinaFujian Key Laboratory of Severe Weather, Fujian Institute of Meteorological, Fuzhou, ChinaCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou, ChinaCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou, ChinaCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou, ChinaCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou, ChinaThe high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.https://www.frontiersin.org/articles/10.3389/feart.2021.760766/fulldeep learningsub-seasonhigh temperature error revisiondeterministic forecastingprobabilistic forecasting |
| spellingShingle | Wei Jin Wei Zhang Jie Hu Bin Weng Tianqiang Huang Jiazhen Chen Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast Frontiers in Earth Science deep learning sub-season high temperature error revision deterministic forecasting probabilistic forecasting |
| title | Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast |
| title_full | Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast |
| title_fullStr | Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast |
| title_full_unstemmed | Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast |
| title_short | Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast |
| title_sort | using the residual network module to correct the sub seasonal high temperature forecast |
| topic | deep learning sub-season high temperature error revision deterministic forecasting probabilistic forecasting |
| url | https://www.frontiersin.org/articles/10.3389/feart.2021.760766/full |
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