A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation
Precipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolat...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10138400/ |
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| author | Jinliang Yao Feifan Xu Zheng Qian Zhipeng Cai |
| author_facet | Jinliang Yao Feifan Xu Zheng Qian Zhipeng Cai |
| author_sort | Jinliang Yao |
| collection | DOAJ |
| description | Precipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolation are mostly based on ConvRNNs. Unfortunately, as lead time increases, these methods unavoidably suffer from the problem that high reflectivity values are underestimated. Therefore, we propose a forecast-refinement neural network based on DyConvGRU and U-Net to improve the predicting ability for high reflectivity during radar echo extrapolation. To improve the model’s ability to predict high reflectivities, dynamic convolution, and the forecast-refinement architecture are applied. And to obtain more realistic results, the WGAN’s training strategy is adopted to train the forecast module and the refinement module. Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR). |
| format | Article |
| id | doaj-art-c673c99a7e4946c094d87db186f4a4a9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c673c99a7e4946c094d87db186f4a4a92025-08-20T02:16:40ZengIEEEIEEE Access2169-35362023-01-0111532495326110.1109/ACCESS.2023.328093210138400A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo ExtrapolationJinliang Yao0https://orcid.org/0000-0003-4689-3302Feifan Xu1https://orcid.org/0009-0002-6959-7303Zheng Qian2https://orcid.org/0000-0003-4904-2984Zhipeng Cai3https://orcid.org/0000-0003-1844-3646School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaNingbo Meteorological Service Center, Ningbo, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaPrecipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolation are mostly based on ConvRNNs. Unfortunately, as lead time increases, these methods unavoidably suffer from the problem that high reflectivity values are underestimated. Therefore, we propose a forecast-refinement neural network based on DyConvGRU and U-Net to improve the predicting ability for high reflectivity during radar echo extrapolation. To improve the model’s ability to predict high reflectivities, dynamic convolution, and the forecast-refinement architecture are applied. And to obtain more realistic results, the WGAN’s training strategy is adopted to train the forecast module and the refinement module. Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).https://ieeexplore.ieee.org/document/10138400/Spatiotemporal sequence predictionradar echo extrapolationDyConvGRUU-Netdynamic convolutionWGAN |
| spellingShingle | Jinliang Yao Feifan Xu Zheng Qian Zhipeng Cai A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation IEEE Access Spatiotemporal sequence prediction radar echo extrapolation DyConvGRU U-Net dynamic convolution WGAN |
| title | A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation |
| title_full | A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation |
| title_fullStr | A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation |
| title_full_unstemmed | A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation |
| title_short | A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation |
| title_sort | forecast refinement neural network based on dyconvgru and u net for radar echo extrapolation |
| topic | Spatiotemporal sequence prediction radar echo extrapolation DyConvGRU U-Net dynamic convolution WGAN |
| url | https://ieeexplore.ieee.org/document/10138400/ |
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