Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network
Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional...
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| Autor*in: |
Yu, Wenbin [verfasserIn] Li, Yangsong [verfasserIn] Fan, Cheng [verfasserIn] Fu, Daoyong [verfasserIn] Zhang, Chengjun [verfasserIn] Chen, Yadang [verfasserIn] Qian, Ming [verfasserIn] Liu, Jie [verfasserIn] Liu, Gaoping [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Earth science informatics - Springer Berlin Heidelberg, 2008, 17(2024), 5 vom: 23. Juli, Seite 4739-4755 |
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| Übergeordnetes Werk: |
volume:17 ; year:2024 ; number:5 ; day:23 ; month:07 ; pages:4739-4755 |
| Links: |
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| DOI / URN: |
10.1007/s12145-024-01412-5 |
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SPR057819998 |
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| 520 | |a Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. | ||
| 650 | 4 | |a Precipitation nowcasting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spatial correlation features |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spatial attention mechanism |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep spatio-temporal fusion networks |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Liu, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Gaoping |e verfasserin |4 aut | |
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10.1007/s12145-024-01412-5 doi (DE-627)SPR057819998 (SPR)s12145-024-01412-5-e DE-627 ger DE-627 rakwb eng 550 004 VZ 550 VZ Yu, Wenbin verfasserin aut Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 Li, Yangsong verfasserin aut Fan, Cheng verfasserin aut Fu, Daoyong verfasserin aut Zhang, Chengjun verfasserin aut Chen, Yadang verfasserin aut Qian, Ming verfasserin aut Liu, Jie verfasserin aut Liu, Gaoping verfasserin aut Enthalten in Earth science informatics Springer Berlin Heidelberg, 2008 17(2024), 5 vom: 23. Juli, Seite 4739-4755 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:17 year:2024 number:5 day:23 month:07 pages:4739-4755 https://dx.doi.org/10.1007/s12145-024-01412-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 5 23 07 4739-4755 |
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10.1007/s12145-024-01412-5 doi (DE-627)SPR057819998 (SPR)s12145-024-01412-5-e DE-627 ger DE-627 rakwb eng 550 004 VZ 550 VZ Yu, Wenbin verfasserin aut Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 Li, Yangsong verfasserin aut Fan, Cheng verfasserin aut Fu, Daoyong verfasserin aut Zhang, Chengjun verfasserin aut Chen, Yadang verfasserin aut Qian, Ming verfasserin aut Liu, Jie verfasserin aut Liu, Gaoping verfasserin aut Enthalten in Earth science informatics Springer Berlin Heidelberg, 2008 17(2024), 5 vom: 23. Juli, Seite 4739-4755 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:17 year:2024 number:5 day:23 month:07 pages:4739-4755 https://dx.doi.org/10.1007/s12145-024-01412-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 5 23 07 4739-4755 |
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10.1007/s12145-024-01412-5 doi (DE-627)SPR057819998 (SPR)s12145-024-01412-5-e DE-627 ger DE-627 rakwb eng 550 004 VZ 550 VZ Yu, Wenbin verfasserin aut Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 Li, Yangsong verfasserin aut Fan, Cheng verfasserin aut Fu, Daoyong verfasserin aut Zhang, Chengjun verfasserin aut Chen, Yadang verfasserin aut Qian, Ming verfasserin aut Liu, Jie verfasserin aut Liu, Gaoping verfasserin aut Enthalten in Earth science informatics Springer Berlin Heidelberg, 2008 17(2024), 5 vom: 23. Juli, Seite 4739-4755 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:17 year:2024 number:5 day:23 month:07 pages:4739-4755 https://dx.doi.org/10.1007/s12145-024-01412-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 5 23 07 4739-4755 |
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10.1007/s12145-024-01412-5 doi (DE-627)SPR057819998 (SPR)s12145-024-01412-5-e DE-627 ger DE-627 rakwb eng 550 004 VZ 550 VZ Yu, Wenbin verfasserin aut Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 Li, Yangsong verfasserin aut Fan, Cheng verfasserin aut Fu, Daoyong verfasserin aut Zhang, Chengjun verfasserin aut Chen, Yadang verfasserin aut Qian, Ming verfasserin aut Liu, Jie verfasserin aut Liu, Gaoping verfasserin aut Enthalten in Earth science informatics Springer Berlin Heidelberg, 2008 17(2024), 5 vom: 23. Juli, Seite 4739-4755 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:17 year:2024 number:5 day:23 month:07 pages:4739-4755 https://dx.doi.org/10.1007/s12145-024-01412-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 5 23 07 4739-4755 |
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10.1007/s12145-024-01412-5 doi (DE-627)SPR057819998 (SPR)s12145-024-01412-5-e DE-627 ger DE-627 rakwb eng 550 004 VZ 550 VZ Yu, Wenbin verfasserin aut Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 Li, Yangsong verfasserin aut Fan, Cheng verfasserin aut Fu, Daoyong verfasserin aut Zhang, Chengjun verfasserin aut Chen, Yadang verfasserin aut Qian, Ming verfasserin aut Liu, Jie verfasserin aut Liu, Gaoping verfasserin aut Enthalten in Earth science informatics Springer Berlin Heidelberg, 2008 17(2024), 5 vom: 23. Juli, Seite 4739-4755 (DE-627)565515772 (DE-600)2423990-2 1865-0481 nnns volume:17 year:2024 number:5 day:23 month:07 pages:4739-4755 https://dx.doi.org/10.1007/s12145-024-01412-5 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4315 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 17 2024 5 23 07 4739-4755 |
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Yu, Wenbin @@aut@@ Li, Yangsong @@aut@@ Fan, Cheng @@aut@@ Fu, Daoyong @@aut@@ Zhang, Chengjun @@aut@@ Chen, Yadang @@aut@@ Qian, Ming @@aut@@ Liu, Jie @@aut@@ Liu, Gaoping @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057819998</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241017064755.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241017s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12145-024-01412-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057819998</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12145-024-01412-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yu, Wenbin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Precipitation nowcasting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial correlation features</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep spatio-temporal fusion networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Yangsong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fan, Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Daoyong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Chengjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yadang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Ming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Gaoping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Earth science informatics</subfield><subfield code="d">Springer Berlin Heidelberg, 2008</subfield><subfield code="g">17(2024), 5 vom: 23. 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Yu, Wenbin |
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Yu, Wenbin ddc 550 misc Precipitation nowcasting misc Spatial correlation features misc Spatial attention mechanism misc Deep spatio-temporal fusion networks Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network |
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550 004 VZ 550 VZ Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network Precipitation nowcasting (dpeaa)DE-He213 Spatial correlation features (dpeaa)DE-He213 Spatial attention mechanism (dpeaa)DE-He213 Deep spatio-temporal fusion networks (dpeaa)DE-He213 |
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Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network |
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Yu, Wenbin Li, Yangsong Fan, Cheng Fu, Daoyong Zhang, Chengjun Chen, Yadang Qian, Ming Liu, Jie Liu, Gaoping |
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precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network |
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Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network |
| abstract |
Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Precipitation nowcasting is crucial for various applications. However, existing deep learning models for meteorological applications face challenges regarding training efficiency, generalization of spatial features, and capturing long-range spatial dependencies. In particular, convolutional neural networks struggle to describe the complete spatial dependencies in radar echo reflectivity image sequences, making it difficult to model spatial features effectively. Additionally, current approaches using Encoder-Decoder structures based on recurrent neural networks have limited success in capturing global spatial dependencies and trajectory motion features in radar echo reflectivity images, especially for medium to high-intensity precipitation nowcasting. This paper addresses these issues by proposing a feature extraction method based on spatial correlation (FESC) and an end-to-end deep spatio-temporal fusion network (DST-FN) for precipitation nowcasting. FESC divides regions based on spatial correlation features extracted from radar echo reflectivity image sequences, improving the model’s understanding and prediction ability of meteorological data. We also introduce a Spatial Attention Mechanism (SAM) module into the TrajGRU model for better performance by adding a new memory channel. The proposed DST-FN framework utilizes the features extracted by FESC and temporal information, overcoming the limitations of encoding-decoding structures in precipitation nowcasting. Our approach demonstrates improved efficiency and effectiveness in capturing complex spatio-temporal dynamics compared to existing deep learning models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.4008636 |