A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting
Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dan Niu [verfasserIn] Hongshu Che [verfasserIn] Chunlei Shi [verfasserIn] Zengliang Zang [verfasserIn] Hongbin Wang [verfasserIn] Xunlai Chen [verfasserIn] Qunbo Huang [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
Deep residual spatiotemporal attention (DRSTA) |
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| Übergeordnetes Werk: |
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 16(2023), Seite 8286-8296 |
|---|---|
| Übergeordnetes Werk: |
volume:16 ; year:2023 ; pages:8286-8296 |
| Links: |
|---|
| DOI / URN: |
10.1109/JSTARS.2023.3310361 |
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| Katalog-ID: |
DOAJ095923322 |
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| 520 | |a Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. | ||
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| 700 | 0 | |a Qunbo Huang |e verfasserin |4 aut | |
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10.1109/JSTARS.2023.3310361 doi (DE-627)DOAJ095923322 (DE-599)DOAJ17674a71cb534dfdbb3476d841fb14c2 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Dan Niu verfasserin aut A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. Deep residual spatiotemporal attention (DRSTA) heterogeneous spatiotemporal (ST) data precipitation nowcasting ST diffusion Ocean engineering Geophysics. Cosmic physics Hongshu Che verfasserin aut Chunlei Shi verfasserin aut Zengliang Zang verfasserin aut Hongbin Wang verfasserin aut Xunlai Chen verfasserin aut Qunbo Huang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 8286-8296 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:8286-8296 https://doi.org/10.1109/JSTARS.2023.3310361 kostenfrei https://doaj.org/article/17674a71cb534dfdbb3476d841fb14c2 kostenfrei https://ieeexplore.ieee.org/document/10234590/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 8286-8296 |
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10.1109/JSTARS.2023.3310361 doi (DE-627)DOAJ095923322 (DE-599)DOAJ17674a71cb534dfdbb3476d841fb14c2 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Dan Niu verfasserin aut A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. Deep residual spatiotemporal attention (DRSTA) heterogeneous spatiotemporal (ST) data precipitation nowcasting ST diffusion Ocean engineering Geophysics. Cosmic physics Hongshu Che verfasserin aut Chunlei Shi verfasserin aut Zengliang Zang verfasserin aut Hongbin Wang verfasserin aut Xunlai Chen verfasserin aut Qunbo Huang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 8286-8296 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:8286-8296 https://doi.org/10.1109/JSTARS.2023.3310361 kostenfrei https://doaj.org/article/17674a71cb534dfdbb3476d841fb14c2 kostenfrei https://ieeexplore.ieee.org/document/10234590/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 8286-8296 |
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10.1109/JSTARS.2023.3310361 doi (DE-627)DOAJ095923322 (DE-599)DOAJ17674a71cb534dfdbb3476d841fb14c2 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Dan Niu verfasserin aut A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. Deep residual spatiotemporal attention (DRSTA) heterogeneous spatiotemporal (ST) data precipitation nowcasting ST diffusion Ocean engineering Geophysics. Cosmic physics Hongshu Che verfasserin aut Chunlei Shi verfasserin aut Zengliang Zang verfasserin aut Hongbin Wang verfasserin aut Xunlai Chen verfasserin aut Qunbo Huang verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 8286-8296 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:8286-8296 https://doi.org/10.1109/JSTARS.2023.3310361 kostenfrei https://doaj.org/article/17674a71cb534dfdbb3476d841fb14c2 kostenfrei https://ieeexplore.ieee.org/document/10234590/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 8286-8296 |
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TC1501-1800 QC801-809 A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting Deep residual spatiotemporal attention (DRSTA) heterogeneous spatiotemporal (ST) data precipitation nowcasting ST diffusion |
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heterogeneous spatiotemporal attention fusion prediction network for precipitation nowcasting |
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A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting |
| abstract |
Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. |
| abstractGer |
Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. |
| abstract_unstemmed |
Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time. |
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A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting |
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