AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature Extraction Article Swipe
Liangchao Geng
,
Huantong Geng
,
Jinzhong Min
,
Xiaoran Zhuang
,
Yu Zheng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14205106
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14205106
Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation between radar and rainfall has several parameters in different areas, and because rainfall varies with seasons, traditional methods cannot capture high-resolution spatiotemporal features. Therefore, we propose an attention fusion spatiotemporal residual network (AF-SRNet) to forecast rainfall precisely for the weak continuity of convective precipitation. Specifically, the spatiotemporal residual network is designed to extract the deep spatiotemporal features of radar echo and precipitation data. Then, we combine the radar echo feature and precipitation feature as the input of the decoder through the attention fusion block; after that, the decoder forecasts the rainfall for the next two hours. We train and evaluate our approaches on the historical data from the Jiangsu Meteorological Observatory. The experimental results show that AF-SRNet can effectively utilize multiple inputs and provides more precise nowcasting of convective precipitation.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14205106
- https://www.mdpi.com/2072-4292/14/20/5106/pdf?version=1666592117
- OA Status
- gold
- Cited By
- 9
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304775836
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- OpenAlex ID
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https://openalex.org/W4304775836Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs14205106Digital Object Identifier
- Title
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AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature ExtractionWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-12Full publication date if available
- Authors
-
Liangchao Geng, Huantong Geng, Jinzhong Min, Xiaoran Zhuang, Yu ZhengList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14205106Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/20/5106/pdf?version=1666592117Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/14/20/5106/pdf?version=1666592117Direct OA link when available
- Concepts
-
Nowcasting, Quantitative precipitation forecast, Radar, Precipitation, Residual, Computer science, Feature (linguistics), Meteorology, Environmental science, Weather radar, Quantitative precipitation estimation, Artificial intelligence, Algorithm, Geography, Telecommunications, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
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2025: 2, 2024: 6, 2022: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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