Extreme Precipitation Nowcasting using Multi-Task Latent Diffusion Models Article Swipe
Chaorong Li
,
Ling Xu-dong
,
Qiang Yang
,
Fengqing Qin
,
Yuanyuan Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.14103
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.14103
Deep learning models have achieved remarkable progress in precipitation prediction. However, they still face significant challenges in accurately capturing spatial details of radar images, particularly in regions of high precipitation intensity. This limitation results in reduced spatial localization accuracy when predicting radar echo images across varying precipitation intensities. To address this challenge, we propose an innovative precipitation prediction approach termed the Multi-Task Latent Diffusion Model (MTLDM). The core idea of MTLDM lies in the recognition that precipitation radar images represent a combination of multiple components, each corresponding to different precipitation intensities. Thus, we adopt a divide-and-conquer strategy, decomposing radar images into several sub-images based on their precipitation intensities and individually modeling these components. During the prediction stage, MTLDM integrates these sub-image representations by utilizing a trained latent-space rainfall diffusion model, followed by decoding through a multi-task decoder to produce the final precipitation prediction. Experimental evaluations conducted on the MRMS dataset demonstrate that the proposed MTLDM method surpasses state-of-the-art techniques, achieving a Critical Success Index (CSI) improvement of 13-26%.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.14103
- https://arxiv.org/pdf/2410.14103
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404312771
All OpenAlex metadata
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https://openalex.org/W4404312771Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.14103Digital Object Identifier
- Title
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Extreme Precipitation Nowcasting using Multi-Task Latent Diffusion ModelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-18Full publication date if available
- Authors
-
Chaorong Li, Ling Xu-dong, Qiang Yang, Fengqing Qin, Yuanyuan HuangList of authors in order
- Landing page
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https://arxiv.org/abs/2410.14103Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.14103Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.14103Direct OA link when available
- Concepts
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Nowcasting, Precipitation, Climatology, Diffusion, Computer science, Environmental science, Econometrics, Geography, Meteorology, Mathematics, Geology, Thermodynamics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.representations | 121 |
| abstract_inverted_index.state-of-the-art | 157 |
| abstract_inverted_index.divide-and-conquer | 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |