Precipitation nowcasting diffusion model based on turbulence theory and multi-source data Article Swipe
Dawei Li
,
Kefeng Deng
,
Di Zhang
,
Hongze Leng
,
Kaijun Ren
,
Junqiang Song
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu25-5319
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu25-5319
Precipitation nowcasting is a long-standing challenge due to the inherent unpredictability, which often lead to significant risks and damage. While traditional approaches focus on modeling the nonlinear relationship between initial precipitation states and future states, these methods often fail to capture accurate precipitation dynamics, such as its distribution and intensity. The absence of guidance from physical theory limits data-driven methods in disclosing the chaotic nature of precipitation. To address this, we integrate Prandtl’s mixing length theory from fluid dynamics with diffusion models commonly used in computer vision to enhance the prediction of precipitation distributions and details over the next 200 minutes. This integration accounts for the turbulent properties of precipitation, improving both accuracy and granularity in forecasts. Additionally, we leverage multi-source data, particularly lightning observations, to train a control network for our diffusion model. This enhancement allows for more accurate and controllable predictions of precipitation initiation, decay, and overall spatial-temporal patterns. Our approach advances the state of the art in precipitation nowcasting, offering a robust framework that bridges physical theory with modern deep learning techniques.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu25-5319
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408439310
All OpenAlex metadata
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https://openalex.org/W4408439310Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu25-5319Digital Object Identifier
- Title
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Precipitation nowcasting diffusion model based on turbulence theory and multi-source dataWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-03-14Full publication date if available
- Authors
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Dawei Li, Kefeng Deng, Di Zhang, Hongze Leng, Kaijun Ren, Junqiang SongList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-egu25-5319Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/egusphere-egu25-5319Direct OA link when available
- Concepts
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Nowcasting, Precipitation, Diffusion, Diffusion theory, Turbulence, Meteorology, Environmental science, Econometrics, Climatology, Atmospheric sciences, Statistical physics, Geology, Geography, Thermodynamics, Economics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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