Summary of Postcast: Generalizable Postprocessing For Precipitation Nowcasting Via Unsupervised Blurriness Modeling, by Junchao Gong et al.
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling
by Junchao Gong, Siwei Tu, Weidong Yang, Ben Fei, Kun Chen, Wenlong Zhang, Xiaokang Yang, Wanli Ouyang, Lei Bai
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Precipitation nowcasting is crucial for severe convective weather warnings. Deep learning approaches have achieved significant progress by exploiting spatiotemporal correlations. However, these methods still suffer from blurriness as lead time increases, hindering accurate predictions of extreme precipitation. To address this issue, researchers explore generative models conditioned on blurry predictions. Our proposed unsupervised postprocessing method eliminates blurriness without requiring pairs of blurry predictions and corresponding ground truth. We utilize blurry predictions to guide a pre-trained unconditional denoising diffusion probabilistic model (DDPM) to obtain high-fidelity predictions with eliminated blurriness. A zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are introduced to adapt the unconditional DDPM to varying blurriness modes in precipitation nowcasting datasets. Our method demonstrates generality and superiority on 7 precipitation radar datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Precipitation nowcasting is important for warning people about severe weather. Researchers have made progress using deep learning, but there’s still a problem: predictions get blurry as time goes on. To fix this, we developed a new method that doesn’t need special training data. We use blurry predictions to help a pre-trained model make better predictions with less blur. Our method works well on different weather datasets and is more accurate than other approaches. |
Keywords
» Artificial intelligence » Deep learning » Diffusion » Probabilistic model » Spatiotemporal » Unsupervised » Zero shot
