Summary of Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss For Skillful Precipitation Nowcasting, by Chiu-wai Yan et al.
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting
by Chiu-Wai Yan, Shi Quan Foo, Van Hoan Trinh, Dit-Yan Yeung, Ka-Hing Wong, Wai-Kin Wong
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper proposes a novel deep learning approach, Fourier Amplitude and Correlation Loss (FACL), for precipitation nowcasting. Unlike previous studies that focus on improving pixel-wise metrics, FACL combines two loss terms: Fourier Amplitude Loss (FAL) and Fourier Correlation Loss (FCL). These losses regularize the Fourier amplitude of model predictions and complement missing phase information. The method replaces traditional L2 losses like MSE and weighted MSE for spatiotemporal prediction on signal-based data. Experiments on one synthetic dataset and three radar echo datasets show that FACL improves perceptual metrics, meteorology skill scores, with a small trade-off to pixel-wise accuracy and structural similarity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to predict the weather using computers. Instead of just getting the details right, like previous methods do, this approach focuses on making the predictions look more realistic. It uses two special techniques to make the predicted images look more like real weather radar data. This makes the predictions more useful for people who need to use them to make decisions about the weather. The method is tested on several different types of weather data and shows that it can improve the accuracy of the predictions. |
Keywords
» Artificial intelligence » Deep learning » Mse » Spatiotemporal
