Summary of Deep Learning For Precipitation Nowcasting: a Survey From the Perspective Of Time Series Forecasting, by Sojung An et al.
Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting
by Sojung An, Tae-Jin Oh, Eunha Sohn, Donghyun Kim
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 reviews recent progress in deep learning-based time series precipitation forecasting models, focusing on preprocessing, objective functions, evaluation metrics, and forecasting strategies. The authors investigate recursive and multiple approaches to predict future frames, assess their performance, and discuss limitations and challenges. They also evaluate current models on a public benchmark, providing insights for better understanding and aiding the development of robust AI solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can predict when it will rain or snow in the near future using special kinds of artificial intelligence called deep learning. Right now, these systems are really good at doing this, but there’s still a lot to learn. The authors take a step back and look at all the different ways people have tried to do this kind of prediction, including what they do before predicting, how they decide when it’s correct, and how well they do. They also talk about some challenges and limitations, as well as ideas for making these systems even better in the future. |
Keywords
* Artificial intelligence * Deep learning * Time series
