Summary of Dune: a Machine Learning Deep Unet++ Based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting, by Pratik Shukla and Milton Halem
DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting
by Pratik Shukla, Milton Halem
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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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 A novel deep-learning architecture is introduced for subseasonal to seasonal and annual mean forecasting of atmospheric and climate fields. The Deep UNet++-based Ensemble (DUNE) neural network employs multi-encoder-decoder structures with residual blocks, leveraging ERA5 monthly averaged long-term data records. Initializing the model from a prior month or year, DUNE produces AI-based global forecasts of 2-meter temperatures and sea surface temperatures. Validation and forecast evaluations are performed over an additional two years, followed by five years to account for natural annual variability. The model generates ensemble seasonal forecasts in seconds, outperforming persistence, climatology, and multiple linear regression. Root Mean Squared Error (RMSE), Anomaly Correlation Coefficient (ACC), and Heidke Skill Score (HSS) statistics are presented globally and regionally. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program uses a special type of artificial intelligence called deep learning to make predictions about the weather over long periods of time. This is different from other weather forecasting methods that use physics-based calculations. The program, called DUNE, can predict temperatures and sea surface temperatures over large areas. It does this by using data from the past 40 years and then making forecasts for an additional two years. These forecasts are more accurate than some existing methods and can be used to make better predictions about the weather. |
Keywords
» Artificial intelligence » Deep learning » Encoder decoder » Linear regression » Neural network » Unet
