Summary of Improving Sub-seasonal Wind-speed Forecasts in Europe with a Non-linear Model, by Ganglin Tian (1) et al.
Improving sub-seasonal wind-speed forecasts in Europe with a non-linear model
by Ganglin Tian, Camille Le Coz, Anastase Alexandre Charantonis, Alexis Tantet, Naveen Goutham, Riwal Plougonven
First submitted to arxiv on: 28 Nov 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 The proposed framework leverages non-linear relationships between 500 hPa geopotential height (Z500) and surface wind speed to improve subs-seasonal wind speed forecasting skills in Europe. It uses a Multiple Linear Regression (MLR) or a Convolutional Neural Network (CNN) to regress surface wind speed from Z500, with the CNN performing better due to its non-linearity. The study demonstrates the advantages of non-linearity using various verification metrics on ERA5 reanalysis and sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts. However, introducing stochastic perturbations helps compensate for the under-dispersive issue by addressing the insufficient spread in statistical models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sub-seasonal wind speed forecasts are important for planning and operating wind power systems. The problem is that forecasting skills decrease sharply after two weeks. This study tries to improve these forecasts by using relationships between large-scale variables like Z500 and surface winds. It uses machine learning models like MLR and CNN to make predictions, with the CNN doing better because it can handle non-linear relationships. The study shows that introducing some randomness into the model helps make the forecasts more accurate. |
Keywords
» Artificial intelligence » Cnn » Linear regression » Machine learning » Neural network
