Summary of Improving Probabilistic Forecasts Of Extreme Wind Speeds by Training Statistical Post-processing Models with Weighted Scoring Rules, By Jakob Benjamin Wessel et al.
Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
by Jakob Benjamin Wessel, Christopher A. T. Ferro, Gavin R. Evans, Frank Kwasniok
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 paper aims to enhance statistical post-processing models for predicting extreme wind speeds, with a focus on improving ensemble model output statistics (EMOS) models. To achieve this, the authors modify the training procedure used to fit EMOS models by employing the threshold-weighted continuous ranked probability score (twCRPS), a proper scoring rule that prioritizes predictions above a certain threshold. Experimental results show that this approach leads to enhanced performance for extreme event forecasting, albeit at the expense of reduced accuracy in predicting the distribution’s body. To mitigate this trade-off, the authors introduce weighted training and linear pooling strategies. The study also provides closed-form expressions for the twCRPS under various distributions, offering a valuable resource for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve forecasts of extreme wind speeds by adjusting how statistical post-processing models are trained. Currently, these models can be biased and not very good at predicting the extremes. The authors try a new way of training these models using something called the threshold-weighted continuous ranked probability score (twCRPS). This helps them make better predictions for extreme events, but might not be as good for predicting what’s happening in between. To solve this problem, they suggest ways to balance things out. The study also explains how the twCRPS works and provides some helpful formulas. |
Keywords
» Artificial intelligence » Ensemble model » Probability
