Power Ensemble Aggregation for Improved Extreme Event AI Prediction
This work addresses the problem of improving extreme weather prediction for climate science and risk management, representing an incremental advancement in ensemble aggregation methods.
The paper tackles the challenge of predicting climate extreme events like heat waves by framing it as a classification problem to determine if surface air temperature exceeds local quantiles. The key result is that using a power mean to aggregate ensemble predictions significantly improves classifier performance, achieving better accuracy than typical mean predictions, with effectiveness increasing for higher extremes.
This paper addresses the critical challenge of improving predictions of climate extreme events, specifically heat waves, using machine learning methods. Our work is framed as a classification problem in which we try to predict whether surface air temperature will exceed its q-th local quantile within a specified timeframe. Our key finding is that aggregating ensemble predictions using a power mean significantly enhances the classifier's performance. By making a machine-learning based weather forecasting model generative and applying this non-linear aggregation method, we achieve better accuracy in predicting extreme heat events than with the typical mean prediction from the same model. Our power aggregation method shows promise and adaptability, as its optimal performance varies with the quantile threshold chosen, demonstrating increased effectiveness for higher extremes prediction.