Power Ensemble Aggregation for Improved Extreme Event AI Prediction

TL;DR

This paper introduces a power mean ensemble aggregation method that significantly improves the accuracy of machine learning models in predicting extreme heat events, outperforming traditional averaging techniques.

Contribution

It proposes a novel non-linear aggregation approach using power means to enhance climate extreme event predictions in machine learning models.

Findings

Power mean aggregation improves prediction accuracy for heat waves.

Optimal performance varies with the quantile threshold.

Method outperforms traditional mean aggregation.

Abstract

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.