AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability

TL;DR

This paper introduces an AI-informed analog forecasting method using neural networks to improve subseasonal-to-seasonal predictions of weather patterns, outperforming traditional methods and enhancing understanding of predictability sources.

Contribution

It demonstrates the effectiveness of an interpretable AI approach for analog forecasting across multiple S2S prediction tasks, with improved accuracy and uncertainty representation.

Findings

AI-informed analogs outperform traditional methods in skill metrics.

Better prediction of temperature extremes and forecast uncertainty.

Learned masks reveal sources of S2S predictability.

Abstract

Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approach, previously employed on longer timescales, to improve S2S predictions. Using an artificial neural network, we learn a mask of weights to optimize analog selection and showcase its versatility across three varied prediction tasks: 1) classification of Week 3-4 Southern California summer temperatures; 2) regional regression of Month 1 midwestern U.S. summer temperatures; and 3) classification of Month 1-2 North Atlantic wintertime upper atmospheric winds. The AI-informed analogs outperform traditional analog forecasting approaches, as well as climatology and persistence baselines, for deterministic and probabilistic skill metrics on both climate model and reanalysis data. We find the analog ensembles built using the AI-informed approach also produce better predictions of temperature extremes and improve representation of forecast uncertainty. Finally, by using an interpretable-AI framework, we analyze the learned masks of weights to better understand S2S sources of predictability.