AI-Informed Model Analogs for Subseasonal-to-Seasonal Prediction
This work addresses a challenging forecasting problem for public health and agriculture, but it is incremental as it applies an existing method to new timescales and tasks.
The paper tackled subseasonal-to-seasonal forecasting by using an interpretable AI-informed model analog approach to improve predictions across three tasks, such as temperature and wind classification, and found it outperformed traditional methods and baselines with better skill metrics and uncertainty representation.
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.