Adaptive Bias Correction for Improved Subseasonal Forecasting

TL;DR

This paper introduces an adaptive bias correction method that combines dynamical model forecasts with observations using machine learning, significantly improving subseasonal temperature and precipitation predictions in the US.

Contribution

The paper presents a novel adaptive bias correction technique that enhances subseasonal weather forecasts by integrating dynamical models with observational data through machine learning.

Findings

Temperature forecast skill improved by 60-90%.

Precipitation forecast skill improved by 40-69%.

Practical workflow for explaining and optimizing ABC skill gains.

Abstract

Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over baseline skills of 0.11-0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.