Improved extended-range prediction of persistent stratospheric perturbations using machine learning

On average every 2 years, the stratospheric polar vortex exhibits extreme perturbations known as sudden stratospheric warmings (SSWs). The impact of these events is not limited to the stratosphere: but they can also influence the weather at the surface of the Earth for up to 3 months after their occurrence. This downward effect is observed in particular for SSW events with extended recovery timescales. This long-lasting stratospheric impact on surface weather can be leveraged to significantly improve the performance of weather forecasts on timescales of weeks to months. In this paper, we present a fully data-driven procedure to improve the performance of long-range forecasts of the stratosphere around SSW events with an extended recovery. We first use unsupervised machine learning algorithms to capture the spatio-temporal dynamics of SSWs and to create a continuous scale index measuring both the frequency and the strength of persistent stratospheric perturbations. We then uncover three-dimensional spatial patterns maximizing the correlation with positive index values, allowing us to assess when and where statistically significant early signals of SSW occurrence can be found. Finally, we propose two machine learning (ML) forecasting models as competitors for the state-of-the-art sub-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) numerical prediction model S2S (sub-seasonal to seasonal): while the numerical model performs better for lead times of up to 25 d, the ML models offer better predictive performance for greater lead times. We leverage our best-performing ML forecasting model to successfully post-process numerical ensemble forecasts and increase their performance by up to 20%.