Subseasonal Prediction of Consecutive Dry Days in Southern Norway using Flow Regimes within a Bayesian Framework

Outlooks of drier or wetter condition few weeks ahead has significant societal applications. Skillfully forecasting such conditions can enhance the climate prediction services for the agricultural sectors, provide drought outlooks and identify potential windows of wildfire risk. However, conventional numerical weather prediction (NWP) and emerging artificial intelligence (AI) methods have shown limited skill at this lead time, particularly for water-related variables. Hybrid methods, which blends observational data and numerical model using data-driven approach, have demonstrated potential to improve the skill of the forecast at these timescales. Additionally, identifying flow regime is also a widely used method to provide an outlook of temperature and precipitation in the extended range.

In this study, we combine both hybrid and flow regime approach to predict consecutive days without rain within a week. We first use machine learning to identify large-scale flow regimes that modulates weekly precipitation. Subsequently, a Bayesian Framework is employed to infer the posterior distribution of the predictand. This is done by updating the per-grid prior distribution of the predictand using two likelihood components: one derived from preceding large-scale regimes and another based on instantaneous flow regimes provided by extended-range forecasts from the Integrated Forecasting System (IFS).