Regional High-Resolution Weather Forecasting over the Arabian Peninsula: A Data-Driven Approach

Accurate high-resolution spatio-temporal weather forecasting is vital for advancing our understanding of regional weather dynamics and improving meteorological applications. Traditional forecasting relies on numerical weather prediction (NWP) models, which are computationally demanding, particularly when implemented for large domains and high-resolution grids. Recently, Deep Learning (DL) has emerged as a powerful alternative, leveraging historical data to identify patterns and predict future atmospheric conditions. In this work, we develop a regional DL-based forecasting system tailored for the Arabian Peninsula (AP), a region with unique climatic conditions characterized by extreme temperatures and high wind energy potential. Therefore, it serves as an ideal case study for regional weather forecasting. The developed system forecasts hourly meteorological variables such as wind speed, wind direction, and temperature at a 5 km spatial resolution up to 48 hours ahead, with a focus on key vertical levels relevant to wind energy applications. Two forecasting approaches are explored: recursive forecasting, which iteratively advances fine-scale spatio-temporal states over time, and downscaling, which refines coarse-resolution forecasts of the meteorological variables into their high-resolution counterparts.  Additionally, we propose a combined approach that integrates these methods by combining fine-scale dynamics propagation with coarse-scale to fine-scale refinement. The frameworks were evaluated both qualitatively and quantitatively, demonstrating that while recursive forecasting accumulates errors over time, the downscaling approach effectively produces high-resolution forecasts. The combined approach significantly improves the forecasting precision, offering robust performance at early time steps and reduced error accumulation over extended forecasting horizons.