Optimisation of Regional Weather Forecasts for Northern Algeria Using a Convolutional Neural Network and AROME Model Analysis.

This study introduces an innovative approach aimed at enhancing the accuracy of regional weather forecasts from the AROME model, covering northern Algeria. By leveraging AROME analysis, a refined representation based on real observations and widely used for monitoring and validating our model, our primary objective was to precisely correct surface parameters, including temperature at 2 meters, humidity, wind force, and sea-level atmospheric pressure (MSLP). This correction was performed based on their forecast ensemble, all while preserving spatial resolution.

This methodology has yielded promising results, demonstrating a significant improvement in the accuracy of regional weather forecasts. The presentation will delve into the detailed integration process of the Convolutional Neural Network (CNN) and AROME analysis, highlighting the successes achieved in correcting essential surface parameters. These advancements strengthen the reliability of regional meteorological models, with positive implications for resource planning and management in the northern region of Algeria.