Deep learning has transformed meteorology by greatly improving the accuracy, resolution, and reliability of weather forecasts. This session provides a platform to discuss recent advances in deep learning methods for weather forecasting, focusing on innovations in prediction accuracy, downscaling, bias correction, integration with traditional numerical models, and cutting-edge applications. It will also explore emerging trends in deep learning techniques for weather forecasting. Discussion topics include, but are not limited to:

1. Deep Learning Models for Weather Forecasting: Advances in deep learning models that improve forecast accuracy and reliability, including novel architectures, data assimilation, probabilistic forecasts, and data-driven and hybrid models.
2. Downscaling Methods: Deep learning-based approaches downscale coarse-resolution weather data to create finer, localized forecasts for more precise predictions.
3. Bias Correction Techniques: Deep learning methods for reducing biases in weather forecasts include calibration, error correction, and observational data integration.
4. Integration with Numerical Weather Prediction Models: Combining deep learning with traditional numerical weather prediction models enhances forecast accuracy and predictive capabilities.
5. Applications and Case Studies: Practical applications of deep learning in weather forecasting, such as disaster preparedness, climate modeling, and agricultural planning. Case studies showcase the transformative impact of these methods across various sectors.