Research

Speaker: Frederick Iat Hin Tam - University of Lausanne, Switzerland

Recent advancements in machine learning (ML) have led to data-driven weatherprediction models that rival or outperform state-of-the-art numerical weather predic- tion models in short— to medium-range forecasts. In order to expand the utility of ML tools in weather prediction and research, practitioners need to have high trust in these tools. The adoption of Explainable Artificial Intelligence (XAI) tools is critical as they show how the ML models make their predictions. Furthermore, it is important to show that ML models can be used to obtain new physical insights on different weather forecasting problems, such as the intensification of tropical cyclones.