Speaker: Frederick Iat-Hin Tam - University of Lausanne, Switzerland
Recent advancements in machine learning (ML) have led to data-driven weather prediction models that rival or outperform state-of-the-art numerical weather prediction 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. In this two-part presentation, I will present two studies that demonstrate the added value of ML tools in (a) discovering new knowledge on the early intensification of tropical cyclones, and (b) improving existing statistical models for TC intensity forecast.