Extreme Precipitation

Extreme precipitation research focuses on improving the accuracy and resolution of precipitation forecasts, particularly for intense events, to better understand and mitigate their impacts. Current research employs advanced machine learning techniques, including generative models (like diffusion models and transformers), neural networks (e.g., convolutional, recurrent, and attention-augmented architectures), and reinforcement learning, to improve both nowcasting (short-term prediction) and longer-term forecasting of extreme rainfall. These efforts aim to address limitations in existing models, such as underestimation of extreme intensities and inaccurate spatial representation, leading to more reliable predictions for improved disaster preparedness and urban planning. The resulting higher-resolution datasets and improved forecasting capabilities are crucial for assessing localized risks and informing effective adaptation strategies.