Subseasonal Forecast

Subseasonal forecasting aims to predict weather patterns two to six weeks in advance, a crucial timeframe for various societal sectors. Current research heavily utilizes machine learning, employing models like convolutional neural networks and random forests, often in conjunction with or as post-processing tools for existing dynamical models, to improve prediction accuracy of key variables such as temperature and precipitation. This focus on advanced algorithms is driven by the need to overcome persistent biases in traditional models and enhance forecasting skill, particularly for extreme weather events, leading to better informed decision-making in areas like water resource management and disaster preparedness.