Global Forecasting

Global forecasting aims to predict various phenomena across the globe, from weather patterns and air quality to famine risk and forest biomass. Current research heavily utilizes machine learning, employing diverse architectures like deep learning (including Transformers, CNNs, and RNNs), Random Forests, and Gaussian Processes, often integrated with data assimilation techniques to improve accuracy and incorporate real-time observations. These advancements enhance prediction capabilities for diverse applications, improving decision-making in areas such as disaster preparedness, resource management, and public health, while also driving methodological innovation in explainability and interpretability of complex models.