Neural Generative Weather Forecasting

cyvy Research Project

In recent decades, meteorologists have consistently improved weather forecasting systems, which have thus become increasingly complex. Sophisticated systems, such as the Consortium for Small Scale Modeling (COSMO) model, incorporate influences such as local topographical, soil, and vegetation properties. Despite these advances, the data remains  approximate because of spatio-temporal differences as well as interactions and influences that either cannot be observed or have not been taken into account. With their Distributed, Spatio-Temporal Graph Artificial Neural Network Architecture (DISTANA), Professor Martin Butz and his Neuro-Cognitive Modeling Group at the University of Tübingen’s Department of Computer Science have now developed a new approach, which may either enhance or serve as an alternative to traditional forecasting systems.

DISTANA applies inductive learning biases, which implement universal principles of weather dynamics, including the principle that system dynamics can be influenced by only partially observable or even fully unknown, but universally applicable local factors, and the principle that the propagation of weather dynamics over space is restricted to local neighborhoods in instances where temporal intervals are sufficiently small.

Over the course of this project, the researchers will be both developing combined weather prediction datasets as benchmarks and enhancing DISTANA. Ultimately, they expect DISTANA to outperform state-of-the-art weather forecasting systems, as it will be able to take unknown factors into account. Once successfully trained, DISTANA may be useful for weather forecasting on various spatio-temporal scales, potentially enabling better predictions of extreme weather events. This would in turn make it possible to take preventive measures accordingly. Additionally, the principle behind DISTANA may be applied in other areas, for instance water flow prediction, erosion modeling, or output prediction for wind park turbines. In the long term, this research could contribute to informing actions that aim to alleviate the negative impact of climate change.