Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

Abstract Machine learning approaches are increasingly used to extract patterns and insights
from the ever-increasing stream of geospatial data, but current approaches may not be …

Weather forecasting is important for science and society. At present, the most accurate
forecast system is the numerical weather prediction (NWP) method, which represents …

Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …

Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …

Conventionally, Earth system (eg, weather and climate) forecasting relies on numerical
simulation with complex physical models and hence is both expensive in computation and …

Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

In this paper, we present Pangu-Weather, a deep learning based system for fast and
accurate global weather forecast. For this purpose, we establish a data-driven environment …

Weather forecasting is dominated by numerical weather prediction that tries to model
accurately the physical properties of the atmosphere. A downside of numerical weather …