With the rapid development of artificial intelligence, machine learning is gradually becoming
popular for predictions in all walks of life. In meteorology, it is gradually competing with …

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to
combat global climate change. We identify two crucial opportunities that AI offers in this …

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 …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

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 …

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 …

We present VideoGPT: a conceptually simple architecture for scaling likelihood based
generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled …

Accurate estimation of predictive uncertainty (model calibration) is essential for the safe
application of neural networks. Many instances of miscalibration in modern neural networks …

Existing weather forecasting models are based on physics and use supercomputers to
evolve the atmosphere into the future. Better physics-based forecasts require improved …