Probabilistic weather forecasting is critical for decision-making in high-impact domains such
as flood forecasting, energy system planning or transportation routing, where quantifying the …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

We introduce generative models for accelerating simulations of high-dimensional systems
through learning and evolving their effective dynamics. In the proposed Generative Learning …

Predictions of weather hazard require expensive km-scale simulations driven by coarser
global inputs. Here, a cost-effective stochastic downscaling model is trained from a high …

The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …

The generation of initial conditions via accurate data assimilation is crucial for reliable
weather forecasting and climate modeling. We propose the DiffDA as a machine learning …

The success of AI models relies on the availability of large, diverse, and high-quality
datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and …

Foundation models have enormous potential in advancing Earth and climate sciences,
however, current approaches may not be optimal as they focus on a few basic features of a …

We introduce deep generative diffusion for multivariate and regional surrogate modeling
learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the …