Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Climate science has been revolutionized by the combined effects of an exponential growth
in computing power, which has enabled more sophisticated and higher-resolution …

The 1960s was an exciting era for atmospheric predictability research: a finite predictability
of the atmosphere was uncovered using Lorenz's models and the well-acknowledged …

Diffusion models achieve state-of-the-art generation quality across many applications, but
their ability to capture rare or extreme events in heavy-tailed distributions remains unclear. In …

This study develops an explainable variational autoencoder (VAE) framework to efficiently
generate high‐fidelity local circulation patterns in Taiwan, ensuring an accurate …

Accurately retrieving surface meteorological states at arbitrary locations is of great
application significance in weather forecasting and climate modeling. Since meteorological …

We introduce a transformer-based neural network to generate high-resolution (3km)
synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims …

Despite tremendous progress in developing deep-learning-based weather forecasting
systems, their design space, including the impact of different design choices, is yet to be well …

High-resolution climate simulations are very valuable for understanding climate change
impacts and planning adaptation measures. This has motivated use of regional climate …

We address the essential role of information retrieval in enhancing climate downscaling,
focusing on the need for high-resolution datasets and the application of deep learning …