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 …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …

While global warming has generally increased the occurrence of extreme precipitation, the
physical mechanisms by which climate change alters regional and local precipitation …

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience
because of their ability to capture nonlinear system behavior and extract predictive …

The gap in resolution between existing global climate model output and that sought by
decision-makers drives an ongoing need for climate downscaling. Here we test the extent to …

Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …

Machine learning (ML) algorithms are powerful tools to build models of clouds and climate
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …

Abstract The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is
a community effort to systematically assess how the uncertainties from AR detectors …

Promising new opportunities to apply artificial intelligence (AI) to the Earth and
environmental sciences are identified, informed by an overview of current efforts in the …