We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …

General circulation models (GCMs) are the foundation of weather and climate prediction,.
GCMs are physics-based simulators that combine a numerical solver for large-scale …

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather
scenarios is crucial for important decisions, from warning the public about hazardous …

Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of
climate change. Recently, data-driven approaches for weather forecasting based on deep …

Climate models have been key for assessing the impact of climate change and simulating
future climate scenarios. The machine learning (ML) community has taken an increased …

The last few years have witnessed the emergence of data-driven weather forecast models
capable of competing with–and, in some respects, outperforming–physics-based numerical …

Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of
extreme events. Constructing an ensemble prediction system (EPS) based on conventional …

We present a parsimonious deep learning weather prediction model to forecast seven
atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km …

Due to insufficient local area information, numerical weather prediction (NWP) may yield
biases for specific areas. Previous studies correct biases mainly by employing handcrafted …