NVIDIA Climate and Weather Research

Welcome to the homepage of the NVIDIA Climate and Weather Simulation Research Lab, led by Mike Pritchard and founded in 2022. Our mission is to re-imagine technology across the Earth System Modeling stack using new ideas in AI on applied topics such as autoregressive Earth System prediction, hybrid physics-AI climate simulation, atmospheric state estimation, generative data assimilation, convection-permitting high-resolution simulation, steerable climate state sampling – and related frontiers.

Technology developed by our team is integrated into NVIDIA PhysicsNemo and Earth2Studio.

Research Areas

Weather

AI models can out-compete physics-based numerical models for weather prediction tasks. Accurately predicting the evolution of atmospheric state vectors, and evaluating skill based on appropriate probabilistic measures, are essential. Our lab has advanced AI forecasting techniques such as FourCastNet, StormCast and DLESyM and developed associated libraries such as e2grid and CuHPX. Major challenges ahead include scaling to increasingly expressive architectures, multi-modal observational datasets, multi-component Earth System prediction, multi-month prediction time horizons, and advancing the frontier of convective-permitting AI weather prediction for storm-scale dynamics.

Climate

Predicting climate with AI is harder than predicting the weather. Our lab has advanced methods for hybrid physics-AI climate prediction (ClimSim) and autoregressive atmosphere- and ocean-coupled climate prediction (OLA, DLESyM, ACE). Open challenges include controlling drift and stability and evolving multi-component autoregressive AI Earth System models to become fully featured for operational climate prediction under scenarios of changing greenhouse gases, land surface state, aerosols, including associated physical feedbacks from clouds and ecosystems, alongside estimates of their uncertainty.

Control

For informatics use cases, AI samplers provide a way to sidestep challenges of autoregressive prediction by explicitly conditioning future weather samples on climate-controlling factors using generative AI. Climate in a Bottle is a research prototype that demonstrates this alongside 13M-pixel super-resolution at the scale of next-generation global cloud resolving climate modeling complexity. Open challenges include generalizing user-control during sampling and avoiding overfitting to sufficiently learn subtle climate change trends hiding amidst strong internal variability especially when training data are limited in duration but high resolution in space and time.

Downscaling

World governments have produced many coarse-resolution projections of future climate. Super-resolving, i.e. downscaling, these data becomes useful for impacts-relevant planning. Our lab has contributed to AI methods like CorrDiff for multivariate generative super-resolution and new channel synthesis. Key challenges include the high dynamic range and low inter-channel correlations of climate data compared to imagery.

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Recent Posts

NVIDIA Launches Earth-2 Family of Open Models — the World's First Fully Open, Accelerated Set of Models and Tools for AI Weather
NVIDIA announces three new open-source weather AI models: HEAL-DA for global data assimilation, ATLAS for medium-range forecasting, and StormScope for nowcasting.
Jan 26, 2026 0 min read Blog
NVIDIA Launches Earth-2 Family of Open Models — the World's First Fully Open, Accelerated Set of Models and Tools for AI Weather
Predict Extreme Weather Events in Minutes Without a Supercomputer
HENS: Huge ensembles for counterfactual climate extremes powered by NVIDIA FourCastNet.
Sep 19, 2025 0 min read Blog
Predict Extreme Weather Events in Minutes Without a Supercomputer
Forecasting the Weather Beyond Two Weeks Using NVIDIA Earth-2
Using Earth2Studio to Make Atmosphere-Ocean Coupled Multi-Month AI Predictions with DLESyM
Jul 11, 2025 0 min read Blog
Forecasting the Weather Beyond Two Weeks Using NVIDIA Earth-2
See all posts

Meet the Team

Members

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Dale Durran

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Jaideep Pathak

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Mike Pritchard

Director of Climate Simulation Research

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Noah Brenowitz

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Peter Manshausen

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Suman Ravuri

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Zeyuan Hu

Intern Alumni

Discover our talented former interns and their contributions to AI research with applications in the Earth System Sciences.
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Publications

Quickly discover relevant content by filtering publications.
January, 2026
Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
arXiv
January, 2026
HealDA: Highlighting the Importance of Initial Errors in End-to-End AI Weather Forecasts
arXiv
January, 2026
Learning Accurate Storm-Scale Evolution from Observations
arXiv
December, 2025
Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models
arXiv
October, 2025 Journal of Advances in Modeling Earth Systems
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
DOI Paper
September, 2025 Geoscientific Model Development
Huge ensembles -- Part 1: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators
Paper Website
September, 2025 Geoscientific Model Development
Huge Ensembles Part 2: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Paper Website
August, 2025 Journal of Machine Learning Research
ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation
Paper
July, 2025 International Conference on Machine Learning (ICML)
Adaptive Flow Matching for Resolving Small-Scale Physics
arXiv
July, 2025
Climate In A Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
arXiv Website
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