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Mike Pritchard
Director of Climate Simulation Research
NVIDIA Research
Interests
Accelerating cloud resolving climate simulations with physics-informed machine learning
Reinforcement learning for climate model calibration
Limitations of autoregressive weather simulations trained on observational data
AI-assisted analysis of large high-resolution climate datasets
Latest
NVIDIA Launches Earth-2 Family of Open Models — the World's First Fully Open, Accelerated Set of Models and Tools for AI Weather
Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting
HealDA: Highlighting the Importance of Initial Errors in End-to-End AI Weather Forecasts
Learning Accurate Storm-Scale Evolution from Observations
Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Predict Extreme Weather Events in Minutes Without a Supercomputer
ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation
Adaptive Flow Matching for Resolving Small-Scale Physics
Heavy-Tailed Diffusion Models
AI Chases the Storm: New NVIDIA Research Boosts Weather Prediction, Climate Simulation
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
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