earth2studio and earth2mip

Earth2studio is a general-purpose framework for building and deploying weather/climate AI models, while earth2mip is a specialized research tool for comparing multiple such models against each other, making them complementary tools used together in model development and evaluation workflows.

earth2studio
80
Verified
earth2mip
58
Established
Maintenance 20/25
Adoption 10/25
Maturity 25/25
Community 25/25
Maintenance 10/25
Adoption 10/25
Maturity 16/25
Community 22/25
Stars: 694
Forks: 155
Downloads:
Commits (30d): 42
Language: Python
License: Apache-2.0
Stars: 254
Forks: 54
Downloads:
Commits (30d): 0
Language: Python
License: Apache-2.0
No risk flags
No Package No Dependents

About earth2studio

NVIDIA/earth2studio

Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.

This tool helps meteorologists, climate scientists, and environmental researchers explore, build, and deploy AI models for weather and climate prediction. You can input current atmospheric data from sources like GFS or IFS, choose from a large collection of pre-trained AI models (like FourCastNet3, AIFS, or GraphCast), and then generate future weather forecasts or climate simulations. It's designed for professionals who need to quickly run and customize advanced AI-driven Earth system models.

weather-forecasting climate-modeling atmospheric-science environmental-research geospatial-analysis

About earth2mip

NVIDIA/earth2mip

Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.

This tool helps climate researchers and scientists compare different AI models for weather and climate prediction. You can input various pre-trained AI weather models and historical atmospheric data, then run simulations to see how well each model predicts future weather conditions. It outputs scores and metrics like ACC/RMSE, allowing you to evaluate and understand the strengths and weaknesses of different AI approaches in capturing Earth's atmospheric physics.

climate modeling weather forecasting atmospheric science AI model evaluation geospatial data analysis

Scores updated daily from GitHub, PyPI, and npm data. How scores work