Earth2Studio

A Python-based package designed to get users up and running with AI weather and climate models fast. Our mission is to enable everyone to build, research and explore AI driven meteorology.
https://github.com/nvidia/earth2studio

Category: Climate Change
Sub Category: Earth and Climate Modeling

Keywords

ai climate-science deep-learning weather

Last synced: about 7 hours ago
JSON representation

Repository metadata

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

Host: GitHub
URL: https://github.com/nvidia/earth2studio
Owner: NVIDIA
License: apache-2.0
Created: 2024-04-05T17:39:51.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2025-04-18T20:22:26.000Z (8 days ago)
Last Synced: 2025-04-19T11:14:46.591Z (8 days ago)
Topics: ai, climate-science, deep-learning, weather
Language: Python
Homepage: https://nvidia.github.io/earth2studio/
Size: 259 MB
Stars: 167
Watchers: 8
Forks: 46
Open Issues: 11
Releases: 5
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff

README.md

Earth2Studio Banner

Earth2Studio is a Python-based package designed to get users up and running
with AI weather and climate models fast.
Our mission is to enable everyone to build, research and explore AI driven meteorology.

- Earth2Studio Documentation -

Install | User-Guide |
Examples | API

Earth2Studio Banner

Quick start

Install Earth2Studio:

# For 0.5.0 (current on pypi) and below
pip install earth2studio
# For > 0.5.0
pip install earth2studio[dlwp]

Run a deterministic weather prediction in just a few lines of code:

from earth2studio.models.px import DLWP
from earth2studio.data import GFS
from earth2studio.io import NetCDF4Backend
from earth2studio.run import deterministic as run

model = DLWP.load_model(DLWP.load_default_package())
ds = GFS()
io = NetCDF4Backend("output.nc")

run(["2024-01-01"], 10, model, ds, io)

Features

Earth2Studio provides access to pre-trained AI weather models and inference
features through an easy to use and extendable Python interface.
This package focuses on supplying users the tools to build their own
workflows, pipelines, APIs, packages, etc. via modular components including:

Collection of pre-trained weather/climate prediction models
Collection of pre-trained diagnostic weather models
Variety of online and on-prem data sources for initialization, scoring, analysis, etc.
IO utilities for exporting predicted data to user friendly formats
Suite of perturbation methods for building ensemble predictions
Sample workflows and examples for common tasks / use cases
Seamless integration into other Nvidia packages including PhysicsNeMo

For a more complete list of feature set, be sure to view the documentation.
Don't see what you need?
Great news, extension and customization are at the heart of our design.

Contributors

Check out the Contributing document for details about the technical
requirements and the userguide for higher level philosophy, structure, and design.

License

Earth2Studio is provided under the Apache License 2.0, please see
LICENSE file for full license text.

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite it as below.
title: NVIDIA Earth2Studio
authors:
  - family-names: Geneva
    given-names: Nicholas
    orcid: https://orcid.org/0000-0003-4562-459X
  - family-names: Foster
    given-names: Dallas
    orcid: https://orcid.org/0000-0001-8459-9767
url: https://github.com/NVIDIA/earth2studio
repository-code: https://github.com/NVIDIA/earth2studio
date-released: 2024-04-22

Owner metadata

Name: NVIDIA Corporation
Login: NVIDIA
Email:
Kind: organization
Description:
Website: https://nvidia.com
Location: 2788 San Tomas Expressway, Santa Clara, CA, 95051
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/1728152?v=4
Repositories: 342
Last ynced at: 2025-03-21T18:01:39.515Z
Profile URL: https://github.com/NVIDIA

GitHub Events

Total

Create event: 9
Release event: 2
Issues event: 92
Watch event: 82
Delete event: 4
Member event: 4
Issue comment event: 329
Push event: 113
Pull request review event: 152
Pull request review comment event: 142
Pull request event: 136
Fork event: 19

Last Year

Create event: 9
Release event: 2
Issues event: 92
Watch event: 82
Delete event: 4
Member event: 4
Issue comment event: 329
Push event: 113
Pull request review event: 152
Pull request review comment event: 142
Pull request event: 136
Fork event: 19

Committers metadata

Last synced: 6 days ago

Total Commits: 169
Total Committers: 15
Avg Commits per committer: 11.267
Development Distribution Score (DDS): 0.296

Commits in past year: 141
Committers in past year: 15
Avg Commits per committer in past year: 9.4
Development Distribution Score (DDS) in past year: 0.27

Name	Email	Commits
Nicholas Geneva	5****a	119
Dallas Foster	d**f@n**m	28
Marius	2****s	3
Oliver Hennigh	l**1@g**m	3
Peter Harrington	4****n	3
Sai Krishnan Chandrasekar	1****v	2
Jussi Leinonen	j**n@n**m	2
Stefan Weissenberger	s**v@g**m	2
Akshay Subramaniam	6****r	1
Kaustubh Tangsali	7****i	1
Luke Conibear	1****r	1
Manas Sahni	s**s@g**m	1
Sean Lee	1****e	1
gertln	g**l@n**m	1
ivanauyeung	1****g	1

Committer domains:

nvidia.com: 3

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 118
Total pull requests: 246
Average time to close issues: 17 days
Average time to close pull requests: 2 days
Total issue authors: 26
Total pull request authors: 16
Average comments per issue: 0.86
Average comments per pull request: 3.0
Merged pull request: 227
Bot issues: 0
Bot pull requests: 0

Past year issues: 116
Past year pull requests: 201
Past year average time to close issues: 17 days
Past year average time to close pull requests: 3 days
Past year issue authors: 26
Past year pull request authors: 16
Past year average comments per issue: 0.86
Past year average comments per pull request: 3.14
Past year merged pull request: 182
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/nvidia/earth2studio

Top Issue Authors

NickGeneva (64)
swbg (11)
mariusaurus (8)
jleinonen (5)
dallasfoster (4)
luke-conibear (3)
david5010 (2)
hauke-dttl (2)
gertln (2)
bfouquet (1)
mike-scchen (1)
awesomemfg (1)
juliusberner (1)
ShihengDuan (1)
meteoDaniel (1)

Top Pull Request Authors

NickGeneva (166)
dallasfoster (43)
loliverhennigh (8)
mariusaurus (6)
pzharrington (5)
jleinonen (4)
gertln (3)
swbg (2)
saikrishnanc-nv (2)
luke-conibear (1)
ktangsali (1)
akshaysubr (1)
SeanSBLee (1)
rodrigoalmeida94 (1)
sahnimanas (1)

Top Issue Labels

bug (70)
enhancement (36)
documentation (15)
2 - In Progress (15)
1 - On Deck (15)
0 - Backlog (7)
question (6)
? - Needs Triage (2)
0 - Blocked (1)
wontfix (1)

Top Pull Request Labels

4 - In Review (14)
2 - In Progress (10)
3 - Ready for Review (8)
1 - On Deck (4)
! - Release (2)
bug (1)
enhancement (1)

Package metadata

Total packages: 1
Total downloads:
- pypi: 915 last-month
Total dependent packages: 0
Total dependent repositories: 0
Total versions: 6
Total maintainers: 1

pypi.org: earth2studio

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

Homepage: https://github.com/NVIDIA/earth2studio
Documentation: https://nvidia.github.io/earth2studio
Licenses: Apache Software License
Latest release: 0.6.0 (published 11 days ago)
Last Synced: 2025-04-25T18:32:04.528Z (1 day ago)
Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 915 Last month
Rankings:
- Dependent packages count: 9.459%
- Average: 35.931%
- Dependent repos count: 62.403%
Maintainers (1)
- nvidia

Dependencies

.github/workflows/blossom-ci.yml actions

NVIDIA/blossom-action main composite
actions/checkout v2 composite

pyproject.toml pypi

boto3 >=1.34.50
cdsapi >= 0.6.1
cfgrib >= 0.9.10.3
cftime *
eccodes >=1.4.0
ecmwf-opendata >=0.2.0
ecmwflibs >=0.5.2
fsspec >=2023.1.0
gcsfs *
h5netcdf >=1.0.0
h5py >=3.2.0
herbie-data *
huggingface-hub >=0.4.0
importlib_metadata *
loguru *
netCDF4 >=1.6.4
numpy *
nvidia-modulus >= 0.4.0
python-dotenv *
s3fs >=2023.5.0
setuptools >=67.6.0
torch >=2.0.0
torch_harmonics >=0.5.0
tqdm >=4.65.0
xarray >=2023.1.0
zarr >=2.14.2

setup.py pypi

Score: 14.709850116068425

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