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
Keywords from Contributors
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Last synced: about 22 hours ago
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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 (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-11-26T18:13:41.000Z (29 days ago)
- Last Synced: 2025-11-27T10:47:08.432Z (28 days ago)
- Topics: ai, climate-science, deep-learning, weather
- Language: Python
- Homepage: https://nvidia.github.io/earth2studio/
- Size: 324 MB
- Stars: 302
- Watchers: 8
- Forks: 79
- Open Issues: 15
- Releases: 10
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
README.md
NVIDIA Earth2Studio
Earth2Studio is a Python-based package designed to get users up and running
with AI Earth system models fast.
Our mission is to enable everyone to build, research and explore AI driven weather and
climate science.
- Earth2Studio Documentation -
Install | User-Guide |
Examples | API

Quick start
Install Earth2Studio:
pip install earth2studio[dlwp]
Run a deterministic AI 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)
Swap out for a different AI model by just installing
and replacing DLWP references with another forecast model.
Latest News
- The latest Climate in a Bottle
generative AI model from NVIDIA research has been
added via several APIs including a data source,
infilling
and super-resolution
APIs. See the cBottle examples
for more. - The long awaited GraphCast 1 degree prognostic model
and GraphCast Operational prognostic model
are now added. - Advanced Subseasonal-to-Seasonal (S2S) forecasting recipe
added demonstrating new inference pipelines for subseasonal weather forecasts (from
2 weeks to 3 months).
For a complete list of latest features and improvements see the changelog.
Overview
Earth2Studio is an AI inference pipeline toolkit focused on weather and climate
applications that is designed to ride on top of different AI frameworks, model
architectures, data sources and SciML tooling while providing a unified API.

The composability of the different core components in Earth2Studio easily allows the
development and deployment of increasingly complex pipelines that may chain multiple
data sources, AI models and other modules together.

The unified ecosystem of Earth2Studio provides users the opportunity to rapidly
swap out components for alternatives.
In addition to the largest model zoo of weather/climate AI models, Earth2Studio is
packed with useful functionality such as optimized data access to cloud data stores,
statistical operations and more to accelerate your pipelines.

Earth2Studio can be used for seamless deployment of Earth-2 models trained in
PhysicsNeMo.
Features
Earth2Studio package focuses on supplying users the tools to build their own
workflows, pipelines, APIs, packages, etc. via modular components including:
Prognostic models
in Earth2Studio perform time integration, taking atmospheric fields at a specific
time and auto-regressively predicting the same fields into the future (typically 6
hours per step), enabling both single time-step predictions and extended time-series
forecasting.
Earth2Studio maintains the largest collection of pre-trained state-of-the-art AI
weather/climate models ranging from global forecast models to regional specialized
models, covering various resolutions, architectures, and forecasting capabilities to
suit different computational and accuracy requirements.
Available models include but are not limited to:
| Model | Resolution | Architecture | Time Step | Coverage |
|---|---|---|---|---|
| GraphCast Small | 1.0° | Graph Neural Network | 6h | Global |
| GraphCast Operational | 0.25° | Graph Neural Network | 6h | Global |
| Pangu 3hr | 0.25° | Transformer | 3h | Global |
| Pangu 6hr | 0.25° | Transformer | 6h | Global |
| Pangu 24hr | 0.25° | Transformer | 24h | Global |
| Aurora | 0.25° | Transformer | 6h | Global |
| FuXi | 0.25° | Transformer | 6h | Global |
| AIFS | 0.25° | Transformer | 6h | Global |
| StormCast | 3km | Diffusion + Regression | 1h | Regional (US) |
| SFNO | 0.25° | Neural Operator | 6h | Global |
| DLESyM | 0.25° | Convolutional | 6h | Global |
For a complete list, see the prognostic model API docs.
Diagnostic models in Earth2Studio perform time-independent
transformations, typically taking geospatial fields at a specific time and
predicting new derived quantities without performing time integration enabling users
to build pipelines to predict specific quantities of interest that may not be
provided by forecasting models.
Earth2Studio contains a growing collection of specialized diagnostic models for
various phenomena including precipitation prediction, tropical cyclone tracking,
solar radiation estimation, wind gust forecasting, and more.
Available diagnostics include but are not limited to:
| Model | Resolution | Architecture | Coverage | Output |
|---|---|---|---|---|
| PrecipitationAFNO | 0.25° | Neural Operator | Global | Total precipitation |
| SolarRadiationAFNO1H | 0.25° | Neural Operator | Global | Surface solar radiation |
| WindgustAFNO | 0.25° | AFNO | Global | Maximum wind gust |
| TCTrackerVitart | 0.25° | Algorithmic | Global | TC tracks & properties |
| CBottleInfill | 100km | Diffusion | Global | Global climate sample |
| CBottleSR | 5km | Diffusion | Regional / Global | High-res climate |
| CorrDiff | Variable | Diffusion | Regional | Fine-scale weather |
| CorrDiffTaiwan | 2km | Diffusion | Regional (Taiwan) | Taiwan fine-scale weather |
For a complete list, see the diagnostic model API docs.
Data sources
in Earth2Studio provide a standardized API for accessing weather and climate
datasets from various providers (numerical models, data assimilation results, and
AI-generated data), enabling seamless integration of initial conditions for model
inference and validation data for scoring across different data formats and storage
systems.
Earth2Studio includes data sources ranging from operational weather models (GFS, HRRR,
IFS) and reanalysis datasets (ERA5 via ARCO, CDS) to AI-generated climate data
(cBottle) and local file systems. Fetching data is just plain easy, Earth2Studio
handles the complicated parts giving the users an easy to use Xarray data array of
requested data under a shared package wide vocabulary and
coordinate system.
Available data sources include but are not limited to:
| Data Source | Type | Resolution | Coverage | Data Format |
|---|---|---|---|---|
| GFS | Operational | 0.25° | Global | GRIB2 |
| GFS_FX | Forecast | 0.25° | Global | GRIB2 |
| HRRR | Operational | 3km | Regional (US) | GRIB2 |
| HRRR_FX | Forecast | 3km | Regional (US) | GRIB2 |
| ARCO ERA5 | Reanalysis | 0.25° | Global | Zarr |
| CDS | Reanalysis | 0.25° | Global | NetCDF |
| IFS | Operational | 0.25° | Global | GRIB2 |
| NCAR_ERA5 | Reanalysis | 0.25° | Global | NetCDF |
| WeatherBench2 | Reanalysis | 0.25° | Global | Zarr |
| GEFS_FX | Ensemble Forecast | 0.25° | Global | GRIB2 |
| IMERG | Precipitation | 0.1° | Global | NetCDF |
| CBottle3D | AI Generated | 100km | Global | HEALPix |
For a complete list, see the data source API docs.
IO backends in
Earth2Studio provides a standardized interface for writing and storing
pipeline outputs across different file formats and storage systems enabling users
to store inference outputs for later processing.
Earth2Studio includes IO backends ranging from traditional scientific formats (NetCDF)
and modern cloud-optimized formats (Zarr) to in-memory storage backends.
Available IO backends include:
| IO Backend | Format | Features | Location |
|---|---|---|---|
| ZarrBackend | Zarr | Compression, Chunking | In-Memory/Local |
| AsyncZarrBackend | Zarr | Async writes, Parallel I/O | In-Memory/Local/Remote |
| NetCDF4Backend | NetCDF4 | CF-compliant, Metadata | In-Memory/Local |
| XarrayBackend | Xarray Dataset | Rich metadata, Analysis-ready | In-Memory |
| KVBackend | Key-Value | Fast Temporary Access | In-Memory |
For a complete list, see the IO API docs.
Perturbation methods
in Earth2Studio provide a standardized interface for adding noise
to data arrays, typically enabling the creation of ensembling forecast pipelines
that capture uncertainty in weather and climate predictions.
Available perturbations include but are not limited to:
| Perturbation Method | Type | Spatial Correlation | Temporal Correlation |
|---|---|---|---|
| Gaussian | Noise | None | None |
| Correlated SphericalGaussian | Noise | Spherical | AR(1) process |
| Spherical Gaussian | Noise | Spherical (Matern) | None |
| Brown | Noise | 2D Fourier | None |
| Bred Vector | Dynamical | Model-dependent | Model-dependent |
| Hemispheric Centred Bred Vector | Dynamical | Hemispheric | Model-dependent |
For a complete list, see the perturbations API docs.
Statistics and metrics
in Earth2Studio provide operations typically useful for in-pipeline evaluation of
forecast performance across different dimensions (spatial, temporal, ensemble)
through various statistical measures including error metrics, correlation
coefficients, and ensemble verification statistics.
Available operations include but are not limited to:
| Statistic | Type | Application |
|---|---|---|
| RMSE | Error Metric | Forecast accuracy |
| ACC | Correlation | Pattern correlation |
| CRPS | Ensemble Metric | Probabilistic skill |
| Rank Histogram | Ensemble Metric | Ensemble reliability |
| Standard Deviation | Moment | Spread measure |
| Spread-Skill Ratio | Ensemble Metric | Ensemble calibration |
For a complete list, see the statistics API docs.
For a more complete list of features, 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 the
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: 2025-10-22T01:26:59.789Z
- Profile URL: https://github.com/NVIDIA
GitHub Events
Total
- Create event: 21
- Release event: 6
- Issues event: 173
- Watch event: 144
- Delete event: 13
- Member event: 6
- Issue comment event: 817
- Push event: 263
- Pull request review event: 438
- Pull request review comment event: 380
- Pull request event: 335
- Fork event: 34
Last Year
- Create event: 21
- Release event: 6
- Issues event: 173
- Watch event: 144
- Delete event: 13
- Member event: 6
- Issue comment event: 817
- Push event: 263
- Pull request review event: 438
- Pull request review comment event: 380
- Pull request event: 335
- Fork event: 34
Committers metadata
Last synced: about 2 months ago
Total Commits: 333
Total Committers: 21
Avg Commits per committer: 15.857
Development Distribution Score (DDS): 0.321
Commits in past year: 224
Committers in past year: 18
Avg Commits per committer in past year: 12.444
Development Distribution Score (DDS) in past year: 0.344
| Name | Commits | |
|---|---|---|
| Nicholas Geneva | 5****a | 226 |
| Dallas Foster | d****f@n****m | 31 |
| Oliver Hennigh | l****1@g****m | 15 |
| Peter Harrington | 4****n | 11 |
| Marius | 2****s | 8 |
| gertln | g****l@n****m | 8 |
| dependabot[bot] | 4****] | 6 |
| Jussi Leinonen | j****n@n****m | 6 |
| Stefan Weissenberger | s****g@n****m | 4 |
| Rodrigo Almeida | r****4@o****t | 3 |
| Akshay Subramaniam | 6****r | 2 |
| Alberto Carpentieri | 5****i | 2 |
| Emmanuel Ferdman | e****n@g****m | 2 |
| Sai Krishnan Chandrasekar | 1****v | 2 |
| Jialu (Alicia) Sui | 1****2 | 1 |
| Joshua Elms | j****1@g****m | 1 |
| Kaustubh Tangsali | 7****i | 1 |
| Luke Conibear | 1****r | 1 |
| Manas Sahni | s****s@g****m | 1 |
| Sean Lee | 1****e | 1 |
| ivanauyeung | 1****g | 1 |
Committer domains:
- nvidia.com: 4
- outlook.pt: 1
Issue and Pull Request metadata
Last synced: about 2 months ago
Total issues: 165
Total pull requests: 563
Average time to close issues: 13 days
Average time to close pull requests: 3 days
Total issue authors: 36
Total pull request authors: 23
Average comments per issue: 1.09
Average comments per pull request: 3.02
Merged pull request: 436
Bot issues: 0
Bot pull requests: 23
Past year issues: 122
Past year pull requests: 382
Past year average time to close issues: 14 days
Past year average time to close pull requests: 3 days
Past year issue authors: 31
Past year pull request authors: 20
Past year average comments per issue: 1.15
Past year average comments per pull request: 3.27
Past year merged pull request: 277
Past year bot issues: 0
Past year bot pull requests: 23
Top Issue Authors
- NickGeneva (78)
- swbg (12)
- mariusaurus (10)
- jleinonen (10)
- gertln (5)
- dallasfoster (5)
- mike-scchen (5)
- rodrigoalmeida94 (4)
- luke-conibear (3)
- pzharrington (2)
- sduthaler (2)
- david5010 (2)
- manmeet3591 (2)
- bfouquet (2)
- nbren12 (2)
Top Pull Request Authors
- NickGeneva (355)
- dallasfoster (40)
- loliverhennigh (33)
- dependabot[bot] (23)
- gertln (20)
- mariusaurus (18)
- pzharrington (18)
- jleinonen (15)
- swbg (8)
- rodrigoalmeida94 (7)
- akshaysubr (4)
- saikrishnanc-nv (3)
- albertocarpentieri (3)
- emmanuel-ferdman (2)
- SeanSBLee (2)
Top Issue Labels
- bug (95)
- enhancement (48)
- ? - Needs Triage (43)
- documentation (23)
- 2 - In Progress (9)
- 1 - On Deck (6)
- 0 - Backlog (5)
- question (5)
- dependencies (2)
- wontfix (1)
- ! - Release (1)
- 4 - In Review (1)
Top Pull Request Labels
- 4 - In Review (23)
- dependencies (23)
- python (23)
- 2 - In Progress (19)
- 3 - Ready for Review (17)
- 1 - On Deck (9)
- ! - Release (6)
- bug (3)
- enhancement (1)
- 5 - DO NOT MERGE (1)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 2,207 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 10
- 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: 0.9.0 (published 4 months ago)
- Last Synced: 2025-10-29T20:43:25.450Z (about 2 months ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 2,207 Last month
-
Rankings:
- Dependent packages count: 9.459%
- Average: 35.931%
- Dependent repos count: 62.403%
- Maintainers (1)
Dependencies
- NVIDIA/blossom-action main composite
- actions/checkout v2 composite
- 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
Score: 16.50326661899769