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

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ai climate-science deep-learning weather

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

README.md

NVIDIA Earth2Studio

python version
license
coverage
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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

Earth2Studio Banner

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

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.

Earth2Studio Overview 1

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.

Earth2Studio Overview 1

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 Overview 1

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

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Package metadata

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%
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  • Maintainers (1)

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: 16.50326661899769