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

Running AI weather prediction can be done with just a few lines of code.

• For detailed installation steps, including model-specific installations, see the
  install guide.
• See the examples gallery providing different inference
  workflow samples.
• Swap out data sources or models depending on
  your use case!

Tutorial

Agent-assisted setup

Automate setup with your preferred coding agent using NVIDIA Earth2Studio skills.
Install the Earth2Studio skill set, then ask your favorite agent (Claude, Codex, OpenCode, etc) to
recommend a model, configure an environment, or run a first deterministic forecast.
Find more Earth2Studio skills in the NVIDIA Skills catalog.

npx skills add NVIDIA/skills --skill earth2studio-install
npx skills add NVIDIA/skills --skill earth2studio-discover
npx skills add NVIDIA/skills --skill earth2studio-data-fetch
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast

Example agent prompts:

Use the Earth2Studio discover skill to recommend a starter forecast workflow.
Use the Earth2Studio install skill to set up my environment for FourCastNet3 inference.
Create a script to fetch ERA5 surface winds data for March 2024.
Create a deterministic forecast workflow with GFS, FourCastNet3, and a Zarr output store.

NVIDIA FourCastNet3

from earth2studio.models.px import FCN3
from earth2studio.data import GFS
from earth2studio.io import ZarrBackend
from earth2studio.run import deterministic as run

model = FCN3.load_model(FCN3.load_default_package())
data = GFS()
io = ZarrBackend("outputs/fcn3_forecast.zarr")
run(["2025-01-01T00:00:00"], 10, model, data, io)

ECMWF AIFS

from earth2studio.models.px import AIFS
from earth2studio.data import IFS
from earth2studio.io import ZarrBackend
from earth2studio.run import deterministic as run

model = AIFS.load_model(AIFS.load_default_package())
data = IFS()
io = ZarrBackend("outputs/aifs_forecast.zarr")
run(["2025-01-01T00:00:00"], 10, model, data, io)

Google Graphcast

from earth2studio.models.px import GraphCastOperational
from earth2studio.data import GFS
from earth2studio.io import ZarrBackend
from earth2studio.run import deterministic as run

package = GraphCastOperational.load_default_package()
model = GraphCastOperational.load_model(package)
data = GFS()
io = ZarrBackend("outputs/graphcast_operational_forecast.zarr")
run(["2025-01-01T00:00:00"], 4, model, data, io)

[!IMPORTANT]
Earth2Studio is an interface to third‑party models, checkpoints, and datasets.
Licenses for these assets are owned by their providers.
Ensure you have the rights to download, use, and (if applicable) redistribute each
model and dataset.
Links to the original license and source are often provided in the API docs for each
model/data source.

Latest News

[!NOTE]
As of version 0.14.0, Earth2Studio TOML default installs now target CUDA 13.

• Aurora v1.5,
  Microsoft Aurora v1.5 deterministic and ensemble model wrapper for global
  weather forecasting.
• StormCast CONUS,
  StormCast CONUS prognostic model for convective-scale forecasting over the
  contiguous United States.
• Dynamical.org Sources,
  a comprehensive suite of analysis and forecast data sources reading from
  anonymous Icechunk repositories (AIFS, GFS, GEFS, HRRR, MRMS, ICON-EU, IFS-ENS).
• EarthMover Data Sources,
  EarthMover ERA5 0.25-degree reanalysis and IFS 0.1-degree forecast sources
  hosted by BrightBand.
• StormScope NSRDB,
  solar irradiance (GHI) estimation diagnostic model.

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.

Earth-2 Open Models

Access state of the art Nvidia open models for climate and weather: Earth-2 Open Models.
For training recipes for these models, see the PhysicsNeMo repository.

Features

Earth2Studio package focuses on supplying you the tools to build your own
workflows, pipelines, APIs, or packages using 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:

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:

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 you 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:

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:

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:

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:

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 user guide for higher level philosophy, structure, and design.

License

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