NVIDIA/earth2studio

NVIDIA/earth2studio

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

Language: Python

License: Apache-2.0

Stars: 979

Forks: 218

Open issues: 22

Created: 2024-04-05T17:39:51Z

Pushed: 2026-06-09T23:19:44Z

Default branch: main

Fork: no

Archived: no

README:

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][e2studio_install_url].

• See the [examples][e2studio_examples_url] gallery providing different inference

workflow samples.

• Swap out [data sources][e2studio_data_api] or [models][e2studio_px_api] 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.

!Earth2Studio agentic setup

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.

• **Himawari AHI**,

Himawari-8/9 AHI ISatSS L2 Full Disk satellite data source is now available for geostationary satellite observations over the Western Pacific.

• **GHCN-Daily**,

Global Historical Climatology Network daily station observation data frame source providing access to global surface observations.

• **Orbit-2**,

precipitation downscaling diagnostic model added for high-resolution precipitation estimates.

• Conventional Observation Sources, new data sources for in-situ observations

including NNJA conventional obs and real-time GDAS conventional obs.

For a complete list of latest features and improvements see the [changelog](./CHANGELOG.md).

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][physicsnemo_repo_url].

Features

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

Prognostic Models

[Prognostic models][e2studio_px_url] 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…