Earth2studio

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earth2studio is an open-source deep-learning framework for exploring, building, and deploying AI weather and climate workflows. It provides a Python-based package for rapid development and research.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is earth2studio?

earth2studio is an open-source, Python-based deep-learning framework developed by NVIDIA, designed to accelerate the exploration, building, and deployment of AI-driven weather and climate workflows. It offers a unified API for various AI frameworks, model architectures, and data sources, promoting composability and rapid development of complex pipelines. Key features include a comprehensive model zoo with state-of-the-art prognostic and diagnostic AI weather/climate models, standardized data source access (GFS, ERA5, IFS), and flexible I/O backends (Zarr, NetCDF4). The framework also provides perturbation methods for ensemble forecasting and statistical operations for in-pipeline evaluation, making it a powerful toolkit for researchers and developers in climate science.

Best used for

Ideal for researchers and developers who need to quickly run AI weather predictions, build complex climate pipelines, and evaluate forecast performance. Especially valuable for those working with large-scale climate data and requiring a flexible, composable framework for deep learning in earth sciences.

Common actions

build AI models

analyze climate data

deploy weather workflows

simulate climate scenarios

"AI Agents"face swappinggithub copilotautomated workflowopen-sourceworkflowslow-code/no-codedeepfakecollaboration

Capabilities

Key features

AI weather models
Climate prediction
Data source access
I/O backends
Perturbation methods
Statistical evaluation

Target Audience

researcherdeveloperdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of AI models does earth2studio support?

earth2studio supports a wide range of prognostic models for time integration (e.g., GraphCast, Pangu, AIFS) and diagnostic models for time-independent transformations (e.g., PrecipitationAFNO, SolarRadiationAFNO). It maintains a large collection of pre-trained state-of-the-art AI weather/climate models.

How does earth2studio handle data access and storage?

earth2studio provides a standardized API for accessing various weather and climate datasets from providers like GFS, ERA5, and IFS. It also includes flexible I/O backends such as ZarrBackend and NetCDF4Backend for storing pipeline outputs in different file formats and storage systems.

Can earth2studio be used for ensemble forecasting?

Yes, earth2studio includes perturbation methods that provide a standardized interface for adding noise to data arrays. This enables the creation of ensemble forecast pipelines, which are crucial for capturing uncertainty in weather and climate predictions.

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