Hacker News
Nvidia has launched Earth2Studio, an open-source deep-learning framework designed for exploring, building, and deploying AI-powered weather and climate workflows. This initiative aims to advance AI applications in environmental science.
AI 生成摘要
Nvidia 推出了 Earth2Studio，這是一個開源的深度學習框架，專為探索、構建和部署 AI 驅動的天氣與氣候工作流程而設計，旨在推動 AI 在環境科學領域的應用。
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Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
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.
Install | User-Guide |
Examples | API
Running AI weather prediction can be done with just a few lines of code.
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.
For a complete list of latest features and improvements see the changelog.
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.
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.
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:
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 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:
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.
Check out the contributing document for details about the technical
requirements and the userguide for higher level philosophy, structure, and design.
Earth2Studio is provided under the Apache License 2.0, please see the
LICENSE file for full license text.
Open-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.
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