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Change","sub_category":"Earth and Climate Modeling","monthly_downloads":7648,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003c!-- markdownlint-disable MD002 MD033 MD041 MD053 --\u003e\n\u003cdiv align=\"center\"\u003e\n\n# NVIDIA Earth2Studio\n\n[![python version][e2studio_python_img]][e2studio_python_url]\n[![license][e2studio_license_img]][e2studio_license_url]\n[![coverage][e2studio_cov_img]][e2studio_cov_url]\n[![mypy][e2studio_mypy_img]][e2studio_mypy_url]\n[![format][e2studio_format_img]][e2studio_format_url]\n[![ruff][e2studio_ruff_img]][e2studio_ruff_url]\n[![uv][e2studio_uv_img]][e2studio_uv_url]\n\nEarth2Studio is a Python-based package designed to get users up and running\nwith AI Earth system models *fast*.\nOur mission is to enable everyone to build, research and explore AI driven weather and\nclimate science.\n\n\u003c!-- markdownlint-disable MD036 --\u003e\n**- Earth2Studio Documentation -**\n\u003c!-- markdownlint-enable MD036 --\u003e\n\n[Install][e2studio_install_url] | [User-Guide][e2studio_userguide_url] |\n[Examples][e2studio_examples_url] | [API][e2studio_api_url]\n\n![Earth2Studio Banner](https://huggingface.co/datasets/nvidia/earth2studio-assets/resolve/0.1.0/readme/earth2studio_feature_banner.png?id=1)\n\n\u003c/div\u003e\n\n## Quick start\n\nRunning AI weather prediction can be done with just a few lines of code.\n\n- For detailed installation steps, including model-specific installations, see the\n    [install guide][e2studio_install_url].\n- See the [examples][e2studio_examples_url] gallery providing different inference\n    workflow samples.\n- Swap out [data sources][e2studio_data_api] or [models][e2studio_px_api] depending on\n    your use case!\n\n### NVIDIA FourCastNet3\n\n```python\nfrom earth2studio.models.px import FCN3\nfrom earth2studio.data import GFS\nfrom earth2studio.io import ZarrBackend\nfrom earth2studio.run import deterministic as run\n\nmodel = FCN3.load_model(FCN3.load_default_package())\ndata = GFS()\nio = ZarrBackend(\"outputs/fcn3_forecast.zarr\")\nrun([\"2025-01-01T00:00:00\"], 10, model, data, io)\n```\n\n### ECMWF AIFS\n\n```python\nfrom earth2studio.models.px import AIFS\nfrom earth2studio.data import IFS\nfrom earth2studio.io import ZarrBackend\nfrom earth2studio.run import deterministic as run\n\nmodel = AIFS.load_model(AIFS.load_default_package())\ndata = IFS()\nio = ZarrBackend(\"outputs/aifs_forecast.zarr\")\nrun([\"2025-01-01T00:00:00\"], 10, model, data, io)\n```\n\n### Google Graphcast\n\n```python\nfrom earth2studio.models.px import GraphCastOperational\nfrom earth2studio.data import GFS\nfrom earth2studio.io import ZarrBackend\nfrom earth2studio.run import deterministic as run\n\npackage = GraphCastOperational.load_default_package()\nmodel = GraphCastOperational.load_model(package)\ndata = GFS()\nio = ZarrBackend(\"outputs/graphcast_operational_forecast.zarr\")\nrun([\"2025-01-01T00:00:00\"], 4, model, data, io)\n```\n\n\u003e [!IMPORTANT]\n\u003e Earth2Studio is an interface to third‑party models, checkpoints, and datasets.\n\u003e Licenses for these assets are owned by their providers.\n\u003e Ensure you have the rights to download, use, and (if applicable) redistribute each\n\u003e model and dataset.\n\u003e Links to the original license and source are often provided in the API docs for each\n\u003e model/data source.\n\n[![Watch the video](https://img.youtube.com/vi/Sog6aCapZeA/hqdefault.jpg)](https://www.youtube.com/watch?v=Sog6aCapZeA)\n\n## Latest News\n\n- [**StormCast SDA**](https://nvidia.github.io/earth2studio/modules/generated/models/da/earth2studio.models.da.StormCastSDA.html),\n    score-based data assimilation (SDA) combined with StormCast for high-resolution\n    regional weather prediction, is now available alongside new data assimilation\n    model utilities.\n- **Data Assimilation Models**, a new model class for data assimilation including\n    equirectangular interpolation and [HealDA](https://nvidia.github.io/earth2studio/examples/05_data_assimilation/02_healda.html).\n- [**NOAA UFS Observation Data Sources**](https://nvidia.github.io/earth2studio/modules/datasources_dataframe.html),\n    for satellite and conventional observation dataframes now available with the new\n    Earth2Studio base schema.\n- [**Planetary Computer Data Sources**](https://nvidia.github.io/earth2studio/modules/datasources_analysis.html)\n    added for ECMWF IFS analysis data and GOES cloud and moisture imagery.\n\nFor a complete list of latest features and improvements see the [changelog](./CHANGELOG.md).\n\n## Overview\n\nEarth2Studio is an *AI inference pipeline toolkit* focused on weather and climate\napplications that is designed to ride on top of different AI frameworks, model\narchitectures, data sources and SciML tooling while providing a unified API.\n\n\u003cdiv align=\"center\"\u003e\n\n![Earth2Studio Overview 1](https://huggingface.co/datasets/nvidia/earth2studio-assets/resolve/0.1.0/readme/earth2studio-readme-overview-1.png?id=1)\n\n\u003c/div\u003e\n\nThe composability of the different core components in Earth2Studio easily allows the\ndevelopment and deployment of increasingly complex pipelines that may chain multiple\ndata sources, AI models and other modules together.\n\n\u003cdiv align=\"center\"\u003e\n\n![Earth2Studio Overview 1](https://huggingface.co/datasets/nvidia/earth2studio-assets/resolve/0.1.0/readme/earth2studio-readme-overview-2.png?id=1)\n\n\u003c/div\u003e\n\nThe unified ecosystem of Earth2Studio provides users the opportunity to rapidly\nswap out components for alternatives.\nIn addition to the largest model zoo of weather/climate AI models, Earth2Studio is\npacked with useful functionality such as optimized data access to cloud data stores,\nstatistical operations and more to accelerate your pipelines.\n\n\u003cdiv align=\"center\"\u003e\n\n![Earth2Studio Overview 1](https://huggingface.co/datasets/nvidia/earth2studio-assets/resolve/0.1.0/readme/earth2studio-readme-overview-3.webp?id=1)\n\n\u003c/div\u003e\n\n### Earth-2 Open Models\n\nAccess state of the art Nvidia open models for climate and weather: [Earth-2 Open Models](https://huggingface.co/collections/nvidia/earth-2).\nFor training recipes for these models, see the [PhysicsNeMo repository][physicsnemo_repo_url].\n\n## Features\n\nEarth2Studio package focuses on supplying you the tools to build your own\nworkflows, pipelines, APIs, or packages using modular components including:\n\n\u003cdetails\u003e\n\u003csummary\u003ePrognostic Models\u003c/summary\u003e\n\n[Prognostic models][e2studio_px_url]\n    in Earth2Studio perform time integration, taking atmospheric fields at a specific\n    time and auto-regressively predicting the same fields into the future (typically 6\n    hours per step), enabling both single time-step predictions and extended time-series\n    forecasting.\n\nEarth2Studio maintains the largest collection of pre-trained state-of-the-art AI\n    weather/climate models ranging from global forecast models to regional specialized\n    models, covering various resolutions, architectures, and forecasting capabilities to\n    suit different computational and accuracy requirements.\n\nAvailable models include but are not limited to:\n\n| Model | Resolution | Architecture | Time Step | Coverage |\n|-------|------------|--------------|-----------|----------|\n| GraphCast Small | 1.0° | Graph Neural Network | 6h | Global |\n| GraphCast Operational | 0.25° | Graph Neural Network | 6h | Global |\n| Pangu 3hr | 0.25° | Transformer | 3h | Global |\n| Pangu 6hr | 0.25° | Transformer | 6h | Global |\n| Pangu 24hr | 0.25° | Transformer | 24h | Global |\n| Aurora | 0.25° | Transformer | 6h | Global |\n| FuXi | 0.25° | Transformer | 6h | Global |\n| AIFS | 0.25° | Transformer | 6h | Global |\n| AIFS Ensemble | 0.25° | Transformer Ensemble | 6h | Global |\n| StormCast | 3km | Diffusion + Regression | 1h | Regional (US) |\n| SFNO | 0.25° | Neural Operator | 6h | Global |\n| DLESyM | 0.25° | Convolutional | 6h | Global |\n\nFor a complete list, see the [prognostic model API docs][e2studio_px_api].\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eDiagnostic Models\u003c/summary\u003e\n\n[Diagnostic models][e2studio_dx_url] in Earth2Studio perform time-independent\n    transformations, typically taking geospatial fields at a specific time and\n    predicting new derived quantities without performing time integration enabling users\n    to build pipelines to predict specific quantities of interest that may not be\n    provided by forecasting models.\n\nEarth2Studio contains a growing collection of specialized diagnostic models for\n    various phenomena including precipitation prediction, tropical cyclone tracking,\n    solar radiation estimation, wind gust forecasting, and more.\n\nAvailable diagnostics include but are not limited to:\n\n| Model | Resolution | Architecture | Coverage | Output |\n|-------|------------|--------------|----------|--------|\n| PrecipitationAFNO | 0.25° | Neural Operator  | Global | Total precipitation |\n| SolarRadiationAFNO1H | 0.25° | Neural Operator  | Global | Surface solar radiation |\n| WindgustAFNO | 0.25° | AFNO | Global | Maximum wind gust |\n| TCTrackerVitart | 0.25° | Algorithmic | Global | TC tracks \u0026 properties |\n| CBottleInfill | 100km | Diffusion | Global | Global climate sample |\n| CBottleSR | 5km | Diffusion | Regional / Global | High-res climate |\n| CorrDiff | Variable | Diffusion | Regional | Fine-scale weather |\n| CorrDiffTaiwan | 2km | Diffusion | Regional (Taiwan) | Taiwan fine-scale weather |\n\nFor a complete list, see the [diagnostic model API docs][e2studio_dx_api].\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eDatasources\u003c/summary\u003e\n\n[Data sources][e2studio_data_url]\n    in Earth2Studio provide a standardized API for accessing weather and climate\n    datasets from various providers (numerical models, data assimilation results, and\n    AI-generated data), enabling seamless integration of initial conditions for model\n    inference and validation data for scoring across different data formats and storage\n    systems.\n\nEarth2Studio includes data sources ranging from operational weather models (GFS, HRRR,\n    IFS) and reanalysis datasets (ERA5 via ARCO, CDS) to AI-generated climate data\n    (cBottle) and local file systems. Fetching data is just plain easy, Earth2Studio\n    handles the complicated parts giving you an easy to use Xarray data array of\n    requested data under a shared package wide [vocabulary][e2studio_lex_url] and\n    coordinate system.\n\nAvailable data sources include but are not limited to:\n\n| Data Source | Type | Resolution | Coverage | Data Format |\n|-------------|------|------------|----------|-------------|\n| GFS | Operational | 0.25° | Global | GRIB2 |\n| GFS_FX | Forecast | 0.25° | Global | GRIB2 |\n| HRRR | Operational | 3km | Regional (US) | GRIB2 |\n| HRRR_FX | Forecast | 3km | Regional (US) | GRIB2 |\n| ARCO ERA5 | Reanalysis | 0.25° | Global | Zarr |\n| CDS | Reanalysis | 0.25° | Global | NetCDF |\n| IFS | Operational | 0.25° | Global | GRIB2 |\n| NCAR_ERA5 | Reanalysis | 0.25° | Global | NetCDF |\n| WeatherBench2 | Reanalysis | 0.25° | Global | Zarr |\n| GEFS_FX | Ensemble Forecast | 0.25° | Global | GRIB2 |\n| ISD | Observational | Point | Regional (US) | CSV |\n| MRMS | Reanalysis | 1km | Regional (US) | GRIB2 |\n\nFor a complete list, see the [data source API docs][e2studio_data_api].\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eIO Backends\u003c/summary\u003e\n\n[IO backends][e2studio_io_url] in\n    Earth2Studio provides a standardized interface for writing and storing\n    pipeline outputs across different file formats and storage systems enabling users\n    to store inference outputs for later processing.\n\nEarth2Studio includes IO backends ranging from traditional scientific formats (NetCDF)\n    and modern cloud-optimized formats (Zarr) to in-memory storage backends.\n\nAvailable IO backends include:\n\n| IO Backend | Format | Features | Location |\n|------------|--------|----------|----------|\n| ZarrBackend | Zarr | Compression, Chunking | In-Memory/Local |\n| AsyncZarrBackend | Zarr | Async writes, Parallel I/O | In-Memory/Local/Remote |\n| NetCDF4Backend | NetCDF4 | CF-compliant, Metadata | In-Memory/Local |\n| XarrayBackend | Xarray Dataset | Rich metadata, Analysis-ready | In-Memory |\n| KVBackend | Key-Value| Fast Temporary Access | In-Memory |\n\nFor a complete list, see the [IO API docs][e2studio_io_api].\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003ePerturbation Methods\u003c/summary\u003e\n\n[Perturbation methods][e2studio_pb_url]\n    in Earth2Studio provide a standardized interface for adding noise\n    to data arrays, typically enabling the creation of ensembling forecast pipelines\n    that capture uncertainty in weather and climate predictions.\n\nAvailable perturbations include but are not limited to:\n\n| Perturbation Method | Type | Spatial Correlation | Temporal Correlation |\n|---------------------|------|-------------------|---------------------|\n| Gaussian | Noise | None | None |\n| Correlated SphericalGaussian | Noise | Spherical | AR(1) process |\n| Spherical Gaussian | Noise | Spherical (Matern) | None |\n| Brown | Noise | 2D Fourier | None |\n| Bred Vector | Dynamical | Model-dependent | Model-dependent |\n| Hemispheric Centred Bred Vector | Dynamical | Hemispheric | Model-dependent |\n\nFor a complete list, see the [perturbations API docs][e2studio_pb_url].\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eStatistics / Metrics\u003c/summary\u003e\n\n[Statistics and metrics][e2studio_stat_url]\n    in Earth2Studio provide operations typically useful for in-pipeline evaluation of\n    forecast performance across different dimensions (spatial, temporal, ensemble)\n    through various statistical measures including error metrics, correlation\n    coefficients, and ensemble verification statistics.\n\nAvailable operations include but are not limited to:\n\n| Statistic | Type | Application |\n|-----------|------|-------------|\n| RMSE | Error Metric | Forecast accuracy |\n| ACC | Correlation | Pattern correlation |\n| CRPS | Ensemble Metric | Probabilistic skill |\n| Rank Histogram | Ensemble Metric | Ensemble reliability |\n| Standard Deviation | Moment | Spread measure |\n| Spread-Skill Ratio | Ensemble Metric | Ensemble calibration |\n\nFor a complete list, see the [statistics API docs][e2studio_stat_api].\n\n\u003c/details\u003e\n\nFor a more complete list of features, be sure to view the [documentation][e2studio_docs_url].\nDon't see what you need?\nGreat news, extension and customization are at the heart of our [design][e2studio_customization_url].\n\n## Contributors\n\nCheck out the [contributing](CONTRIBUTING.md) document for details about the technical\nrequirements and the user guide for higher level philosophy, structure, and design.\n\n## License\n\nEarth2Studio is provided under the Apache License 2.0, refer to the\n[LICENSE file][e2studio_license_url] for full license text.\n\n\u003c!-- Badge links --\u003e\n\n[e2studio_python_img]: https://img.shields.io/badge/Python-3.11%20|%203.12%20|%203.13-blue?style=flat-square\u0026logo=python\n[e2studio_license_img]: https://img.shields.io/badge/License-Apache%202.0-green?style=flat-square\n[e2studio_format_img]: https://img.shields.io/badge/Code%20Style-Black-black?style=flat-square\n[e2studio_mypy_img]: https://img.shields.io/badge/mypy-Checked-blue?style=flat-square\u0026labelColor=grey\n[e2studio_cov_img]: https://img.shields.io/codecov/c/github/nvidia/earth2studio?style=flat-square\u0026logo=codecov\n[e2studio_ruff_img]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\u0026style=flat-square\n[e2studio_uv_img]: https://img.shields.io/endpoint?url=https%3A%2F%2Fraw.githubusercontent.com%2Fastral-sh%2Fuv%2Fmain%2Fassets%2Fbadge%2Fv0.json\u0026style=flat-square\n\n[e2studio_python_url]: https://www.python.org/downloads/\n[e2studio_license_url]: ./LICENSE\n[e2studio_format_url]: https://github.com/psf/black\n[e2studio_cov_url]: ./test/\n[e2studio_mypy_url]: https://mypy-lang.org/\n[e2studio_ruff_url]: https://github.com/astral-sh/ruff\n[e2studio_uv_url]: https://github.com/astral-sh/uv\n\n\u003c!-- Doc links --\u003e\n[e2studio_docs_url]: https://nvidia.github.io/earth2studio/\n[e2studio_install_url]: https://nvidia.github.io/earth2studio/userguide/about/install.html\n[e2studio_userguide_url]: https://nvidia.github.io/earth2studio/userguide/\n[e2studio_examples_url]: https://nvidia.github.io/earth2studio/examples/\n[e2studio_api_url]: https://nvidia.github.io/earth2studio/modules/\n[e2studio_customization_url]: https://nvidia.github.io/earth2studio/examples/extend/index.html\n[e2studio_px_url]: https://nvidia.github.io/earth2studio/userguide/components/prognostic.html\n[e2studio_px_api]: https://nvidia.github.io/earth2studio/modules/models.html#earth2studio-models-px-prognostic\n[e2studio_dx_url]: https://nvidia.github.io/earth2studio/userguide/components/diagnostic.html\n[e2studio_dx_api]: https://nvidia.github.io/earth2studio/modules/models.html#earth2studio-models-dx-diagnostic\n[e2studio_data_url]: https://nvidia.github.io/earth2studio/userguide/components/datasources.html\n[e2studio_data_api]: https://nvidia.github.io/earth2studio/modules/datasources.html\n[e2studio_io_url]: https://nvidia.github.io/earth2studio/userguide/components/io.html\n[e2studio_io_api]: https://nvidia.github.io/earth2studio/modules/io.html\n[e2studio_pb_url]: https://nvidia.github.io/earth2studio/userguide/components/perturbation.html\n[e2studio_pb_api]: https://nvidia.github.io/earth2studio/modules/perturbation.html\n[e2studio_stat_url]: https://nvidia.github.io/earth2studio/userguide/components/statistics.html\n[e2studio_stat_api]: https://nvidia.github.io/earth2studio/modules/statistics.html\n[e2studio_lex_url]: https://nvidia.github.io/earth2studio/userguide/advanced/lexicon.html\n\n\u003c!-- Misc links --\u003e\n[physicsnemo_repo_url]: https://github.com/NVIDIA/physicsnemo\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["transforms","measur","archiving","optimize","compose","threads","conversion","animals","products","projection"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/193514","html_url":"https://ost.ecosyste.ms/projects/193514"}