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(Conda)](https://github.com/blaylockbk/Herbie/actions/workflows/tests-conda.yml/badge.svg)](https://github.com/blaylockbk/Herbie/actions/workflows/tests-conda.yml)\n[![Tests (Python)](https://github.com/blaylockbk/Herbie/actions/workflows/tests-python.yml/badge.svg)](https://github.com/blaylockbk/Herbie/actions/workflows/tests-python.yml)\n[![Documentation Status](https://readthedocs.org/projects/herbie/badge/?version=latest)](https://herbie.readthedocs.io/?badge=latest)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n\n[![Conda Downloads](https://img.shields.io/conda/d/conda-forge/herbie-data)](https://anaconda.org/conda-forge/herbie-data)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/herbie-data?label=pypi|downloads)](https://pypistats.org/packages/herbie-data)\n![PyPI - License](https://img.shields.io/pypi/l/herbie-data)\n\n## 📚 [Documentation](https://herbie.readthedocs.io/) | 💬 [Discussions](https://github.com/blaylockbk/Herbie/discussions) | ❔ [Get Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)\n\n\u003c!-- (Badges) --\u003e\n\n\u003c/div\u003e\n\n---\n\n## What is Herbie?\n\nHerbie is a Python package that makes downloading and working with numerical weather prediction (NWP) model data simple and fast. Whether you're a researcher, meteorologist, data scientist, or weather enthusiast, Herbie provides easy access to forecast data from NOAA, ECMWF, and other sources.\n\n**Key Features:**\n- 🌐 **Access 15+ weather models** - HRRR, GFS, RAP, GEFS, ECMWF, and more\n- ⚡ **Smart downloads** - Get full GRIB2 files or subset by variable to save time and bandwidth\n- 🔄 **Multiple data sources** - Automatically searches different archive (AWS, Google Cloud, NOMADS, Azure)\n- 📊 **Built-in data reading** - Load data directly into xarray for analysis\n- 🛠️ **CLI and Python API** - Use from command line or in your Python scripts\n- 🗺️ **Visualization aids** - Includes Cartopy integration for mapping\n\n**Keywords:** weather data download, GRIB2, python, numerical weather prediction, meteorological data, weather forecast API, xarray, atmospheric data, research, academia, data science, machine learning,visualization\n\n---\n\n## Quick Start\n\n### Installation\n\n**With conda or mamba:**\n```bash\nconda install -c conda-forge herbie-data\n```\n```bash\nmamba install -c conda-forge herbie-data\n```\n\n**With pip:**\n```bash\npip install herbie-data\n```\n\n**With uv:**\n```bash\nuv add herbie-data\n```\n\n*Note: optional features require manual installation of wgrib2*\n\n### Simple Example\n\n```python\nfrom herbie import Herbie\n\n# Create a Herbie object for HRRR model data\nH = Herbie(\n    '2021-01-01 12:00',  # Date and time\n    model='hrrr',         # Model name\n    product='sfc',        # Product type\n    fxx=6                 # Forecast hour\n)\n\n# Show file contents\nH.inventory()\n\n# Download and read 2-meter temperature\ntemperature = H.xarray(\"TMP:2 m\")\n```\n\n### Command Line Interface\n\n```bash\n# Download HRRR surface forecast\nherbie download -m hrrr --product sfc -d \"2023-03-15 12:00\" -f 0\n\n# Get specific variable (temperature at 850 mb)\nherbie download -m gfs --product 0p25 -d 2023-03-15 -f 24 --subset \":TMP:850 mb:\"\n\n# View available variables\nherbie inventory -m rap -d 2023031512 -f 0\n```\n\n---\n\n## Supported Weather Models\n\nHerbie provides access to a wide range of numerical weather prediction models:\n\n### US Models (NOAA)\n- **[HRRR](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/hrrr.html)** - High Resolution Rapid Refresh (3km resolution)\n- **[HRRR-Alaska](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/hrrrak.html)** - Alaska version\n- **[GFS](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/gfs.html)** - Global Forecast System\n- **[GEFS](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/gefs.html)** - Global Ensemble Forecast System\n- **[RAP](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/rap.html)** - Rapid Refresh\n- **[NAM](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/nam.html)** - North American Mesoscale Model\n- **[NBM](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/nbm.html)** - National Blend of Models\n- **[RTMA/URMA](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/rtma-urma.html)** - Real-Time/Un-Restricted Mesoscale Analysis\n- **[RRFS](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/rrfs.html)** - Rapid Refresh Forecast System *(prototype)*\n- **[HAFS](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/hafs.html)** - Hurricane Analysis and Forecast System\n- **[CFS](https://herbie.readthedocs.io/en/latest/gallery/noaa_models/cfs.html)** - Climate Forecast System\n\nMuch of this data is made available through the [NOAA Open Data Dissemination](https://www.noaa.gov/information-technology/open-data-dissemination) (NODD) program.\n\n### Other Models\n- **[ECMWF](https://herbie.readthedocs.io/en/latest/gallery/ecmwf_models/ecmwf.html)** - ECMWF's IFS and AIFS Open Data Forecasts\n- **[HRDPS](https://herbie.readthedocs.io/en/latest/gallery/eccc_models/hrdps.html)** - Canada's High Resolution Deterministic Prediction System (Canada)\n- **[NAVGEM](https://herbie.readthedocs.io/en/latest/gallery/usnavy_models/navgem.html)** - U.S. Navy Global Environmental Model\n\n**[View all models in the gallery →](https://herbie.readthedocs.io/en/latest/gallery/index.html)**\n\n\n---\n\n## Core Capabilities\n\n**Features:**\n- 🔍 Search model output from different data sources\n- ⬇️ Download full or subset GRIB2 files\n- 📖 Read data with xarray and index files with Pandas\n- 🗺️ Built-in Cartopy aids for mapping\n- 🎯 Extract data at specific points\n- 🔌 Extensible with [custom model templates](https://github.com/blaylockbk/herbie-plugin-tutorial)\n\n\n```mermaid\n  graph TD;\n      d1[(HRRR)] -..-\u003e H\n      d2[(RAP)] -.-\u003e H\n      d3[(GFS)] -..-\u003e H\n      d33[(GEFS)] -.-\u003e H\n      d4[(IFS)] -..-\u003e H\n      d44[(AIFS)] -..-\u003e H\n      d5[(NBM)] -.-\u003e H\n      d6[(RRFS)] -..-\u003e H\n      d7[(RTMA)] -.-\u003e H\n      d8[(URMA)] -..-\u003e H\n      H((Herbie))\n      H --- .inventory\n      H --- .download\n      H --- .xarray\n\n      style H fill:#d8c89d,stroke:#0c3576,stroke-width:4px,color:#000000\n```\n\n### Python API\n\nHerbie's Python API is used like this:\n\n```python\nfrom herbie import Herbie\n\n# Herbie object for the HRRR model 6-hr surface forecast product\nH = Herbie(\n  '2021-01-01 12:00',\n  model='hrrr',\n  product='sfc',\n  fxx=6\n)\n\n# View all variables in a file\nH.inventory()\n\n# Download options\nH.download()              # Download full GRIB2 file\nH.download(\":500 mb\")     # Download subset (all 500 mb fields)\nH.download(\":TMP:2 m\")    # Download specific variable\n\n# Read data into xarray\nds = H.xarray(\"TMP:2 m\")  # 2-meter temperature\nds = H.xarray(\":500 mb\")  # All 500 mb level data\n```\n\n### Command Line Interface\n\nHerbie also has a command line interface (CLI) so you can use Herbie right in your terminal.\n\n```bash\n# Get the URL for a HRRR surface file from today at 12Z\nherbie data -m hrrr --product sfc -d \"2023-03-15 12:00\" -f 0\n\n# Download GFS 0.25° forecast hour 24 temperature at 850mb\nherbie download -m gfs --product 0p25 -d 2023-03-15T00:00 -f 24 --subset \":TMP:850 mb:\"\n\n# View all available variables in a RAP model run\nherbie inventory -m rap -d 2023031512 -f 0\n\n# Download multiple forecast hours for a date range\nherbie download -m hrrr -d 2023-03-15T00:00 2023-03-15T06:00 -f 1 3 6 --subset \":UGRD:10 m:\"\n\n# Specify custom source priority (check only Google)\nherbie data -m hrrr -d 2023-03-15 -f 0 -p google\n```\n## Data Sources\n\nHerbie automatically searches for data at multiple data sources:\n\n- [NOMADS](https://nomads.ncep.noaa.gov/)\n- [NOAA Open Data Dissemination Program (NODD)](https://www.noaa.gov/information-technology/open-data-dissemination) partners (i.e., AWS, Google, Azure).\n- [ECMWF Open Data Forecasts](https://www.ecmwf.int/en/forecasts/datasets/open-data)\n- University of Utah CHPC Pando archive\n- Local file system\n\n---\n\n## Documentation \u0026 Help\n\n📘 **[Full Documentation](https://herbie.readthedocs.io/)** - Comprehensive guides and API reference\n\n🖼️ **[Example Gallery](https://herbie.readthedocs.io/en/latest/gallery/index.html)** - Browse code examples for each model\n\n💬 **[GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions)** - Ask questions and share ideas\n\n🚑 **[Report Issues](https://github.com/blaylockbk/Herbie/issues)** - Found a bug? Let us know\n\n---\n\n## Citation \u0026 Acknowledgment\n\nIf Herbie played an important role in your work, please [tell us about it](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)! \n\n### Suggested Citation\n\n\u003e Blaylock, B. K. (YEAR). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 20xx.x.x) [Computer software]. https://doi.org/10.5281/zenodo.4567540\n\n### Suggested Acknowledgment\n\n\u003e A portion of this work used code generously provided by Brian Blaylock's Herbie python package (https://doi.org/10.5281/zenodo.4567540)\n\n---\n\n## Contributing\n\nWe welcome contributions! Here's how you can help:\n\n- ⭐ Star this repository\n- 👀 Watch for new discussions and issues\n- 💬 Participate in [GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions)\n- 🙌 Share your work in [Show and Tell](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)\n- 🐛 Report bugs or suggest features via [Issues](https://github.com/blaylockbk/Herbie/issues)\n- 📝 Improve documentation\n- 🧪 Test latest releases\n- 💻 Submit pull requests\n\nRead the [Contributing Guide](https://herbie.readthedocs.io/en/stable/user_guide/housekeeping/disclaimer.html) for more details.\n\n---\n\n\n## Project History and Background\n\nDuring my PhD at the University of Utah, I created, at the time, the [only publicly-accessible archive of HRRR data](http://hrrr.chpc.utah.edu/). Over 1,000 research scientists and professionals used that archive.\n\n\u003cblockquote\u003e\u003ccite\u003e\n\u003cp style=\"padding-left: 22px ; text-indent: -22px ;\"\u003e Blaylock B., J. Horel and S. Liston, 2017: Cloud Archiving and Data Mining of High Resolution Rapid Refresh Model Output. Computers and Geosciences. 109, 43-50. \u003ca href=\"https://doi.org/10.1016/j.cageo.2017.08.005\"\u003ehttps://doi.org/10.1016/j.cageo.2017.08.005\u003c/a\u003e.\u003c/p\u003e\n\u003c/cite\u003e\u003c/blockquote\u003e\n\nHerbie was then developed to access HRRR data from that archive and was first used on the Open Science Grid.\n\n\u003cblockquote\u003e\u003ccite\u003e\n\u003cp style=\"padding-left: 22px ; text-indent: -22px ;\"\u003e Blaylock, B. K., J. D. Horel, and C. Galli, 2018: High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment. J. Atmos. Oceanic Technol., 35, 2213–2227, \u003ca href=\"https://doi.org/10.1175/JTECH-D-18-0073.1\"\u003ehttps://doi.org/10.1175/JTECH-D-18-0073.1\u003c/a\u003e.\u003c/p\u003e\n\u003c/cite\u003e\u003c/blockquote\u003e\n\nIn 2020, the HRRR dataset was made available through the [NOAA Open Data Dissemination Program](https://www.noaa.gov/information-technology/open-data-dissemination). Herbie evolved from my original download scripts into a comprehensive package supporting multiple models and data sources.\n\n**Name Origin:** I originally released this package under the name “HRRR-B” because it only worked with the HRRR dataset; the “B” was for Brian. Since then, I have added the ability to download many more models including RAP, GFS, ECMWF, GEFS, and RRFS with the potential to add more models in the future. Thus, this package was renamed **_Herbie_**, named after one of my favorite childhood movies.\n\nThe University of Utah MesoWest group now manages a [HRRR archive in Zarr format](http://hrrr.chpc.utah.edu/). Maybe someday, Herbie will be able to take advantage of that archive.\n\n## About the Author\n\n**Thanks for using Herbie, and happy racing!** 🏁\n\nBrian Blaylock  \n[🌐 Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html)\n\n### Other Projects by Brian\n\n- [🌎 **GOES-2-go**](https://github.com/blaylockbk/goes2go) - Download GOES satellite data and create RGB composites\n- [🌡 **SynopticPy**](https://github.com/blaylockbk/SynopticPy) - Access mesonet data from the Synoptic API  \n- [🔨 **Carpenter Workshop**](https://github.com/blaylockbk/Carpenter_Workshop) - Useful tools for meteorological data analysis\n- [💬 **Bubble Print**](https://github.com/blaylockbk/BubblePrint) - Add personality to your Python print statements\n- [🌹 **Pandas Rose**](https://github.com/blaylockbk/pandas-rose) - Easier wind rose plots\n- [📜 **MET Syntax**](https://github.com/blaylockbk/vscode-met-syntax) - VS Code syntax highlighting for Model Evaluation Tools\n\n---\n\n## Alternative Tools\n\n**rclone:** As an alternative to Herbie, you can use [rclone](https://rclone.org/) to download files from remote archives. 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