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https://github.com/blaylockbk/Herbie

Download numerical weather prediction datasets (HRRR, RAP, GFS, IFS, etc.) from NOMADS, NODD partners (Amazon, Google, Microsoft), ECMWF open data, and the University of Utah Pando Archive System.
https://github.com/blaylockbk/Herbie

big-data-program cfgrib download ecmwf-data gfs grib grib2 hrrr noaa-data nomads numerical-weather-prediction open-data python rap xarray

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Download numerical weather prediction datasets (HRRR, RAP, GFS, IFS, etc.) from NOMADS, NODD partners (Amazon, Google, Microsoft), ECMWF open data, and the University of Utah Pando Archive System.

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README 

        



![](https://github.com/blaylockbk/Herbie/blob/main/images/logo_new/Herbie-logo.png?raw=True)



# Herbie: Retrieve NWP Model Data 🏁



[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/herbie-data)

](https://pypi.org/project/herbie-data/) [![PyPI](https://img.shields.io/pypi/v/herbie-data)](https://pypi.python.org/pypi/herbie-data/)

[![Conda Version](https://img.shields.io/conda/vn/conda-forge/herbie-data)

](https://anaconda.org/conda-forge/herbie-data)

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[![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)



![Conda Downloads](https://img.shields.io/conda/d/conda-forge/herbie-data)

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## [📘 Documentation](https://herbie.readthedocs.io/) | [💬 Discussions](https://github.com/blaylockbk/Herbie/discussions) | [❔ Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)






**Herbie** is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. NWP data is distributed in GRIB2 format which Herbie reads using xarray+cfgrib. Herbie also provides some extra features to help visualize and extract data.



Herbie helps you discover, download, and read data from:



- [High Resolution Rapid Refresh (HRRR)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrr.html) | [HRRR-Alaska](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrrrak.html)

- [Rapid Refresh (RAP)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rap.html)

- [Global Forecast System (GFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gfs.html)

- [Global Ensemble Forecast System (GEFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/gefs.html)

- [ECMWF Open Data Forecasts](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/ecmwf.html) (IFS and AIFS)

- [Navy Global Environmental Model (NAVGEM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/navgem.html)

- [North American Mesoscale Model (NAM)](https://github.com/blaylockbk/Herbie/blob/main/docs/user_guide/_model_notebooks/nam.ipynb)

- [National Blend of Models (NBM)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/nbm.html)

- [Rapid Refresh Forecast System (RRFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rrfs.html) _prototype_

- [Real-Time/Un-Restricted Mesoscale Analysis (RTMA/URMA)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/rtma.html)

- [Hurricane Analysis And Forecast System (HAFS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hafs.html)

- [High Resolution Deterministic Prediction System (HRDPS)](https://herbie.readthedocs.io/en/latest/user_guide/_model_notebooks/hrdps.html)



Much of this data is made available through the [NOAA Open Data Dissemination](https://www.noaa.gov/information-technology/open-data-dissemination) (NODD) program (formerly the Big Data Program) which has made weather data more accessible than ever before.



## Installation



The easiest way to instal Herbie and its dependencies is with [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) from conda-forge.



```bash

conda install -c conda-forge herbie-data

```



You may also create the provided Conda environment, **[`environment.yml`](https://github.com/blaylockbk/Herbie/blob/main/environment.yml)**.



```bash

# Download environment file

wget https://github.com/blaylockbk/Herbie/raw/main/environment.yml



# Modify that file if you wish.



# Create the environment

conda env create -f environment.yml



# Activate the environment

conda activate herbie

```



Alternatively, Herbie is published on PyPI and you can install it with pip, _but_ it requires some dependencies that you will have to install yourself:



- Python 3.9+

- [cURL](https://anaconda.org/conda-forge/curl)

- [eccodes](https://anaconda.org/conda-forge/eccodes), a requirement for [cfgrib](https://github.com/ecmwf/cfgrib).

- _Optional:_ [wgrib2](https://anaconda.org/conda-forge/wgrib2)



When those are installed within your environment, _then_ you can install Herbie with pip.



```bash

# Latest published version

pip install herbie-data



# ~~ or ~~



# Most recent changes

pip install git+https://github.com/blaylockbk/Herbie.git



# Dependecies for extra features

pip install herbie-data[extra]

```



## Capabilities



- Search for model output from different data sources.

- Download full GRIB2 files.

- Download subset GRIB2 files (by grib field).

- Read data with xarray.

- Read index file with Pandas.

- Extra features (herbie xarray accessors)

  - Extract data at a point

  - Get Cartopy coordinate references system

  - Plot data with Cartopy (very early development).



```mermaid

  graph TD;

      d1[(HRRR)] -..-> H

      d2[(RAP)] -.-> H

      d3[(GFS)] -..-> H

      d33[(GEFS)] -.-> H

      d4[(IFS)] -..-> H

      d44[(AIFS)] -..-> H

      d5[(NBM)] -.-> H

      d6[(RRFS)] -..-> H

      d7[(RTMA)] -.-> H

      d8[(URMA)] -..-> H

      H((Herbie))

      H --- .inventory

      H --- .download

      H --- .xarray



      style H fill:#d8c89d,stroke:#0c3576,stroke-width:4px,color:#000000

```



```python

from herbie import Herbie



# Herbie object for the HRRR model 6-hr surface forecast product

H = Herbie(

  '2021-01-01 12:00',

  model='hrrr',

  product='sfc',

  fxx=6

)



# Look at file contents

H.inventory()



# Download the full GRIB2 file

H.download()



# Download a subset, like all fields at 500 mb

H.download(":500 mb")



# Read subset with xarray, like 2-m temperature.

H.xarray("TMP:2 m")

```



## Data Sources



Herbie downloads model data from the following sources, but can be extended to include others:



- [NOMADS](https://nomads.ncep.noaa.gov/)

- [NOAA Open Data Dissemination Program (NODD)](https://www.noaa.gov/information-technology/open-data-dissemination) partners (i.e., AWS, Google, Azure).

- [ECMWF Open Data Forecasts](https://www.ecmwf.int/en/forecasts/datasets/open-data)

- University of Utah CHPC Pando archive

- Local file system



## Community



Having trouble using Herbie or have a question? [❔ GitHub Discussions/Ask For Help](https://github.com/blaylockbk/Herbie/discussions/categories/q-a)



Just want to talk about Herbie or have an idea? [💬 GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories)



See something that might be wrong? [🚑 GitHub Issues](https://github.com/blaylockbk/Herbie/issues)



Want to contribute? Great! I'd love your help.



- "Watch" this repo's discussions and issues.

- Participate in [💬 GitHub Discussions](https://github.com/blaylockbk/Herbie/discussions/categories) and answer questions.

- Share how you use Herbie in [🙌 GitHub Discussions/Show and tell](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)

- Open an issue or file a pull request to make Herbie even better!

- Contribute to documentation.

- Test latest releases and report issues.

- Read the [👨🏻‍💻 Disclaimer & Contributing Guide](https://herbie.readthedocs.io/en/stable/user_guide/housekeeping/disclaimer.html)



## How to Cite and Acknowledge



If Herbie played an important role in your work, please [tell me about it](https://github.com/blaylockbk/Herbie/discussions/categories/show-and-tell)! Also, consider including a citation or acknowledgement in your article or product.



**_Suggested Citation_**



> Blaylock, B. K. (YEAR). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 20xx.x.x) [Computer software]. https://doi.org/10.5281/zenodo.4567540



**_Suggested Acknowledgment_**



> A portion of this work used code generously provided by Brian Blaylock's Herbie python package (Version 20xx.x.x) (https://doi.org/10.5281/zenodo.4567540)






## History



During 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.





 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. https://doi.org/10.1016/j.cageo.2017.08.005.





Herbie was then developed to access HRRR data from that archive and was first used on the Open Science Grid.





 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, https://doi.org/10.1175/JTECH-D-18-0073.1.





In the later half of 2020, the HRRR dataset from 2014 to present was made available through the [NODD Open Data Dissemination Program](https://www.noaa.gov/information-technology/open-data-dissemination) (formerly NOAA's Big Data Program). The latest version of Herbie organizes and expands my original download scripts into a more coherent package with the extended ability to download data for other models from many different archive sources, and it will continues to evolve.



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.



The 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.



**Thanks for using Herbie, and happy racing!**



🏁 Brian



- [🌐 Personal Webpage](http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/home.html)







P.S. If you like Herbie, check out my other repos:



- [🌎 GOES-2-go](https://github.com/blaylockbk/goes2go): A python package to download GOES-East/West data and make RGB composites.

- [🌡 SynopticPy](https://github.com/blaylockbk/SynopticPy): A python package to download mesonet data from the Synoptic API.

- [🔨 Carpenter Workshop](https://github.com/blaylockbk/Carpenter_Workshop): A python package with various tools I made that are useful (like easy funxtions to build Cartopy maps).

- [💬 Bubble Print](https://github.com/blaylockbk/BubblePrint): A silly little python package that gives your print statement's personality.

- [📜 MET Syntax](https://github.com/blaylockbk/vscode-met-syntax): An extension for Visual Studio Code that gives syntax highlighting for Model Evaluation Tools (MET) configuration files.



> **Note**: Alternative Download Tools  

> As an alternative to Herbie, you can use [rclone](https://rclone.org/) to download files from AWS or GCP. I love rclone. Here is a short [rclone tutorial](https://github.com/blaylockbk/pyBKB_v3/blob/master/rclone_howto.md)



| [Visualize Structure](https://mango-dune-07a8b7110.1.azurestaticapps.net/?repo=blaylockbk%2FHerbie) | [Star History](https://star-history.com/#blaylockbk/Herbie&Date) | [PyPI Download Statistics](https://pypistats.org/packages/herbie-data)