herbie-data

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

Keywords

Keywords from Contributors

Repository

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.

README.md

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) | HRRR-Alaska
• Rapid Refresh (RAP)
• Global Forecast System (GFS)
• Global Ensemble Forecast System (GEFS)
• ECMWF Open Data Forecasts (IFS and AIFS)
• Navy Global Environmental Model (NAVGEM)
• North American Mesoscale Model (NAM)
• National Blend of Models (NBM)
• Rapid Refresh Forecast System (RRFS) prototype
• Real-Time/Un-Restricted Mesoscale Analysis (RTMA/URMA)
• Hurricane Analysis And Forecast System (HAFS)
• High Resolution Deterministic Prediction System (HRDPS)
• Climate Forecast System (CFS)
• and more! Check out the gallery.

Much of this data is made available through the NOAA Open Data Dissemination (NODD) program which has made weather data more accessible than ever before.

Installation

conda or mamba

The easiest way to instal Herbie and its dependencies is with Conda from conda-forge.

bash conda install -c conda-forge herbie-data

You may also create the provided Conda environment, 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 ```

pip

Alternatively, Herbie is published on PyPI and you can install it 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] ```

Keep in mind that Herbie does require the following dependencies that you will have to install yourself if you don't have them in your PATH:

• Python 3.10+
• cURL
• wgrib2 (for some extra features)

uv

You can add Herbie to your project with the uv command:

bash uv add herbie-data

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).
• Support for custom model templates with Herbie plugins.

```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

```

Herbie Python

Herbie's Python API is used like this:

```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") ```

Herbie CLI

Herbie also has a command line interface (CLI) so you can use Herbie right in your terminal.

```bash

Get the URL for a HRRR surface file from today at 12Z

herbie data -m hrrr --product sfc -d "2023-03-15 12:00" -f 0

Download GFS 0.25° forecast hour 24 temperature at 850mb

herbie download -m gfs --product 0p25 -d 2023-03-15T00:00 -f 24 --subset ":TMP:850 mb:"

View all available variables in a RAP model run

herbie inventory -m rap -d 2023031512 -f 0

Download multiple forecast hours for a date range

herbie download -m hrrr -d 2023-03-15T00:00 2023-03-15T06:00 -f 1 3 6 --subset ":UGRD:10 m:"

Specify custom source priority (check only Google)

herbie data -m hrrr -d 2023-03-15 -f 0 -p google ```

Data Sources

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

• NOMADS
• NOAA Open Data Dissemination Program (NODD) partners (i.e., AWS, Google, Azure).
• ECMWF Open Data Forecasts
• University of Utah CHPC Pando archive
• Local file system

Community

Having trouble using Herbie or have a question? ❔ GitHub Discussions/Ask For Help

Just want to talk about Herbie or have an idea? 💬 GitHub Discussions

See something that might be wrong? 🚑 GitHub Issues

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

• "Watch" this repo's discussions and issues.
• Participate in 💬 GitHub Discussions and answer questions.
• Share how you use Herbie in 🙌 GitHub Discussions/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

How to Cite and Acknowledge

If Herbie played an important role in your work, please tell me about it! 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. 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 (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. Maybe someday, Herbie will be able to take advantage of that archive.

Thanks for using Herbie, and happy racing!

🏁 Brian

• 🌐 Personal Webpage

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

• 🌎 GOES-2-go: A python package to download GOES-East/West data and make RGB composites.
• 🌡 SynopticPy: A python package to download mesonet data from the Synoptic API.
• 🔨 Carpenter Workshop: A python package with various tools I made that are useful (like easy funxtions to build Cartopy maps).
• 💬 Bubble Print: A silly little python package that gives your print statement's personality.
• 📜 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 to download files from AWS or GCP. I love rclone. Here is a short rclone tutorial

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