herbie-without-docs
A repository of Herbie without rendered documentation, just for demonstration purposes
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 6 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.7%) to scientific vocabulary
Keywords from Contributors
Repository
A repository of Herbie without rendered documentation, just for demonstration purposes
Basic Info
- Host: GitHub
- Owner: amotl
- License: mit
- Language: Python
- Default Branch: main
- Size: 46.3 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Herbie is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. Its most popular capability is to download HRRR model data. NWP data in GRIB2 format can be read with xarray+cfgrib. Much of this data is made available through the NOAA Open Data Dissemination (NODD) Program (formerly the Big Data Program) which has made weather data more accessible than ever before.
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 Forecast Products
- North American Mesoscale Model (NAM)
- National Blend of Models (NBM)
- Rapid Refresh Forecast System - Prototype (RRFS)
📓 Herbie Documentation
Installation
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 ```
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 3.8, 3.9, or 3.10 (recommended)
- cURL
- Cartopy, which requires GEOS and Proj.
- cfgrib, which requires eccodes.
- Optional: wgrib2
- Optional: Carpenter Workshop
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 ```
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.
- Plot data with Cartopy (very early development).
```mermaid graph TD; d1[(HRRR)] -.-> H d2[(RAP)] -.-> H d3[(GFS)] -.-> H d33[(GEFS)] -.-> H d4[(ECMWF)] -.-> H d5[(NBM)] -.-> H d6[(RRFS)] -.-> H H((Herbie)) H --- .download H --- .xarray H --- .read_idx
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 )
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
- Big Data Program Partners (AWS, Google, Azure)
- ECMWF Open Data Azure storage
- University of Utah CHPC Pando archive
- Local file system
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
In the later half of 2020, the HRRR dataset from 2014 to present was made available through the NOAA Big Data Program. 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.
I originally released this package under the name “HRRR-B” because it only worked with the HRRR dataset; the “B” was for my first-name initial. Since then, I have added the ability to download RAP, GFS, ECMWF, GEFS, RRFS, and others with potentially more models in the future. Thus, this package was renamed Herbie, named after one of my favorite childhood movie characters.
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.
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. (2022). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 2022.9.0) [Computer software]. https://github.com/blaylockbk/Herbie
Suggested Acknowledgment
A portion of this work used code generously provided by Brian Blaylock's Herbie python package (https://github.com/blaylockbk/Herbie)
Thanks for using Herbie, and happy racing!
🏁 Brian
| | | | :-: | ----------------------------------------------------------------------------------------------------- | | 👨🏻💻 | Contributing Guidelines | | 💬 | GitHub Discussions | | 🚑 | GitHub Issues | | 🌐 | Personal Webpage | | 🌐 | University of Utah HRRR archive |
Cite Herbie:
Blaylock, B. K., 2022: Herbie: Retrieve Numerical Weather Prediction
Model Data [Computer software]. https://doi.org/10.5281/zenodo.6526110.
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
Owner
- Name: Andreas Motl
- Login: amotl
- Kind: user
- Location: $PYTHONPATH
- Repositories: 24
- Profile: https://github.com/amotl
Supporting open source, open access, and open science.
Citation (CITATION.cff)
cff-version: 1.2.0
abstract: Herbie is a python package that downloads recent and archived numerical weather prediction (NWP) model output from different cloud archive sources. Its most popular capability is to download HRRR model data. NWP data in GRIB2 format can be read into an xarray dataframe using the cfgrib package. Much of this data is made available through the NOAA Open Data Dissemination (NODD) Program (formerly the Big Data Program) which has made weather data more accessible than ever before.
message: "If you use this software, please cite it as below."
authors:
- family-names: Blaylock
given-names: Brian K.
email: blaylockbk@gmail.com
orcid: "https://orcid.org/0000-0003-2133-9313"
title: "Herbie: Retrieve Numerical Weather Prediction Model Data"
version: 2022.9.0
date-released: "2022-05-06"
url: "https://blaylockbk.github.io/Herbie/_build/html/"
repository-code: "https://github.com/blaylockbk/Herbie"
type: software
keywords:
- meteorology
- weather
- numerical weather prediction
- forecast
- atmosphere
license: "MIT"
identifiers:
- type: doi
value: 10.5281/zenodo.4567540
GitHub Events
Total
Last Year
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Brian Blaylock | b****k@g****m | 703 |
| Brian Blaylock | b****r@n****l | 13 |
| Coat | a****t@a****r | 12 |
| Tamas Weisz | w****a@g****m | 3 |
| djgagne@ou.edu | d****e@g****m | 2 |
| Haim Daniel | h****l@g****m | 2 |
| Haim Daniel | h****m@j****m | 2 |
| The Gitter Badger | b****r@g****m | 1 |
| Brian | = | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0