herbie-data
Download numerical weather prediction GRIB2 model data.
Decision gist · record as of 2026-08-14
Yes. Herbie is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and solves a real problem for weather data workflows. Install friction is low and dependencies are standard scientific Python packages. Suitable for research, operational meteorology, and data science projects involving atmospheric forecasts.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Optional features (wgrib2 integration) require manual system installation of wgrib2.
- Low friction install with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — MIT License permits commercial and private use, modification, and redistribution with minimal restrictions. You may use this in proprietary projects provided you include the license text and copyright notice.
last release 2026-03-07 (160 days) · last repo commit 2026-06-07 · 780 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 184,844 downloads/mo, #10,023 on PyPI
Alternatives
Verify before relying
pip install herbie-data
from herbie import Herbie
H = Herbie('2021-01-01 12:00', model='hrrr', product='sfc', fxx=6)
temperature = H.xarray('TMP:2 m')- Whether all 15+ supported weather models are equally well-maintained and documented.
- Performance characteristics when downloading large subsets or multiple forecast hours.
- Specific data latency and availability guarantees across different source providers.
- Whether Cartopy integration requires additional system dependencies beyond those listed.
What it is and what it does
Herbie is a Python interface to numerical weather prediction model data from NOAA (HRRR, GFS, RAP, GEFS, NAM, and others), ECMWF (IFS, AIFS), and additional sources. It abstracts away the complexity of locating and downloading GRIB2 files from multiple archives (NOMADS, AWS, Google Cloud, Azure) by automatically searching available sources and downloading either full files or subsets by variable. The package integrates tightly with xarray and pandas, allowing you to load downloaded data directly into analysis-ready formats.
Typical workflows involve creating a Herbie object with a date, model name, and product type, then either downloading raw GRIB2 files or reading specific variables directly into xarray Datasets. It includes a command-line interface for scripting and batch operations, plus built-in helpers for Cartopy-based mapping. The package is designed for researchers, meteorologists, and data scientists working with atmospheric forecasts—both for real-time operational use and historical analysis.
Use it for
- Download high-resolution HRRR surface forecasts for regional weather analysis or nowcasting applications.
- Retrieve specific atmospheric variables (e.g., temperature at 850 mb) from global GFS forecasts for ensemble analysis.
- Batch-download multiple forecast hours across a date range for machine learning training on weather patterns.
- Access ECMWF IFS data for comparison with NOAA models in research or operational workflows.
- Extract point-specific data (e.g., at a weather station location) from GRIB2 files without processing entire grids.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Herbie is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and solves a real problem for weather data workflows. Install friction is low and dependencies are standard scientific Python packages. Suitable for research, operational meteorology, and data science projects involving atmospheric forecasts.
Install
herbie-data on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance (last commit 2026-06-07) and 780 repository stars. Eight runtime dependencies including numpy, pandas, xarray, and cfgrib—all standard scientific Python packages with broad ecosystem support.
Requires Python 3.11 or later. Optional features (wgrib2 integration) require manual system installation of wgrib2.
License in practice
MIT License permits commercial and private use, modification, and redistribution with minimal restrictions. You may use this in proprietary projects provided you include the license text and copyright notice.
Quickstart
pip install herbie-data
from herbie import Herbie
H = Herbie('2021-01-01 12:00', model='hrrr', product='sfc', fxx=6)
temperature = H.xarray('TMP:2 m')Verify before relying
- Whether all 15+ supported weather models are equally well-maintained and documented.
- Performance characteristics when downloading large subsets or multiple forecast hours.
- Specific data latency and availability guarantees across different source providers.
- Whether Cartopy integration requires additional system dependencies beyond those listed.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagescfgribeccodeseccodeslibnumpypandaspyprojrequestsxarray |
| Maintenance | Actively maintained 160 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 184,844 / month, #10,023 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Atmospheric Science |
Evidence: herbie_data-2026.3.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “GRIB2 file retrieval”
- herbie-dataHerbie downloads numerical weather prediction model data (GRIB2…
- ecmwf-opendataDownloads ECMWF open meteorological data using a request-based…
- simple-dwd-weatherforecastRetrieves hourly weather forecasts for the next 10 days from…
Give your agent the search over MCP, or paste the wish link into any chat.
More Atmospheric Science packages
Albumentations applies image transformations to training data, supporting classification, segmentation, object detection, and pose estimation with a unified API for images, masks, bounding boxes, and keypoints.
Install it if you need a unified, production-grade augmentation API for computer vision tasks.
Provides unit-aware measurement objects for Python that support conversion between different units and arithmetic operations across multiple measurement types including distance, weight, temperature, energy, speed, volume, time, and area.
Retrieves and calculates chemical properties and constants for over 20,000 chemicals, including molecular weight, boiling/melting points, vapor pressure, heat capacity, viscosity, and thermodynamic phase equilibrium solutions.
Install it if you need reliable chemical constants, property correlations, or thermodynamic phase equilibrium routines.
Thermo calculates temperature and pressure-dependent thermodynamic and transport properties for pure chemicals and mixtures, including phase equilibria calculations using equations of state.
Install it if you work with thermodynamic modeling, phase equilibria, or chemical property lookups; skip it if your work does not involve chemical engineering…
cwltool is the reference implementation of the Common Workflow Language standard, enabling you to write, validate, and execute portable scientific workflows defined in CWL format across different computing environments.
Install it if you need to execute portable workflows, validate CWL definitions, or integrate CWL workflows into Python applications.
MetPy provides tools for reading, visualizing, and performing calculations on weather data, integrating with the scientific Python ecosystem (NumPy, SciPy, Matplotlib).
Install it if you work with weather or atmospheric data.
See also ecmwf-opendata · atcf-data-parser · cfgrib · ecmwf-api-client · pygrib · earthkit-meteo · simple-dwd-weatherforecast · AI-WQ-package · pdbufr · open-radar-data