---
id: herbie-data
version: "2026.3.0"
license: MIT License Copyright (c) 2019-2024 Brian K. Blaylock Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# herbie-data — Download numerical weather prediction GRIB2 model data.

License: permissive · Maintenance: active · Downloads: 184.8K/mo

## 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 above — verify before relying.

Herbie downloads numerical weather prediction model data (GRIB2 files) from NOAA, ECMWF, and other sources, then loads it into xarray for analysis and visualization.

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

pip install herbie-data
uv add herbie-data
poetry add herbie-data

## Installing herbie-data

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.

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')

Requires Python 3.11 or later. Optional features (wgrib2 integration) require manual system installation of wgrib2.

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: MIT License Copyright (c) 2019-2024 Brian K. Blaylock Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 184.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

weather forecast data download, GRIB2 file retrieval, numerical weather prediction access, HRRR GFS RAP download, atmospheric model data python, meteorological forecast API, weather data xarray, weather-data, atmospheric-science, grib2

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