This release is a pre-release and may not be stable for production use.
arl-met
Read, write, subset, and sample NOAA ARL meteorological files.
arl-met provides a Python-first interface to the ARL packed meteorology
format used by HYSPLIT and related workflows. It supports:
- low-level record-preserving reads and writes through
File,RecordSet, andDataRecord - xarray Dataset reads and common-case writes through
open_dataset()andwrite_dataset(), plus an xarray backend (xr.open_dataset(path, engine="arl")) - downloading from NOAA's ARL archives (HRRR, NAM, GDAS, GFS, ...) with optional crop-on-download
- crop-before-unpack subset extraction with
extract_subset() - vertical helper functions such as
pressure(),z_agl(), andz_msl() - point sampling with
sample_points() - joining files with
concat()andconcat_by_time()(e.g. 6-hourly into daily) - finding damage that does not stop a file from opening (a time step written partway, a missing time step) with
File.check()
Status
arl-met is in beta. The core API is settled enough to build on, but it may still change before 1.0; breaking changes are marked Breaking in the changelog.
Strengths:
- low-level ARL fidelity, including preservation of trailing
DIF*records - xarray-native analysis workflow for common ARL files
- direct subset extraction and point sampling
- tested support for Python 3.11 through 3.14
Known limitations:
write_dataset()is intentionally conservative and targets the flat common-case Dataset contract- complex multi-record DIFF chains are not tested
- WRF vertical flag 5 and rotated grids are not implemented
Installation
Install the core package:
pip install arlmet
Install the optional archive-download dependencies:
pip install "arlmet[archives]"
For development:
git clone https://github.com/jmineau/arl-met.git
cd arl-met
uv sync
uv run pre-commit install
just quality-check # lint, type check, docstrings, tests
Quick examples
Open an ARL file as a Dataset:
import arlmet
ds = arlmet.open_dataset("met.arl")
print(ds)
# or through xarray, e.g. to get dask-backed arrays
import xarray as xr
ds = xr.open_dataset("met.arl", engine="arl", chunks={"time": 1})
Modify a Dataset and write it back:
import arlmet
ds = arlmet.open_dataset("met.arl")
ds["TEMP"] = ds["TEMP"] - 273.15
ds["WWND"].attrs["diff"] = "DIFW"
arlmet.write_dataset(ds, "edited.arl")
Extract a subset without unpacking the full file first:
import arlmet
arlmet.extract_subset(
"met.arl",
"subset.arl",
bbox=(-114.0, 39.0, -110.0, 42.0),
levels=[0, 1, 2],
) # returns the output path (a pathlib.Path)
Join short files into longer ones (HYSPLIT accepts at most 12 met files per run):
import arlmet
# join a list of files into one (ordered by valid time)
arlmet.concat(["20240101_00_hrrr", "20240101_06_hrrr"], "20240101_hrrr")
# or batch a whole directory into daily files
arlmet.concat_by_time(
"hrrr/", "daily/", freq="1D", pattern="*_hrrr", template="{time:%Y%m%d}_hrrr"
)
Check a file for damage before a HYSPLIT run:
import arlmet
with arlmet.File("20210226_18-23_hrrr") as met:
print(met.check()) # [] for a whole file
Use the low-level writer for irregular layouts:
import numpy as np
import pandas as pd
import arlmet
grid = arlmet.Grid(
projection=arlmet.Projection(
pole_lat=90.0,
pole_lon=0.0,
tangent_lat=1.0,
tangent_lon=1.0,
grid_size=0.0,
orientation=0.0,
cone_angle=0.0,
sync_x=1.0,
sync_y=1.0,
sync_lat=-10.0,
sync_lon=20.0,
),
nx=20,
ny=20,
)
vertical_axis = arlmet.PressureAxis(levels=[0.0, 1000.0])
time = pd.Timestamp("2024-07-18 00:00")
prss = np.ones((grid.ny, grid.nx), dtype=np.float32)
wwnd = np.ones((grid.ny, grid.nx), dtype=np.float32)
with arlmet.File("custom.arl", mode="w", source="TEST", grid=grid, vertical_axis=vertical_axis) as arl:
rs = arl.create_recordset(time, forecast=0)
rs.create_datarecord("PRSS", level=0, forecast=0, data=prss)
rs.create_datarecord("WWND", level=1, forecast=0, data=wwnd, diff="DIFW")
Records are held in memory until the file is flushed or closed. When writing
many time steps, call arl.flush() after filling each one so memory stays
bounded by a single time step.
Resources
Documentation is available at https://jmineau.github.io/arl-met/
Useful ARL/HYSPLIT references:
- HYSPLIT User Guide for the broader model and file-format context.
- HYSPLIT meteorology page for the ARL meteorology format overview.
- READY archive for the available meteorology archives.
- GDAS1 packing notes for a concrete example of ARL packing behavior.
Related project:
- ARLreader, which focuses on GDAS1 files, while
arl-mettargets a broader ARL/xarray workflow.
Release notes
See CHANGELOG.md for release history.
Contributing
Contributions are welcome. See CONTRIBUTING.md for development setup and contribution guidelines.
Use of AI coding agents
This project is developed with the help of AI coding agents, directed and reviewed by the maintainer, who owns the design and the science.
License
This project is licensed under the MIT License. See LICENSE.
1 maintainer
Metadata
1 maintainer
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