LAIR: Land-Air Interactions Research#

lair is a collection of tools that I have developed/acquired for my research regarding land-air interactions. It is designed to make it easier to work with atmospheric data: meteorological calculations, emissions inventories, NOAA greenhouse-gas products, soundings, HRRR winds, background estimation, mobile transects, geospatial helpers, and plotting.

Installation#

Install from the git repository with pip:

pip install git+https://github.com/jmineau/lair.git

or clone the repository and install it as an editable package:

git clone https://github.com/jmineau/lair.git
cd lair
pip install -e .

lair requires Python 3.10 or higher.

Optional dependencies#

The core install is light. Modules that need heavier packages import them only when used; install the matching extra (e.g. pip install "lair[geo,science]"):

Extra	Packages	Used by
`requests`	boto3, requests, s3fs, siphon	`hrrr`, `soundings`
`formats`	zarr, numcodecs, fastkml, lxml	`hrrr`, `records.read_kml`
`geo`	cartopy, networkx, pyproj, rasterio, rioxarray, shapely	`geo`, `hrrr`, `inventories`, `transects`
`science`	molmass, scipy	`background`, `inventories`, `transects`
`regridding`	xesmf	`geo` (regrid / resample)
`complete`	all of the above

xesmf needs the ESMF library, which is easiest to get from conda-forge:

mamba install -c conda-forge esmpy

Data locations#

lair has no built-in data paths. Functions and classes that read from a local data archive take an explicit directory argument and otherwise fall back to an environment variable:

Variable	Used by
`LAIR_INVENTORY_DIR`	`lair.inventories` (root with `EDGAR/`, `EPA/`, `GFEI/`, `vulcan/`, `WetCHARTs/`)
`LAIR_SOUNDING_DIR`	`lair.soundings` (one subdirectory per station)
`LAIR_CARBONTRACKER_DIR`	`lair.noaa.CarbonTracker`
`LAIR_GML_DIR`	`lair.noaa.GMLData`
`LAIR_CACHE_DIR`	cached results (default `~/.cache/lair`)

For example:

export LAIR_INVENTORY_DIR=/path/to/inventories

Verbosity#

Verbosity is set via lair.config.verbose as a boolean:

import lair
lair.config.verbose = False

For early versions of the package verbose defaults to True; this will change in a future version.

Acknowledgements#

This package was partially inspired by, and uses some code generously provided by, Brian Blaylock’s Carpenter Workshop. Background filtering wraps NOAA GML’s CCG curve-fitting code.

Disclaimer#

Portions of this package were written with AI-based tools including GitHub Copilot, ChatGPT, and Google Gemini.
Various code snippets were borrowed from StackOverflow and other online resources.

Contributing#

Contributions are welcome! Please take a look at the current issues and feel free to submit a pull request with new features or bug fixes. Please document your code using numpydoc style docstrings.

Citation#

If you use any portion of this package in your research, please cite the software and/or acknowledge me.