gridwxcomp

gridwxcomp: A Python package to evaluate and interpolate biases between station and gridded weather data - Published in JOSS (2025)

https://github.com/wswup/gridwxcomp

Keywords

Scientific Fields

Repository

Comparison of weather station and gridded climate datasets

README.rst

gridwxcomp
==========

|Build| |Documentation Status| |Downloads per month| |PyPI version| |JOSS|

-----------

A package for comparing weather station data to gridded weather data that are hosted on Google Earth Engine. Major functionality includes: 

* parsing of multiple weather stations and weather variables and metadata
* downloading point data from gridded datasets on Google Earth Engine at weather station locations 
* temporal pairing of station and gridded data
* unit handling and automated conversions
* calculation of mean bias ratios between station and gridded data and related statistics 
* performing spatial mapping and interpolation of bias ratios with multiple options 
* calculation of residuals between spatially interpolated bias ratios and those computed at station locations 
* building geo-referenced vector and raster data of spatially interpolated and point data
* zonal averaging of spatially interpolated bias results using a fishnet grid  
* interactive graphics (time series, scatter, and bar charts) comparing station and gridded data

Bias ratios calculated by ``gridwxcomp`` can be used to correct bias of grid to station data based on the properties of the stations. For example, monthly humidity ratios between station and grid for stations within agricultural settings can be used to estimate grid bias relative to agricultural locations. 

``gridwxcomp`` has been used to create monthly bias ratios of `gridMET `_ reference evapotranspiration (ETo) data relative to ETo calculated at irrigated weather stations. The bias ratios were subsequently interpolated and used to correct gridMET ETo which is a key scaling flux for most of the remote sensing models that are part of the `OpenET `_ platform. 

Documentation
-------------
`Online documentation `_

Installation
------------

Currently we recommend using the provided conda environment file to install ``gridwxcomp`` and its dependencies in a virtual environment. Download the `environment.yml `_ file and then install and activate it. If you don't have conda `get it here `_. To install dependencies in a virtual environment run 

.. code-block:: bash

    $ conda env create -f environment.yml

To activate the environment before using ``gridwxcomp`` run

.. code-block:: bash

    $ conda activate gridwxcomp

After installing all the dependencies using conda, install ``gridwxcomp`` using `pip `_,

.. code-block:: bash

    $ pip install gridwxcomp

Due to dependency conflicts you may have issues directly installing with pip before activating the conda environment. This is because the package includes several modules that are not pure Python such as GDAL and pyproj which seem to be better handled by conda. 

Alternatively, or if there are installation issues, you can manually install. First activate the ``gridwxcomp`` conda environment (above). Next, clone or download the package from `GitHub `_ or `PyPI `_ and then install locally with pip in "editable" mode. For example with cloning,

.. code-block:: bash

    $ git clone https://github.com/WSWUP/gridwxcomp.git
    $ cd gridwxcomp

If you are experiencing errors on installing the ``gridwxcomp`` conda environment above with dependencies. For example, if the Shapely package is not installing from the enironment.yml file, remove it or modify it from the "setup.py" file in the install requirements section before you install gridwxcomp from source with:

.. code-block:: bash

    $ pip install -e .

More help with installation issues related to dependency conflicts can be found in the ``gridwxcomp`` `issues `_ on GitHub, be sure to check the closed issues as well.

How to contribute
-----------------
We welcome contributions, big or small, from the community to ``gridwxcomp``! Please review our `Contribution and community guidelines `_ for more information. 

How to cite
-----------
If you use ``gridwxcomp`` for research or published works, please use the following citation: 

Volk et al., (2025). *gridwxcomp: A Python package to evaluate and interpolate biases between station and gridded weather data*. Journal of Open Source Software, 10(105), 7178. https://doi.org/10.21105/joss.07178


.. |Build| image:: https://github.com/WSWUP/gridwxcomp/actions/workflows/gridwxcomp_tests.yml/badge.svg
   :target: https://github.com/WSWUP/gridwxcomp/actions

.. |Downloads per month| image:: https://img.shields.io/pypi/dm/gridwxcomp.svg
   :target: https://pypi.python.org/pypi/gridwxcomp/

.. |Documentation Status| image:: https://img.shields.io/website-up-down-green-red/http/shields.io.svg
   :target: https://wswup.github.io/gridwxcomp/

.. |PyPI version| image:: https://img.shields.io/pypi/v/gridwxcomp.svg
   :target: https://pypi.python.org/pypi/gridwxcomp/

.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.07178/status.svg
   :target: https://doi.org/10.21105/joss.07178

Owner

JOSS Publication

gridwxcomp: A Python package to evaluate and interpolate biases between station and gridded weather data

Published

January 20, 2025

DOI

10.21105/joss.07178

Volume 10, Issue 105, Page 7178

Authors

John M. Volk
Desert Research Institute, Reno, USA

Christian Dunkerly
Desert Research Institute, Reno, USA

Christopher Pearson
Desert Research Institute, Reno, USA

Charles G. Morton
Desert Research Institute, Reno, USA

Justin L. Huntington
Desert Research Institute, Reno, USA

Editor

Hugo Ledoux

Tags

hydrology interpolation weather station meteorology Google Earth Engine

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