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Compare meterological station data to gridded data

Project description

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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.


Online documentation


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

$ conda env create -f environment.yml

To activate the environment before using gridwxcomp run

$ conda activate gridwxcomp

Optionally, install using pip,

$ pip install gridwxcomp

Due to dependency conflicts you may have issues directly installing with pip before activating the conda environment.

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,

$ git clone
$ 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 “” file in the install requirements section before you install gridwxcomp from source with:

$ 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.

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