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agweather-qaqc provides a flexible workflow for the visualization, review, and QAQC of daily weather data. This script is intended to be used as an early step in any analysis that might use daily sources of agricultural weather data, particularly for projects with an interest in reference evapotranspiration (ET) data, or where observational data are considered to be 'truth' when evaluating model predictions.agweather-qaqc is command-line interface driven, and provides reminders, prompts, and recommendations to assist users who may not be overly proficient with Python.

Functionalities include:

• Importing data without having to convert it to a standardized format, with unit conversions based on a user-specified configuration file.
• Converting multiple input formats from separate sources or networks into a single, uniform format for easier downstream analysis.
• Visualizing data before and after processing with interactive plots, as daily time series and as mean monthly averages.
• Filtering and removal of data, both manually and automatically, with statistics-based approaches to identify and correct issues such as sensor miscalibration.
• Calculation oftheoretical clear-sky solar radiation andThornton-Running solar radiation.
• Calculation of grass and alfalfa reference ET according to theAmerican Society of Civil Engineers Standardized reference evapotranspiration equation via theRefET library.
• Evaluating station aridity through the visualization of both relative humidity and dew point depression plots.
• Optional gap-filling of data using station climatologies, empirical approaches (e.g. Thornton-Running solar), or random sampling.

Github Page

1. Clone the repository:

   git clone https://github.com/WSWUP/agweather-qaqc

2. Navigate the command line/terminal into the repository root directory:

   cd path/to/agweather-qaqc

3. Setting up and activating the environment can be done one of three ways:

   • Conda Environment:

     conda env create -f environment.yml

     conda activate agweatherqaqc

   • Pipenv Environment:

     pipenv install -r requirements.txt

     pipenv shell

   • PDM Environment:

     pdm install

     pdm shell

4. Run the script via the fileqaqc_single_station.py

   python qaqc_single_station.py <OPTIONAL ARGUMENTS>

See thedocumentation for more information.