The Relative Clutter Adjustment (RCA) technique is used for radar calibration monitoring, by estimating and correcting for unwanted "clutter" signals that can arise from various sources such as buildings, mountains, and trees. This repository contains code for performing RCA on weather radar data, as well as an example Jupyter notebook to demonstrate the technique.

The following Python libraries are required to use cluttercal:

• numpy
• pandas
• netCDF4
• xarray
• dask
• pyodim

You can install the core dependencies using pip:

pip install numpy pandas netCDF4 xarray dask pyodim

An example Jupyter notebook is available in the example directory. This notebook demonstrates how to use the cluttercal library to perform RCA on radar data. The notebook provides step-by-step instructions for downloading a sample of radar data from the Australian weather radar network archive, computing the clutter mask, and extracting the RCA value using the clutter mask. Finally, the notebook uses Matplotlib to create a plot of the RCA value over the radar data.

If you use cluttercal in your research, please cite the following paper:

Louf, Valentin, and Alain Protat. Real-Time Monitoring of Weather Radar Network Calibration and Antenna Pointing. Journal of Atmospheric and Oceanic Technology, April 24, 2023. https://doi.org/10.1175/JTECH-D-22-0118.1.

BibTeX:

@article {Louf2023,
    author = "Valentin Louf and Alain Protat",
    title = "Real-Time Monitoring of Weather Radar Network Calibration and Antenna Pointing",
    journal = "Journal of Atmospheric and Oceanic Technology",
    year = "2023",
    publisher = "American Meteorological Society",    
    volume = "40",
    number = "7",
    doi = "10.1175/JTECH-D-22-0118.1",
    pages=  "823 - 844",    
}

This library is open source and made freely available according to the below text:

Copyright 2020 Valentin Louf
Copyright 2023 Commonwealth of Australia, Bureau of Meteorology

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

A copy of the license is also provided in the LICENSE file included with the source distribution of the library.