ELDORA Machine Learning Quality Control Dataset

The repository contains data from the airborne radar quality control process for the ELDORA airborne radar discussed in the manuscript "Airborne Radar Quality Control with Machine Learning" by Alexander J. DesRosiers and Michael M. Bell. The main data files are the '.h5' files which should be stored in a directory titled 'h5_files' locally to try and run the code. The 'make_rf_model.py' script uses the h5 data to train and evaluate a random forest model for radar quality control. The 'utils.py' script contains helper scripts needed to mine data, train, and evaluate models with this methodology which Bruno Melli helped originally develop. The 'rf_ophelia_raw_cfrad_clean.py' script can be used to clean a sample cfradial file included which should be stored in a 'sample_cfrad' directory locally. The model itself is stored in a pickle file titled 'rf_Eldo_model.pkl'.