Bolide fragment detection in Doppler weather radar data using artificial intelligence/machine learning

Abstract

AbstractUnsupervised machine learning methods present a promising approach for detecting fragments produced from meteors and bolides as distinct signatures within Doppler weather radar data. A method combining principal component analysis (PCA), t‐distributed statistical neighbor embedding (t‐SNE), and data point pruning based on the nearest neighbor algorithm is introduced as a process to detect outlier meteor signatures from terrestrial weather signatures using the national NOAA WSR‐88D Doppler radar network. This method is applied against unlabeled data from four weather radar sites during two bolide events: the KFWS radar for the Ash Creek bolide and the KDAX, KRGX, and KBBX radars for the Sutter’s Mill bolide. The combined algorithm results in an accuracy rate of 99.7% and can classify the data in <8 min for a 121,000 return sized data set. However, the classifier’s recall and precision rates remained low due to difficulties in correctly classifying true‐positive meteorite fall events. This method enables the expedited detection of materials from bolides and meteors that fall within the national radar network, leading to the faster confirmation of meteorite fall events and subsequent dispatch of recovery teams.