Tracking Pyrometeors With Meteorological Radar Using Unsupervised Machine Learning

Abstract

AbstractPyrometeors are the large (>2 mm) debris lofted above wildfires that are composed of the by‐products of combustion of the fuels. One speciation of pyrometeor is firebrands, which are burning debris that lead to ignitions ahead of the surface fire and can be the dominant mechanism of fire spread and structure loss. Pyrometeors are observed by meteorological radar. To date, there have been no investigations into identification of pyrometeor speciation with radar. Here we present an unsupervised machine learning method (Gaussian mixture model) to classify pyrometeor modes using X‐band radar data. The coherent features of the mode of pyrometeor identified most likely to transport firebrands were tracked in time and space. The radar classification and tracking method shows that wildfires do produce signatures in radar returns that could be used for spot fire risk prediction. In wildfires, different types of debris (known as pyrometeors) are lofted in the smoke plumes and transported downwind. Some types of pyrometeors may, when in the air, still be burning and capable of starting new wildfires. Here we investigate the potential for meteorological radar to classify different types of pyrometeors and to track them to determine their potential for starting new fires downwind of the main fire front.