Extended Polarimetric Observations of Chaff using the WSR-88D Weather Radar Network

TL;DR

This study analyzes an extensive dataset of chaff observations from the WSR-88D radar network, revealing new insights into its characteristics and aiding the development of algorithms for target identification using machine learning.

Contribution

It provides a comprehensive statistical analysis of chaff using extended radar data, including height dependence and differential reflectivity, supporting improved target classification algorithms.

Findings

Height dependence of chaff characteristics analyzed

Differential reflectivity range extended and studied

Implications for machine learning target separation

Abstract

Military chaff is a metallic, fibrous radar countermeasure that is released by aircraft and rockets for diversion and masking of targets. It is often released across the United States for training purposes, and, due to its resonant cut lengths, is often observed on the S-band Weather Surveillance Radar - 1988 Doppler (WSR-88D) network. Efforts to identify and characterize chaff and other non-meteorological targets algorithmically require a statistical understanding of the targets. Previous studies of chaff characteristics have provided important information that has proven to be useful for algorithmic development. However, recent changes to the WSR-88D processing suite have allowed for a vastly extended range of differential reflectivity, a prime topic of previous studies on chaff using weather radar. Motivated by these changes, a new dataset of 2.8 million range gates of chaff from 267 cases across the United States is analyzed. With a better spatiotemporal representation of cases compared to previous studies, new analyses of height dependence, as well as changes in statistics by volume coverage pattern are examined, along with an investigation of the new "full" range of differential reflectivity. A discussion of how these findings are being used in WSR-88D algorithm development is presented, specifically with a focus on machine learning and separation of different target types.