  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.
