We developed criteria that can be used to automatically detect multimodal spectra by analyzing only radar moment data. Specifically, we analyze the time-averaged Doppler spectrum width <SW> and standard deviation of the mean Doppler velocity σMDV from vertically pointing scans; cases with large <SW> coupled with small σMDV are flagged as having potential multimodal spectra. These criteria were developed using data from three independent Ka-band radars, including two U.S. Department of Energy Atmospheric Radiation Measurement (DOE-ARM) Ka-band Zenith-pointing radars (KAZRs) -- one at the North Slope of Alaska (NSA) site in Utqiaġvik, and one in the Southern Great Plains (SGP) site in Oklahoma -- as well as the Ka-band Scanning Polarimetric Radar (KASPR) located at Stony Brook University on Long Island, New York.
To verify the algorithm, we applied these criteria to one year of months November-April from the KAZR at the NSA site and found that 75% of the flagged cases contained multimodal spectra. After verifying the algorithm’s performance in correctly identifying multimodal spectra, we applied it to a long-term dataset from NSA. From this large dataset, we examine the most common features and conditions associated with multimodal spectra events. Such a tool is useful for constructing a climatology of multimodal spectra and highlighting the associated microphysical processes.

