Identifying Multimodal Spectra Events with Doppler Radar Observations

Radar spectra are valuable tools, rich in information, though very computationally expensive to process in large numbers. One avenue of particular scientific interest has been multimodal spectra from vertically pointing radar, which can be indicative of microphysical processes such as primary ice nucleation, secondary ice multiplication, and drizzle onset. By efficiently identifying multi-modal spectra cases, we can better develop a large dataset for statistical analyses and establish potential case studies of microphysical processes of interest. This has motivated us to seek a way to better identify these multi-modal spectra events through the use of radar moments, which are more computationally feasible to process in large numbers.

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.