Spectral Feature-Based Classification of Wind Profiler Power Spectra

Wind profilers (WPs) are coherent pulsed Doppler radars operating in the ultrahigh frequency and very high frequency bands. These atmospheric radars receive backscattered signals from the atmospheric and nonatmospheric targets. Atmospheric targets include clear air turbulence, precipitation, mesospheric turbulence, ionospheric D, E, F regions, and meteors, whereas nonatmospheric targets are objects like airplanes, birds, insects, hills, and so on. Modern WPs operate for long hours and often change radar operating parameters. Echoes from one beam direction are observed for 8 to 32 s and a set of Doppler power spectra is generated. WPs generate more than a hundred sets of Doppler power spectra per hour. These Doppler spectra often contain echoes from more than one target. The studies for atmospheric modeling and prediction require analysis of the backscattered signal. In order to facilitate systematic study, the data need to be classified and archived according to the atmospheric target type. Considering the large data volume consisting of echoes from different atmospheric targets, there is a need of an automated classification technique that segregates the Doppler power spectral data according to the types of atmospheric targets. The technique is expected to operate in real time with the data generation. This paper presents a spectral feature-based classification (SFBC) method for the classification of Doppler power spectra. This method associates three to four descriptor spectral features with each type of atmospheric target. The SFBC method classifies the data into a particular type of the atmospheric target if concurrent occurrence of descriptor features corresponding to that target type is observed. This paper presents the results indicating that the SFBC classifies the data from different WP radars with good accuracy. It also shows that the SFBC method is computationally simpler compared with other established classification methods.