A simple biota removal algorithm for 35 GHz cloud radar measurements

Abstract

Abstract. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and nonhydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume targetbased theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ∼ 4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS.

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

“…The radar background noise is firstly removed using the noise-equivalent reflectivity (NER) (Kalapureddy et al, 2018), which is r 2 × Z start range , where r is height and Z start range is the noise-equivalent reflectivity of the first range gate from the bottom. Here, we use a Z start range of −60 dBZ, because it fits the radar noise level well after several trials.…”

“…4e) is located as the middle of the melting layer for each identified precipitation profile; then the height of maximum (|reflectivity | × |velocity |) up to 500 m above (below) the peak is defined as the top (bottom) of melting layer as shown in Fig. 4e with red dots (Devisetty et al, 2019;Khanal et al, 2019). The identified melting layer and precipitation are plotted in Fig.…”

“…9a). Note that for these flat clouds, Kalapureddy et al (2018) used the standard deviation of reflectivity to remove clutter. However, this method causes some false positives (clouds are wrongly identified as clutter) around fuzzy cloud edges.…”

Using cloud radar to investigate the effect of rainfall on migratory insect flight

“…Increasingly sophisticated methods to identify and remove insect clutter have been developed (see e.g. Kalapureddy et al, 2018; Luke et al, 2008; Williams et al, 2018, 2021). Generally, these algorithms delineate range gates containing insects, clouds, an insect/cloud mix or precipitation.…”

Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system

“…A narrowbeamwidth antenna makes the cloud radars less susceptible to non-meteorological signals in contrast to high-power longwavelength radars (Kollias et al, 2007). To discriminate clutter echoes from clouds, some algorithms, e.g., based on the coherent characteristics of clouds (Kalapureddy et al, 2018), the Bayesian method (Hu et al, 2021) or polarimetric measurements (Martner and Moran, 2001), have been proposed. But such approaches fall short when meteorological signals are mixed with clutter.…”