Abstract
The new Chinese Ka-band solid-state transmitter cloud radar (CR) uses four operational modes with different pulse widths and coherent integration and non-coherent integration numbers to meet long-term cloud measurement requirements. The CR and an instrument-equipped aircraft were used to observe clouds and precipitation on the east side of Taihang Mountain in Hebei Province in 2018. To resolve the data quality problems caused by attenuation in the precipitation area; we focused on developing an algorithm for attenuation correction based on rain drop size distribution (DSD) retrieved from the merged Doppler spectral density data of the four operational modes following data quality control (QC). After dealiasing Doppler velocity and removal of range sidelobe artifacts; we merged the four types of Doppler spectral density data. Vertical air speed and DSD are retrieved from the merged Doppler spectral density data. Finally, we conducted attenuation correction of Doppler spectral density data and recalculated Doppler moments such as reflectivity; radial velocity; and spectral width. We evaluated the consistencies of reflectivity spectra from the four operational modes and DSD retrieval performance using airborne in situ observation. We drew three conclusions: First, the four operational modes observed similar reflectivity and velocity for clouds and low-velocity solid hydrometeors; however; three times of coherent integration underestimated Doppler reflectivity spectra for velocities greater than 2 m s−1. Reflectivity spectra were also underestimated for low signal-to-noise ratios in the low-sensitivity operational mode. Second, QC successfully dealiased Doppler velocity and removed range sidelobe artifacts; and merging of the reflectivity spectra mitigated the effects of coherent integration and pulse compression on radar data. Lastly, the CR observed similar DSD and liquid water content vertical profiles to airborne in situ observations. Comparing CR and aircraft data yielded uncertainty due to differences in observation space and temporal and spatial resolutions of the data.
Highlights
A Ka-band cloud radar (CR) with a solid-state transmitter using pulse compression, coherent integration, and incoherent integration in several operational modes can detect clouds and precipitation at varying heights and intensities
We developed an attenuation correction based on algorithms for dealiasing singly wrapped aliased Doppler spectral density, detecting and removing artifacts produced by pulse compression, and merging reflectivity spectra [21] using drop size distribution (DSD) retrieved from Doppler spectral density to reduce the attenuation effects on reflectivity
Algorithms for quality control (QC) and merging of Doppler spectral density data from the four operational modes were used to process CR data, and an algorithm for attenuation correction based on retrieved DSD from the merged Doppler spectral density data was used to correct attenuation for reflectivity spectral density
Summary
A Ka-band cloud radar (CR) with a solid-state transmitter using pulse compression, coherent integration, and incoherent integration in several operational modes can detect clouds and precipitation at varying heights and intensities. Coherent integration’s effects on reflectivity estimation were corrected using a power transfer function through coherent integration, and sidelobe artifacts were distinguished using non-range-corrected return power as proposed by Moran et al [2]. Clothiaux et al proposed a radar data-processing algorithm to unalias radial velocity aliasing, remove both second-trip echoes and pulse compression sidelobes, and merge reflectivity and velocity from different operational modes [3]. In those works, millimeter wavelength CR (MMCR) recorded three Doppler spectra moments (i.e., reflectivity, velocity, and spectral width) and discarded the Doppler spectra. In Kollias’ work, precipitation attenuation was not corrected and coherent integration effects on Doppler spectral data were not discussed
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