Abstract
Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka/Ku radar system. Firstly, non-meteorological clutter close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, the abnormal distribution of the probability density of the Doppler spectrum in presence of range sidelobe due to the implementation of the pulse compression technique was identified and used to separate sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the results show good performance of clutter/sidelobe suppression and spectral merging.
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