Abstract
Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The accurate long-term observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutter, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutter for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width, and their dependence on the time of the day, height, and season are used as the discriminants. A low-pass spatial filter is applied to the Bayesian undecided classification mask by considering the spatial correlation difference between clouds and clutter. The final feature mask result has a good agreement with lidar detection, showing a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the study of their climate effect and the interaction with local abundant dust aerosol in semi-arid regions.
Highlights
Clouds play a crucial role in the Earth–atmosphere system by reflecting solar radiation back to space and trapping outgoing terrestrial radiation (Bony et al, 2015; Fu et al, 2000, 2018; Quaas et al, 2016)
In the context of global warming, tropical low-level cloud amount decreases because of stronger surface turbulent fluxes and drier planetary boundary layer, generating a positive climate feedback through a reduction in the reflection of shortwave radiation (Brient and Bony, 2012; Zhang et al, 2018), while the liquid water path of low-level clouds over midlatitudes to high latitudes tends to increase due to a reduced conversion efficiency of liquid water to ice and precipitation, which leads to a negative feedback (Ceppi et al, 2016; Terai et al, 2016)
If we put them into the cloud category, it would affect the accuracy of the created probability density function (PDF) to characterize clouds and clutter
Summary
Clouds play a crucial role in the Earth–atmosphere system by reflecting solar radiation back to space and trapping outgoing terrestrial radiation (Bony et al, 2015; Fu et al, 2000, 2018; Quaas et al, 2016). X. Hu et al.: A robust low-level cloud and clutter discrimination method cloud radars (MMCRs) being deployed all over the world (Arulraj and Barros, 2017; Huo et al, 2020; Kollias et al, 2019). Hu et al.: A robust low-level cloud and clutter discrimination method cloud radars (MMCRs) being deployed all over the world (Arulraj and Barros, 2017; Huo et al, 2020; Kollias et al, 2019) Their short wavelengths allow the radars to detect clouds with small droplets and infer the microphysical and dynamical cloud processes (Kollias et al, 2007a).
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