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- Title
A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar.
- Authors
Xiaoyu Hu; Jinming Ge; Jiajing Du; Qinghao Li; Jianping Huang; Qiang Fu
- Abstract
Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The long-term and accurate 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 clutters, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutters 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 dependences 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, considering the spatial correlation difference between clouds and clutters. The resulting feature mask shows a good agreement with lidar detection, which has 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, to study their climate effect and the interaction with local abundant dust aerosol in semi-arid region.
- Subjects
ARID regions; RADAR; HYDROLOGIC cycle; DISTRIBUTION (Probability theory); SPATIAL filters
- Publication
Atmospheric Measurement Techniques Discussions, 2020, p1
- ISSN
1867-8610
- Publication type
Article
- DOI
10.5194/amt-2020-230