A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar
Xiaoyu Hu,Jinming Ge,Jiajing Du,Qinghao Li,Jianping Huang,and Qiang Fu
Xiaoyu Hu
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Jiajing Du
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qinghao Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou,
730000, China
Qiang Fu
Department of Atmospheric Sciences, University of Washington, Seattle, WA 98105, USA
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Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutter. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutter by considering their different features in several variables. We applied this method to the radar observations at the SACOL site and found the results have good agreement with lidar detection.
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar...
