Articles | Volume 14, issue 2
Research article
03 Mar 2021
Research article |  | 03 Mar 2021

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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jinming Ge on behalf of the Authors (30 Dec 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Jan 2021) by Brian Kahn
ED: Publish as is (25 Jan 2021) by Brian Kahn

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Jinming Ge on behalf of the Authors (26 Feb 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (26 Feb 2021) by Brian Kahn
Short summary
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.