Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.668
IF 5-year value: 3.707
IF 5-year
CiteScore value: 6.3
SNIP value: 1.383
IPP value: 3.75
SJR value: 1.525
Scimago H <br class='widget-line-break'>index value: 77
Scimago H
h5-index value: 49
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Sep 2020

24 Sep 2020

Review status
This preprint is currently under review for the journal AMT.

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

Xiaoyu Hu1, Jinming Ge1, Jiajing Du1, Qinghao Li1, Jianping Huang1, and Qiang Fu2 Xiaoyu Hu et al.
  • 1Key Laboratory for Semi-Arid Climate Change of the Ministry of Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
  • 2Department of Atmospheric Sciences, University of Washington, Seattle, WA, 98105, USA

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.

Xiaoyu Hu et al.

Interactive discussion

Status: open (until 19 Nov 2020)
Status: open (until 19 Nov 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Xiaoyu Hu et al.

Xiaoyu Hu et al.


Total article views: 220 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
163 55 2 220 5 2
  • HTML: 163
  • PDF: 55
  • XML: 2
  • Total: 220
  • BibTeX: 5
  • EndNote: 2
Views and downloads (calculated since 24 Sep 2020)
Cumulative views and downloads (calculated since 24 Sep 2020)

Viewed (geographical distribution)

Total article views: 212 (including HTML, PDF, and XML) Thereof 209 with geography defined and 3 with unknown origin.
Country # Views %
  • 1



No saved metrics found.


No discussed metrics found.
Latest update: 28 Oct 2020
Publications Copernicus
Short summary
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutters. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutters by considering their different features in several variables. We applied this method to the radar observations at SACOL site and found the results have a good agreement with lidar detections.
Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar...