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

Journal metrics

Journal metrics

  • IF value: 3.248 IF 3.248
  • IF 5-year value: 3.650 IF 5-year
  • CiteScore value: 3.37 CiteScore
  • SNIP value: 1.253 SNIP 1.253
  • SJR value: 1.869 SJR 1.869
  • IPP value: 3.29 IPP 3.29
  • h5-index value: 47 h5-index 47
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 60 Scimago H
    index 60
Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Mar 2019

Research article | 04 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Calibration of the 2007–2017 record of ARM Cloud Radar Observations using CloudSat

Pavlos Kollias1,2, Bernat Puigdomènech Treserras3, and Alain Protat4 Pavlos Kollias et al.
  • 1School of Marine and Atmospheric Sciences, Stony Brook University, USA
  • 2Center for Multiscale Applied Sensing, Brookhaven National Laboratory, USA
  • 3Department of Atmospheric and Oceanic Sciences, McGill University, Canada
  • 4Bureau of Meteorology, Melbourne Australia

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program has been at the forefront of millimeter wavelength radar development and operations since the late 90’s. The operational performance of the ARM cloud radar network is very high; however, the calibration of the historical record is not well established. Here, we use a well-characterized spaceborne 94-GHz cloud profiling radar (CloudSat) to characterize the calibration of the different generations of the ARM cloud radars from 2007 to 2017 over a variety of climatological regimes and for fixed and mobile deployments. Over 43 years of ARM profiling cloud radar observations are compared to CloudSat and the calibration offsets are reported as a function of time using a sliding window of 6 months. The study also provides the calibration offsets for each operating mode of the ARM cloud radars. Overall, significant calibration offsets are found that exceed the uncertainty of the technique (1–2 dB). The findings of this study are critical to past, on-going and planned studies of cloud and precipitation and should assist the DOE ARM to build a legacy decadal ground-based cloud radar dataset for global climate model validation.

Pavlos Kollias et al.
Interactive discussion
Status: open (until 22 May 2019)
Status: open (until 22 May 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Pavlos Kollias et al.
Pavlos Kollias et al.
Total article views: 303 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
206 95 2 303 4 3
  • HTML: 206
  • PDF: 95
  • XML: 2
  • Total: 303
  • BibTeX: 4
  • EndNote: 3
Views and downloads (calculated since 04 Mar 2019)
Cumulative views and downloads (calculated since 04 Mar 2019)
Viewed (geographical distribution)  
Total article views: 231 (including HTML, PDF, and XML) Thereof 230 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 22 May 2019
Publications Copernicus
Short summary
Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based observatories. Calibrating these radars is important for establishing a long record of observations suitable for model evaluation and improvement. Here, the CloudSat CPR is used to assess the calibration a 10+ year long record of ARM cloud radar observations (a total of 44 years). The results indicate that correction coefficients are needed to improve record reliability and usability.
Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based...