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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-20
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-20
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Jan 2019

Research article | 30 Jan 2019

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

Can liquid cloud microphysical processes be used for vertically-pointing cloud radar calibration?

Maximilian Maahn1,2, Fabian Hoffmann1,3, Matthew D. Shupe1,2, Gijs de Boer1,2, Sergey Y. Matrosov1,2, and Edward P. Luke4 Maximilian Maahn et al.
  • 1Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
  • 2NOAA Earth System Research Laboratory (ESRL), Physical Sciences Division, Boulder, Colorado, USA
  • 3NOAA Earth System Research Laboratory (ESRL), Chemical Sciences Division, Boulder, Colorado, USA
  • 4Brookhaven National Laboratory, Upton, New York, USA

Abstract. Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single, robust method exists for assessing calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes of liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). We identify reference points of these relationships and evaluate their potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for these reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that the methods are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that in 2016, the radar reflectivity at NSA was about 1 ± 1 dB too low, but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

Maximilian Maahn et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Maximilian Maahn et al.
Data sets

Cloud mask from Micropulse Lidar (30SMPLCMASK1ZWANG). Oliktok Point (OLI) and North Slope of Alaska (NSA). ARM Data Center ARM user facility (compiled by C. Sivaraman, K. Johnson, L. Riihimaki and S. Giangrande) https://doi.org/10.5439/1027736

Microwave Radiometer (MWRLOS). North Slope of Alaska (NSA). ARM Data Center ARM user facility (compiled by C. Sivaraman, K. Gaustad, L. Riihimaki, M. Cadeddu, T. Shippert and V. Ghate) https://doi.org/10.5439/1046211

Ceilometer (CEIL). Oliktok Point (OLI) and North Slope of Alaska (NSA). ARM Data Center ARM user facility (compiled by B. Ermold and V. Morris) https://doi.org/10.5439/1181954

Interpolated Sonde (INTERPOLATEDSONDE). Oliktok Point (OLI) and North Slope of Alaska (NSA). ARM Data Center ARM user facility https://doi.org/10.5439/1095316

Microwave Radiometer – High Frequency (MWRHF). North Slope of Alaska (NSA). ARM Data Center ARM user facility (compiled by M. Cadeddu and V. Ghate) https://doi.org/10.5439/1025250

Microwave Radiometer – High Frequency (MWR3C). Oliktok Point (OLI). ARM Data Center ARM user facility (compiled by M. Cadeddu and V. Ghate) https://doi.org/10.5439/1025248

Campaign Datasets for ARM Airborne Carbon Measurements (ARM-ACME-V). ARM Data Center ARM user facility (compiled by S. Biraud, F. Mei, C. Flynn, J. Hubbe, C. Long, A. Matthews, M. Pekour, A. Sedlacek, S. Springston, J. Tomlinson, and D. Chand) https://doi.org/10.5439/1346549

Model code and software

Pamtra2 (Passive and Active Microwave TRANsfer 2) M. Maahn and D. Ori https://doi.org/10.5281/zenodo.2552448

Maximilian Maahn et al.
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Short summary
Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Here, we present, apply and evaluate three novel methods for calibrating vertically pointing cloud radars. These calibration methods are based on microphysical processes of liquid clouds such as the transition of cloud droplets to drizzle drops.
Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar...
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