Preprints
https://doi.org/10.5194/amt-2021-168
https://doi.org/10.5194/amt-2021-168

  23 Jun 2021

23 Jun 2021

Review status: a revised version of this preprint is currently under review for the journal AMT.

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Nicholas J. Kedzuf1, J. Christine Chiu1, V. Chandrasekar2, Sounak Biswas2, Shashank S. Joshil2, Yinghui Lu3, Peter Jan van Leeuwen1,4, Christopher Westbrook4, Yann Blanchard5, and Sebastian O'Shea6 Nicholas J. Kedzuf et al.
  • 1Department of Atmospheric Science, Colorado State University, Fort Collins, CO, 80523, USA
  • 2Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA
  • 3Department of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, PA 16802, USA
  • 4Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
  • 5ESCER Centre, Department of Earth and Atmospheric Sciences, University of Quebec at Montreal, Montreal, Quebec, Canada
  • 6Department of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK

Abstract. Ice and mixed phase clouds play a key role in our climate system, because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates, because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 %, and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in-situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in total ice number concentration and 44 % in total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

Nicholas J. Kedzuf et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-168', Anonymous Referee #1, 20 Jul 2021
    • AC1: 'Reply on RC1', Jui-Yuan Christine Chiu, 18 Sep 2021
  • RC2: 'Comment on amt-2021-168', Anonymous Referee #2, 10 Aug 2021
    • AC2: 'Reply on RC2', Jui-Yuan Christine Chiu, 18 Sep 2021

Nicholas J. Kedzuf et al.

Nicholas J. Kedzuf et al.

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Short summary
Ice clouds play a key role in our climate system, due to their strong controls on precipitation and radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open many research opportunities.