Articles | Volume 15, issue 4
Atmos. Meas. Tech., 15, 927–944, 2022
https://doi.org/10.5194/amt-15-927-2022
Atmos. Meas. Tech., 15, 927–944, 2022
https://doi.org/10.5194/amt-15-927-2022

Research article 23 Feb 2022

Research article | 23 Feb 2022

Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms

Yuli Liu and Gerald G. Mace

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on amt-2021-2', Jana Mendrok, 30 Jul 2021
    • AC3: 'Reply on CC1', Yuli Liu, 22 Nov 2021
  • RC1: 'Comment on amt-2021-2', Anonymous Referee #2, 04 Aug 2021
  • RC2: 'Comment on amt-2021-2', Anonymous Referee #1, 05 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yuli Liu on behalf of the Authors (23 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (23 Nov 2021) by Patrick Eriksson
RR by Anonymous Referee #1 (10 Dec 2021)
ED: Publish subject to technical corrections (05 Jan 2022) by Patrick Eriksson
AR by Yuli Liu on behalf of the Authors (25 Jan 2022)  Author's response    Manuscript
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Short summary
We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.