Articles | Volume 15, issue 18
Atmos. Meas. Tech., 15, 5415–5438, 2022
https://doi.org/10.5194/amt-15-5415-2022

Special issue: Profiling the atmospheric boundary layer at a European scale...

Atmos. Meas. Tech., 15, 5415–5438, 2022
https://doi.org/10.5194/amt-15-5415-2022
Research article
26 Sep 2022
Research article | 26 Sep 2022

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Alistair Bell et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-30', Anonymous Referee #1, 12 Mar 2022
  • RC2: 'Comment on amt-2022-30', Anonymous Referee #2, 12 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alistair Bell on behalf of the Authors (06 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (04 Aug 2022) by Andrew Sayer
AR by Alistair Bell on behalf of the Authors (05 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (08 Aug 2022) by Andrew Sayer
Download
Short summary
Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.