Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5607–5622, 2021
https://doi.org/10.5194/gmd-14-5607-2021
Geosci. Model Dev., 14, 5607–5622, 2021
https://doi.org/10.5194/gmd-14-5607-2021

Development and technical paper 10 Sep 2021

Development and technical paper | 10 Sep 2021

Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0

Jianbing Jin et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-10', Anonymous Referee #1, 07 May 2021
  • RC2: 'Comment on gmd-2021-10', Anonymous Referee #2, 27 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jianbing Jin on behalf of the Authors (06 Jul 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Jul 2021) by Christoph Knote
RR by Anonymous Referee #1 (09 Aug 2021)
ED: Publish as is (13 Aug 2021) by Christoph Knote
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Short summary
When discussing the accuracy of a dust forecast, the shape and position of the plume as well as the intensity are key elements. The position forecast determines which locations will be affected, while the intensity only describes the actual dust level. A dust forecast with position misfit directly results in incorrect timing profiles of dust loads. In this paper, an image-morphing-based data assimilation is designed for realigning a simulated dust plume to correct for the position error.