Preprints
https://doi.org/10.5194/gmd-2021-10
https://doi.org/10.5194/gmd-2021-10

Submitted as: development and technical paper 09 Mar 2021

Submitted as: development and technical paper | 09 Mar 2021

Review status: this preprint is currently under review for the journal GMD.

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

Jianbing Jin1,2, Arjo Segers3, Hai Xiang Lin2, Bas Henzing3, Xiaohui Wang2, Arnold Heemink2, and Hong Liao1 Jianbing Jin et al.
  • 1Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China
  • 2Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
  • 3TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands

Abstract. When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing, or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely, and leave the position errors mainly unchanged. In this paper, a grid distorted data assimilation, which consists of an imaging morphing method and an ensemble-based variational assimilation, is designed for re-aligning a simulated dust plume to correct the position error. This new developed grid distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation that focuses solely on intensity correction, a grid distorted data assimilation that focuses on position correction only, and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion improves only slightly the dust simulation, while the grid distorted data assimilation effectively improves the dust simulation and forecast. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecast of dust concentrations.

Jianbing Jin et al.

Status: open (until 04 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jianbing Jin et al.

Jianbing Jin et al.

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