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.

Data sets

the ground observations for dust storm event in 2017 May in East Asia Jianbing Jin https://doi.org/10.5281/zenodo.4579953

Model code and software

Python source code of grid distorted data assimilation (ver1.0) Jianbing Jin https://doi.org/10.5281/zenodo.4579960

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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.