Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5607-2021
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, Arjo Segers, Hai Xiang Lin, Bas Henzing, Xiaohui Wang, Arnold Heemink, and Hong Liao

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Cited articles

Alfaro, S. C., Gaudichet, A., Gomes, L., and Maillé, M.: Modeling the size distribution of a soil aerosol produced by sandblasting, J. Geophys. Res., 102, 11239–11249, https://doi.org/10.1029/97jd00403, 1997. a
Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007. a
Basart, S., Pérez, C., Nickovic, S., Cuevas, E., and Baldasano, J.: Development and evaluation of the BSC-DREAM8b dust regional model over Northern Africa, the Mediterranean and the Middle East, Tellus B, 64, 18539, https://doi.org/0.3402/tellusb.v64i0.18539, 2012. a
Basart, S., Nickovic, S., Terradellas, E., Cuevas, E., García-Pando, C. P., García-Castrillo, G., Werner, E., and Benincasa, F.: The WMO SDS-WAS Regional Center for Northern Africa, Middle East and Europe, in: E3S Web of Conferences, vol. 99, EDP Sciences, Les Ulis, France, 2019. a
Beezley, J. D. and Mandel, J.: Morphing ensemble Kalman filters, Tellus A, 60, 131–140, https://doi.org/10.1111/j.1600-0870.2007.00275.x, 2008. a, b
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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.