Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Jin, J. (author), Segers, Arjo (author), Heemink, A.W. (author), Yoshida, Mayumi (author), Han, Wei (author), Lin, H.X. (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: data assimilation; dust mask; dust storm; Himawari-8 AOD; observation selection; OA-Fund TU Delft; Aerosol Robotic Network
• Online Access: http://resolver.tudelft.nl/uuid:ca96f22e-ffd8-4bce-b492-a1b4c6bb1f97; https://doi.org/10.1029/2018MS001491

Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a comparison with AErosol RObotic NETwork AODs. This study focuses on extreme dust events only when dust aerosols are dominant; promising results are obtained in AOD assimilation experiments during a case in May 2017. The dust emission fields that drive the simulation model are strongly improved by the inverse modeling, and consequently, the simulated dust concentrations are in better agreements with the observed AOD as well as ground-based observations of PM 10 . However, some satellite AODs show significant inconsistence with the simulations and the PM 10 and AErosol RObotic NETwork observations, which might arise from retrieval errors over a partially clouded scene. The data assimilation procedure therefore includes a screening method to exclude these observations in order to avoid unrealistic results. A dust mask screening method is designed, which is based on selecting only those observations where the deterministic model produces a substantial amount of dust. This screen algorithm is tested to give more accurate result compared to the traditional method based on background covariance in the case study. Note that our screen method would exclude valuable information in case the model is not able to simulate the dust plume shape correctly; hence, applications in related studies require inspections of simulations and observations by user. Mathematical Physics