Preprints
https://doi.org/10.5194/acp-2022-630
https://doi.org/10.5194/acp-2022-630
 
26 Oct 2022
26 Oct 2022
Status: this preprint is currently under review for the journal ACP.

How aerosol size matters in AOD assimilation and the optimization using Ångström exponent

Jianbing Jin1,a, Bas Henzing1, and Arjo Segers1 Jianbing Jin et al.
  • 1TNO, Department of Climate, Air and Sustainability, The Netherlands
  • anow at: Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

Abstract. Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount.

In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially-conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur either due to the standard AOD assimilation analysis or hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.

Jianbing Jin et al.

Status: open (until 07 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-630', Anonymous Referee #1, 28 Nov 2022 reply

Jianbing Jin et al.

Jianbing Jin et al.

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Short summary
Aerosol models and satellite retrieval algorithms rely on different aerosol size assumptions. In practise, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. To avoid inconsistencies, we designed a hybrid assimilation approach. Different from a standard AOD assimilation that directly assimilates AODs, the hybrid one first estimates aerosol size parameters by assimilating Ångström observations, before assimilating the AODs.
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