Articles | Volume 23, issue 2
https://doi.org/10.5194/acp-23-1641-2023
https://doi.org/10.5194/acp-23-1641-2023
Research article
 | 
27 Jan 2023
Research article |  | 27 Jan 2023

How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent

Jianbing Jin, Bas Henzing, and Arjo Segers

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Cited articles

Ackermann, I. J., Hass, H., Memmesheimer, M., Ebel, A., Binkowski, F. S., and Shankar, U.: Modal aerosol dynamics model for Europe: Development and first applications, Atmos. Environ., 32, 2981–2999, 1998. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989. a
Andreae, M. O. and Crutzen, P. J.: Atmospheric aerosols: Biogeochemical sources and role in atmospheric chemistry, Science, 276, 1052–1058, 1997. a
Ångström, A.: On the Atmospheric Transmission of Sun Radiation and on Dust in the Air, Geografiska Annaler, 11, 156–166, 1929. a, b
Baker, A., Kelly, S., Biswas, K., Witt, M., and Jickells, T.: Atmospheric deposition of nutrients to the Atlantic Ocean, Geophys. Res. Lett., 30, 2296, https://doi.org/10.1029/2003GL018518, 2003. a
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Short summary
Aerosol models and satellite retrieval algorithms rely on different aerosol size assumptions. In practice, differences between simulations and observations do not always reflect the difference in aerosol amount. To avoid inconsistencies, we designed a hybrid assimilation approach. Different from a standard aerosol optical depth (AOD) assimilation that directly assimilates AODs, the hybrid one estimates aerosol size parameters by assimilating Ängström observations before assimilating the AODs.
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