Near‐cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects

Yang, C. K., Chiu, J. C., Marshak, A., Feingold, G., Várnai, T., Wen, G., Yamaguchi, T. and Van Leeuwen, P. J. (2022) Near‐cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects. Geophysical Research Letters, 49 (20). e2022GL098274. ISSN 0094-8276 doi: 10.1029/2022GL098274

Abstract/Summary

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100–500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately −2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

Item Type	Article
URI	https://reading-pure-test.eprints-hosting.org/id/eprint/109109
Identification Number/DOI	10.1029/2022GL098274
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
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