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Dataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"


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.


The dataset contains two parts: 1) Large eddy simulation (LES) output 2) a module that performs all-clear-sky aerosol retrieval using a machine-learning-based model. There are also example data and a script to demonstrate how to use the module.
Department of Atmospheric Science

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Aerosol direct radiative effect
Aerosol remote sensing
Machine Learning


Associated Publications

Yang, C. K., Chiu, J. C., Marshak, A., Feingold, G., Várnai, T., Wen, G., et al. (2022). Near-cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects. Geophysical Research Letters, 49, e2022GL098274.