Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects

Abstract

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.

Key Points

• A convolutional neural network is used to retrieve aerosol optical depth (AOD) with an uncertainty of 0.01 + 5%AOD in all cloud‐free regions

• Due to aerosol hygroscopic growth, the optical depth of aerosols near clouds can be enhanced by 100% compared to those far from clouds

• The enhancement in AOD near clouds leads to an overall 55% increase in clear‐sky aerosol direct radiative effects