Dataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"
Files
README.txt (3.95 KB) ID1.nc (775.24 MB) The LES simulation output named "ID1" from RICO field campaign; the output time is 50-hr ID2.nc (787.55 MB) The LES simulation output named "ID2" from RICO field campaign; the output time is 54-hr ID3.nc (900.06 MB) The LES simulation output named "ID3" from 7SEAS field campaign simulated without vertical wind shear ID4.nc (900.06 MB) The LES simulation output named "ID4" from 7SEAS field campaign simulated with vertical wind shear
Date
2022
Authors
Yang, C. Kevin
Chiu, Christine
Marshak, Alexander
Feingold, Graham
Várnai, Tamás
Wen, Guoyong
Yamaguchi, Takanobu
van Leeuwen, Peter Jan
Journal Title
Journal ISSN
Volume Title
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.
Description
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
Department of Atmospheric Science
Rights Access
Subject
Aerosol direct radiative effect
Aerosol remote sensing
Machine Learning
Citation
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. https://doi.org/10.1029/2022GL098274