A survey of radiative and physical properties of North Atlantic mesoscale cloud morphologies from multiple identification methodologies

Eastman, Ryan; McCoy, Isabel L.; Schulz, Hauke; Wood, Robert

doi:https://doi.org/10.5194/acp-24-6613-2024

Articles | Volume 24, issue 11

https://doi.org/10.5194/acp-24-6613-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-24-6613-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 11

Research article

|

06 Jun 2024

Research article |

| 06 Jun 2024

A survey of radiative and physical properties of North Atlantic mesoscale cloud morphologies from multiple identification methodologies

Ryan Eastman, Isabel L. McCoy, Hauke Schulz, and Robert Wood

Data sets

MODIS Atmosphere L2 Cloud Product (06_L2) Steve Platnick et al. https://doi.org/10.5067/MODIS/MYD06_L2.006

CERES Regionally Averaged TOA Fluxes, Clouds and Aerosols Hourly Aqua Edition4A NASA/LARC/SD/ASDC https://doi.org/10.5067/AQUA/CERES/SSF1DEGHOUR_L3.004

The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua (https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MYD08_D3) Steven Platnick et al. https://doi.org/10.1109/TGRS.2016.2610522

Short summary

Cloud types are determined using machine learning image classifiers applied to satellite imagery for 1 year in the North Atlantic. This survey of these cloud types shows that the climate impact of a cloud scene is, in part, a function of cloud type. Each type displays a different mix of thick and thin cloud cover, with the fraction of thin cloud cover having the strongest impact on the clouds' radiative effect. Future studies must account for differing properties and processes among cloud types.