Preprints
https://doi.org/10.5194/egusphere-2023-2118
https://doi.org/10.5194/egusphere-2023-2118
26 Sep 2023
 | 26 Sep 2023

A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies

Ryan Eastman, Isabel Louise McCoy, Hauke Schulz, and Robert Wood

Abstract. Three supervised neural network cloud classification routines are applied to daytime MODIS Aqua imagery and compared for the year 2018 over the North Atlantic Ocean: The Morphology Identification Data Aggregated over the Satellite-era (MIDAS), which specializes in subtropical stratocumulus (Sc) clouds; Sugar, Gravel, Flowers, and Fish (SGFF), which is focused on shallow cloud systems in the tropical trade winds; and the community record of marine low cloud mesoscale morphology supported by the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) dataset, which is focused on shallow clouds globally.

Comparisons of co-occurrence and vertical and geographic distribution show that morphologies are classified in geographically distinct regions: shallow suppressed and deeper aggregated and disorganized cumulus are seen in the tropical trade winds. Shallow Sc types are frequent in subtropical subsidence regions. More vertically developed solid stratus and open and closed cell Sc are frequent in the mid-latitude storm track. Differing classifier routines favor noticeably different distributions of equivalent types.

Average scene albedo is more strongly correlated with cloud albedo than cloud amount for each morphology. Cloud albedo is strongly correlated with the fraction of optically thin cloud cover. The albedo of each morphology is dependent on latitude and location in the mean anticyclonic wind flow over the N. Atlantic. Strong rain rates are associated with middling values of albedo for many cumuliform types, hinting at a complex relationship between the presence of heavily precipitating cores and cloud albedo. The presence of ice at cloud top is associated with higher albedos. For a constant albedo, each morphology displays a distinct set of physical characteristics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ryan Eastman, Isabel Louise McCoy, Hauke Schulz, and Robert Wood

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2118', Anonymous Referee #1, 21 Oct 2023
  • RC2: 'Comment on egusphere-2023-2118', Anonymous Referee #2, 13 Dec 2023
  • AC1: 'Comment on egusphere-2023-2118', Ryan Eastman, 17 Jan 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2118', Anonymous Referee #1, 21 Oct 2023
  • RC2: 'Comment on egusphere-2023-2118', Anonymous Referee #2, 13 Dec 2023
  • AC1: 'Comment on egusphere-2023-2118', Ryan Eastman, 17 Jan 2024
Ryan Eastman, Isabel Louise McCoy, Hauke Schulz, and Robert Wood
Ryan Eastman, Isabel Louise McCoy, Hauke Schulz, and Robert Wood

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Short summary
Cloud types are determined using machine learning image classifiers applied to satellite imagery for one 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 types.