Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 18
Articles | Volume 17, issue 18
https://doi.org/10.5194/amt-17-5655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-5655-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 18
Research article
 | 
26 Sep 2024
Research article |  | 26 Sep 2024

Marine cloud base height retrieval from MODIS cloud properties using machine learning

Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic

Data sets

Data for the article: "Marine cloud base height retrieval from MODIS cloud properties using machine learning" Julien Lenhardt et al. https://doi.org/10.5281/zenodo.10517686

MIDAS: Global Marine Meteorological Ob- servations Data Met Office https://catalogue.ceda.ac.uk/uuid/77910bcec71c820d4c92f40d3ed3f249

Cumulo: A Dataset for Learning Cloud Classes Valentina Zantedeschi et al. https://www.dropbox.com/sh/6gca7f0mb3b0ikz/AAAeTWF21WGZ7-y9MpSiL9P3a/CUMULO

Model code and software

Method code for the article: "Marine cloud base height retrieval from MODIS cloud properties using machine learning" Julien Lenhardt et al. https://doi.org/10.5281/zenodo.10517686

Interactive computing environment

Method code and data for the article: "Marine cloud base height retrieval from MODIS cloud properties using machine learning" Julien Lenhardt et al. https://doi.org/10.5281/zenodo.10517686

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Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
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