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Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation

Kaps, Arndt and Lauer, Axel and Camps-Valls, Gustau and Gentine, Pierre and Gomez-Chova, Luis and Eyring, Veronika (2023) Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation. IEEE Transactions on Geoscience and Remote Sensing, 61, pp. 1-15. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2023.3237008. ISSN 0196-2892.

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Official URL: https://dx.doi.org/10.1109/TGRS.2023.3237008


Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change projections. This study introduces a new machine-learning-based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument labeled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud-type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud-type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labeled satellite data for a more systematic evaluation of clouds in climate models

Item URL in elib:https://elib.dlr.de/193838/
Document Type:Article
Title:Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kaps, ArndtDLR, IPAhttps://orcid.org/0000-0002-5368-5950UNSPECIFIED
Lauer, AxelDLR, IPAhttps://orcid.org/0000-0002-9270-1044UNSPECIFIED
Camps-Valls, GustauUniversity of Valencia, Valencia, Spainhttps://orcid.org/0000-0003-1683-2138UNSPECIFIED
Gentine, PierreColumbia University, New York, NY, USAhttps://orcid.org/0000-0002-0845-8345UNSPECIFIED
Gomez-Chova, LuisUniversity of Valencia, Valencia, Spainhttps://orcid.org/0000-0003-3924-1269UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:23 January 2023
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 1-15
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Climate change , Clouds , Machine learning , Modeling , Process control , Satellite communication , MODIS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Kaps, Arndt
Deposited On:26 Apr 2023 14:40
Last Modified:26 Apr 2023 14:40

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