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dc.contributor.authorSvennevik, Hanna
dc.contributor.authorRiegler, Michael A.
dc.contributor.authorHicks, Steven
dc.contributor.authorStorelvmo, Trude
dc.contributor.authorHammer, Hugo L.
dc.date.accessioned2021-12-08T13:08:55Z
dc.date.available2021-12-08T13:08:55Z
dc.date.created2021-11-26T19:04:08Z
dc.date.issued2021-11-03
dc.identifier.citationBig Data and Cognitive Computing. 2021, 5 (4), .en_US
dc.identifier.issn2504-2289
dc.identifier.urihttps://hdl.handle.net/11250/2833363
dc.description.abstractClimate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually projected using numerical climate methods. In this paper, we explore the potential of using machine learning as part of a statistical downscaling framework to project future CFC. We are not aware of any other research that has explored this. We evaluated the potential of two different methods, a convolutional long short-term memory model (ConvLSTM) and a multiple regression equation, to predict CFC from other environmental variables. The predictions were associated with much uncertainty indicating that there might not be much information in the environmental variables used in the study to predict CFC. Overall the regression equation performed the best, but the ConvLSTM was the better performing model along some coastal and mountain areas. All aspects of the research analyses are explained including data preparation, model development, ML training, performance evaluation and visualization.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesBig Data and Cognitive Computing;Volume 5 / Issue 4
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectClimate scienceen_US
dc.subjectCloud fractional coversen_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.subjectStatistical downscalingen_US
dc.titlePrediction of cloud fractional cover using machine learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2021 by the authors.en_US
dc.source.articlenumber62en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.3390/bdcc5040062
dc.identifier.cristin1960039
dc.source.journalBig Data and Cognitive Computingen_US
dc.source.volume5en_US
dc.source.issue4en_US
dc.source.pagenumber13en_US


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