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Prediction of cloud fractional cover using machine learning

Permanent link
https://hdl.handle.net/10037/23553
DOI
https://doi.org/10.3390/bdcc5040062
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Date
2021-11-03
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Svennevik, Hanna; Riegler, Michael A.; Hicks, Steven; Storelvmo, Trude; Hammer, Hugo L.
Abstract
Climate 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
Publisher
MDPI
Citation
Svennevik, Riegler, Hicks, Storelvmo, Hammer. Prediction of cloud fractional cover using machine learning. Big Data and Cognitive Computing. 2021;5(4)
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