Prediction of Cloud Fractional Cover Using Machine Learning

Hanna Svennevik, Michael Alexander Riegler, Steven Alexander Hicks, Trude Storelvmo, Hugo L. Hammer

Big Data and Cognitive Computing · 03 November 2021

In one sentence. Compares ConvLSTM and multiple regression for statistical downscaling of future cloud fractional cover; regression performs best overall, while ConvLSTM wins in coastal and mountain areas.

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.

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