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.
Highlights
Climate change is stated as one of the biggest issues of our time, resulting in many unwanted effects
We have evaluated the potential of using the ConvLSTM model and a regression equation to predict Cloud fractional cover (CFC) from other environmental variables
The selected environmental variables were chosen since they (i) are modelled well in numerical climate models (NCMs) and (ii) and could potentially contain information to predict
Summary
Climate change is stated as one of the biggest issues of our time, resulting in many unwanted effects. As an alternative to numerical methods, the following two-step procedure, called statistical downscaling, can be used to potentially improve the projection of future CFC [8]. DL methods learn the statistical relation between input and output and the features to use to optimize for prediction performance. In this paper we will focus on the first step of the statistical downscaling procedure described above, i.e., to learn statistical relations to predict CFC from the other environmental variables. Given the complexities of cloud formation and the large amount of data available to learn these relations through the ECC dataset (latitude × longitude × time × number of variables = 81 × 161× 129,312 × 5), we will investigate the potential of using ML to predict.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.