{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T21:06:32Z","timestamp":1777151192557,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bdcc5040062","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T17:59:38Z","timestamp":1635962378000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of Cloud Fractional Cover Using Machine Learning"],"prefix":"10.3390","volume":"5","author":[{"given":"Hanna","family":"Svennevik","sequence":"first","affiliation":[{"name":"Department of Geosciences, University of Oslo, 0316 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael A.","family":"Riegler","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Troms\u00f8, 9037 Troms\u00f8, Norway"},{"name":"SimulaMet, 0167 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Hicks","sequence":"additional","affiliation":[{"name":"SimulaMet, 0167 Oslo, Norway"},{"name":"Department of Computer Science, Oslo Metropolitan University, 0130 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-2430","authenticated-orcid":false,"given":"Trude","family":"Storelvmo","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Oslo, 0316 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-7148","authenticated-orcid":false,"given":"Hugo L.","family":"Hammer","sequence":"additional","affiliation":[{"name":"SimulaMet, 0167 Oslo, Norway"},{"name":"Department of Computer Science, Oslo Metropolitan University, 0130 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"ref_1","unstructured":"Team, C.W. (2021, November 01). IPCC: Climate Change 2021: The Physical Science Basis. Available online: https:\/\/www.ipcc.ch\/report\/ar6\/wg1\/downloads\/report\/IPCC_AR6_WGI_SPM.pdf."},{"key":"ref_2","unstructured":"UN (2021, November 01). Secretary-General Calls Latest IPCC Climate Report \u2018Code Red for Humanity\u2019, Stressing \u2018Irrefutable\u2019 Evidence of Human Influence. Available online: https:\/\/www.un.org\/press\/en\/2021\/sgsm20847.doc.htm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/nature24144","article-title":"A global plan for nature conservation","volume":"550","author":"Watson","year":"2017","journal-title":"Nature"},{"key":"ref_4","unstructured":"IPCC (2014). IPCC: Climate Change 2014: Synthesis Report, IPCC."},{"key":"ref_5","unstructured":"Zeng, X. (2021, November 01). Is Climate Change to Blame for Extreme Weather Events? Attribution Science Says Yes, for Some\u2014Here\u2019s How It Works. The Conversation. Available online: https:\/\/theconversation.com\/is-climate-change-to-blame-for-extreme-weather-events-attribution-science-says-yes-for-some-heres-how-it-works-164941."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.5194\/gmd-9-3461-2016","article-title":"The scenario model intercomparison project (ScenarioMIP) for CMIP6","volume":"9","author":"Tebaldi","year":"2016","journal-title":"Geosci. Model Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2019GL085782","DOI":"10.1029\/2019GL085782","article-title":"Causes of higher climate sensitivity in CMIP6 models","volume":"47","author":"Zelinka","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Benestad, R.E., Chen, D., and Hanssen-Bauer, I. (2008). Empirical-Statistical Downscaling, World Scientific Publishing Company.","DOI":"10.1142\/6908"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11704-020-0800-8","article-title":"Machine learning after the deep learning revolution","volume":"14","author":"Buntine","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kelleher, J.D. (2019). Deep Learning, MIT Press.","DOI":"10.7551\/mitpress\/11171.001.0001"},{"key":"ref_12","unstructured":"Svennevik, H., Riegler, M.A., Hicks, S., Storelvmo, T., and Hammer, H.L. (2021). ECC: A Dataset for Predicting Cloud Cover over Europe in the Future. Nat. Sci. Data, under review."},{"key":"ref_13","unstructured":"(2021, September 02). ECC: A Dataset for Predicting Cloud Cover over Europe. Available online: https:\/\/datasets.simula.no\/ecc-dataset\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2020MS002076","DOI":"10.1029\/2020MS002076","article-title":"A moist physics parameterization based on deep learning","volume":"12","author":"Han","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_15","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","article-title":"Rainfall\u2013runoff modelling using long short-term memory (LSTM) networks","volume":"22","author":"Kratzert","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, H., Lee, J., Yoon, S., Kahou, S.E., Kashinath, K., and Prabhat, M. (2019, January 7\u201311). Deep-hurricane-tracker: Tracking and forecasting extreme climate events. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00192"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1016\/j.apr.2020.05.015","article-title":"An LSTM-based aggregated model for air pollution forecasting","volume":"11","author":"Chang","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_20","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2016). Understanding the effective receptive field in deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 4905\u20134913."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gers, F.A., Schmidhuber, J., and Cummins, F. (1999, January 7\u201310). Learning to forget: Continual prediction with LSTM. Proceedings of the 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (Conf. Publ. No. 470), Edinburgh, UK.","DOI":"10.1049\/cp:19991218"},{"key":"ref_22","unstructured":"Liu, J.N., Hu, Y., You, J.J., and Chan, P.W. (2014, January 21\u201324). Deep neural network based feature representation for weather forecasting. Proceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014\u2014WORLDCOMP 2014, Las Vegas, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor\u00e1nyi, A., Mu\u00f1oz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.","DOI":"10.1002\/qj.3803"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.5194\/acp-17-1417-2017","article-title":"Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems","volume":"17","author":"Fujiwara","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1175\/BAMS-83-7-Schmetz-2","article-title":"An introduction to Meteosat second generation (MSG)","volume":"83","author":"Schmetz","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lohmann, U., L\u00fc\u00f6nd, F., and Mahrt, F. (2016). Clouds. An Introduction to Clouds: From the Microscale to Climate, Cambridge University Press.","DOI":"10.1017\/CBO9781139087513"},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning, PMLR."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1002\/joc.3544","article-title":"The statistical downscaling model: Insights from one decade of application","volume":"33","author":"Wilby","year":"2013","journal-title":"Int. J. Climatol."},{"key":"ref_29","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2021, November 01). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Bottou, L., Orr, G.B., and M\u00fcller, K.R. (2012). Efficient BackProp. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/4\/62\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:22Z","timestamp":1760167522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/4\/62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["bdcc5040062"],"URL":"https:\/\/doi.org\/10.3390\/bdcc5040062","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,3]]}}}