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Machine learning for total cloud cover prediction

Baran, Ágnes; Lerch, Sebastian; El Ayari, Mehrez; Baran, Sándor

Abstract:
Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC; however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002–2014, we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000122319
Veröffentlicht am 03.08.2020
Originalveröffentlichung
DOI: 10.1007/s00521-020-05139-4
Scopus
Zitationen: 2
Web of Science
Zitationen: 3
Dimensions
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Stochastik (STOCH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0941-0643, 1433-3058
KITopen-ID: 1000122319
Erschienen in Neural computing & applications
Verlag Springer
Band 33
Seiten 2605–2620
Vorab online veröffentlicht am 06.07.2020
Nachgewiesen in Web of Science
Dimensions
Scopus
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