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001-es BibID:	BIBFORM087360
035-os BibID:	(WoS)000545899300003 (Scopus)85087622962
Első szerző:	Baran Ágnes (matematikus)
Cím:	Machine learning for total cloud cover prediction / Ágnes Baran, Sebastian Lerch, Mehrez El Ayari, Sándor Baran
Dátum:	2021
ISSN:	0941-0643 1433-3058
Megjegyzések:	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. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill, and except for very short lead times the extended MLP model shows the best overall performance.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Neural Computing & Applications. - 33 (2021), p. 2605-2620. -
További szerzők:	Lerch, Sebastian (1986-) (matematikus) El Ayari, Mehrez (1989-) (informatikus) Baran Sándor (1973-) (matematikus, informatikus)
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001-es BibID:	BIBFORM079342
035-os BibID:	(Scopus)85065663172 (WOS)000474848500021
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Statistical Postprocessing of Water Level Forecasts Using Bayesian Model Averaging With Doubly Truncated Normal Components / Sándor Baran, Stephan Hemri, Mehrez El Ayari
Dátum:	2019
ISSN:	0043-1397
Megjegyzések:	Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state?of?the?art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical postprocessing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical postprocessing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible postprocessing of possibly multimodel ensemble forecasts of water level, is introduced. A case study based on water levels for a gauge of river Rhine reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach. Using rolling training periods, BMA considerably outerperforms ensemble model output statistics. However, this gap shrinks drastically when using analog?based training periods.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Water Resources Research. - 55 : 5 (2019), p. 3997-4013. -
További szerzők:	Hemri, Stephan El Ayari, Mehrez (1989-) (informatikus)
Pályázati támogatás:	NN125679 Egyéb DFG MO 3394/1-1 Egyéb EFOP-3.6.3-VEKOP-16-2017-00002 EFOP
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001-es BibID:	BIBFORM093743
035-os BibID:	(WoS)000647729800004 (Scopus)85105329815
Első szerző:	Schulz, Benedikt (alkalmazott matematikus)
Cím:	Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting / Benedikt Schulz, Mehrez El Ayari, Sebastian Lerch, Sándor Baran
Dátum:	2021
ISSN:	0038-092X
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Solar Energy. - 220 (2021), p. 1016-1031. -
További szerzők:	El Ayari, Mehrez (1989-) (informatikus) Lerch, Sebastian (1986-) (matematikus) Baran Sándor (1973-) (matematikus, informatikus)
Pályázati támogatás:	EFOP-3.6.2-16-2017-00015 EFOP NKFIH NN125679 Egyéb
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