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001-es BibID:	BIBFORM115661
035-os BibID:	(WoS)001119372200001 (Scopus)85175346590
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Statistical post-processing of visibility ensemble forecasts / Sándor Baran, Mária Lakatos
Dátum:	2023
ISSN:	1350-4827
Megjegyzések:	To be able to produce accurate and reliable predictions of visibility has crucial importance in aviation meteorology, as well as in water- and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill and reliability of visibility predictions are far reduced compared with other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or nonparametric approaches, including machine learning-based techniques. As visibility observations-according to the suggestion of the World Meteorological Organization-are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show that while climatological forecasts outperform the raw ensemble by a wide margin, post-processing results in further substantial improvement in forecast skill, and in general, POLR models are superior to their MLP counterparts.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk classification ensemble calibration multilayer perceptron proportional odds logistic regression visibility
Megjelenés:	Meteorological Applications. - 30 : 5 (2023), p. 1-18. -
További szerzők:	Nagy-Lakatos Mária (1995-) (programtervező informatikus)
Pályázati támogatás:	K142849 OTKA ÚNKP-22-3-I-DE-186 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM125811
035-os BibID:	(WoS)001370206200001 (Scopus)85210022384
Első szerző:	Nagy-Lakatos Mária (programtervező informatikus)
Cím:	Enhancing multivariate post-processed visibility predictions utilizing Copernicus Atmosphere Monitoring Service forecasts / Lakatos Mária, Baran Sándor
Dátum:	2024
ISSN:	1350-4827
Megjegyzések:	In our contemporary era, meteorological weather forecasts increasingly incorporate ensemble predictions of visibility-a parameter of great importance in aviation, maritime navigation, and air quality assessment, with direct implications for public health. However, this weather variable falls short of the predictive accuracy achieved for other quantities issued by meteorological centers. Therefore, statistical post-processing is recommended to enhance the reliability and accuracy of predictions. By estimating the predictive distributions of the variables with the aid of historical observations and forecasts, one can achieve statistical consistency between true observations and ensemble predictions. Visibility observations, following the recommendation of the World Meteorological Organization, are typically reported in discrete values; hence, the predictive distribution of the weather quantity takes the form of a discrete parametric law. Recent studies demonstrated that the application of classification algorithms can successfully improve the skill of such discrete forecasts; however, a frequently emerging issue is that certain spatial and/or temporal dependencies could be lost between marginals. Based on visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts for 30 locations in Central Europe, we investigate whether the inclusion of Copernicus Atmosphere Monitoring Service (CAMS) predictions of the same weather quantity as an additional covariate could enhance the skill of the post-processing methods and whether it contributes to the successful integration of spatial dependence between marginals. Our study confirms that post-processed forecasts are substantially superior to raw and climatological predictions, and the utilization of CAMS forecasts provides a further significant enhancement both in the univariate and multivariate setup. We also demonstrate that post-processing significantly improves the predictions of low visibility events, which opens the door for aeronautical applications.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Copernicus Atmosphere Monitoring Service(CAMS) ensemble calibration ensemble copula coupling multivariate post-processing Schaake shuffle visibility
Megjelenés:	Meteorological Applications. - 31 : 6 (2024), p. 1-19. -
További szerzők:	Baran Sándor (1973-) (matematikus, informatikus)
Pályázati támogatás:	K142849 OTKA 2023-2.1.1-ÚNKP-2023-00011 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: