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001-es BibID:BIBFORM115661
035-os BibID:(cikkazonosító)e2157 (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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2.

001-es BibID:BIBFORM083317
035-os BibID:(cikkazonosító)e1818 (WoS)000476362500001 (Scopus)85069864721
Első szerző:Díaz, Mailiu
Cím:Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile / Mailiu Díaz, Orietta Nicolis, Julio César Marín, Sándor Baran
Dátum:2020
ISSN:1350-4827
Megjegyzések:Modelling forecast uncertainty is a difficult task in any forecasting problem. In weather forecasting a possible solution is the use of forecast ensembles, which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations to provide information about the expected uncertainty. Currently all major meteorological centres issue forecasts using their operational ensemble prediction systems. However, it is a general problem that the spread of the ensemble is too small compared to observations at specific sites resulting in under-dispersive forecasts, leading to a lack of calibration. In order to correct this problem, various statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques were tested for calibrating nine member ensemble forecasts of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting model using different planetary boundary layer and land surface model parametrizations. In particular, the ensemble model output statistics and Bayesian model averaging techniques were implemented and, since the observations are characterized by large altitude differences, the estimation of model parameters was adapted to the actual conditions at hand. Compared to the raw ensemble, all tested post-processing approaches significantly improve the calibration of probabilistic forecasts and the accuracy of point forecasts. The ensemble model output statistics method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill.
Tárgyszavak:Természettudományok Környezettudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Bayesian model averaging
ensemble model output statistics
ensemble post-processing
probabilistic forecasting
temperature forecast
Megjelenés:Meteorological Applications. - 27 : 1 (2020), p. 1-12. -
További szerzők:Nicolis, Orietta Marín, Julio César Baran Sándor (1973-) (matematikus, informatikus)
Pályázati támogatás:Bolyai János Kutatási Ösztöndíj
MTA
NN125679
Egyéb
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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