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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
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001-es BibID:BIBFORM086413
Első szerző:Lerch, Sebastian (matematikus)
Cím:Simulation-based comparison of multivariate ensemble post-processing methods / Sebastian Lerch, Sándor Baran, Annette Möller, Jürgen Groß, Roman Schefzik, Stephan Hemri, and Maximiliane Graeter
Dátum:2020
ISSN:1607-7946
Megjegyzések:Many practical applications of statistical post-processing methods for ensemble weather forecasts require accurate modeling of spatial, temporal, and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state-of-the-art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow ready interpretation of the effects of different types of misspecifications in the mean, variance, and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.
Tárgyszavak:Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Nonlinear Processes in Geophysics. - 27 : 2 (2020), p. 349-371. -
További szerzők:Baran Sándor (1973-) (matematikus, informatikus) Möller, Annette (1981-) (statisztikus) Groß, Jürgen Schefzik, Roman Hemri, Stephan Graeter, Maximiliane
Pályázati támogatás:NKFIH NN125679
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DOI
Intézményi repozitóriumban (DEA) tárolt változat
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