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001-es BibID:	BIBFORM088189
035-os BibID:	(WoS)000560814600001 (Scopus)85089706946
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Statistical post-processing of heat index ensemble forecasts: Is there a royal road? / Sándor Baran, Ágnes Baran, Florian Pappenberger, Zied Ben Bouallegue
Dátum:	2020
ISSN:	0035-9009 1477-870X
Megjegyzések:	We investigate the effect of statistical post?processing on the probabilistic skill of discomfort index (DI) and indoor wet?bulb globe temperature (WBGTid) ensemble forecasts, both calculated from the corresponding forecasts of temperature and dew point temperature. Two different methodological approaches to calibration are compared. In the first case, we start with joint post?processing of the temperature and dew point forecasts and then create calibrated samples of DI and WBGTid using samples from the obtained bivariate predictive distributions. This approach is compared with direct post?processing of the heat index ensemble forecasts. For this purpose, a novel ensemble model output statistics model based on a generalized extreme value distribution is proposed. The predictive performance of both methods is tested on the operational temperature and dew point ensemble forecasts of the European Centre for Medium?Range Weather Forecasts and the corresponding forecasts of DI and WBGTid. For short lead times (up to day 6), both approaches significantly improve the forecast skill. Among the competing post?processing methods, direct calibration of heat indices exhibits the best predictive performance, very closely followed by the more general approach based on joint calibration of temperature and dew point temperature. Additionally, a machine learning approach is tested and shows comparable performance for the case when one is interested only in forecasting heat index warning level categories.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Quarterly Journal Of The Royal Meteorological Society. - 146 : 732 (2020), p. 3416-3434. -
További szerzők:	Baran Ágnes (1972-) (matematikus) Pappenberger, Florian Ben Bouallègue, Zied
Pályázati támogatás:	NKFIH NN125679 Egyéb EFOP-3.6.2-16-2017-00015 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM078818
035-os BibID:	(WoS)000474149700026 (Scopus)85063800348
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Statistical post-processing of dual-resolution ensemble forecasts / Sándor Baran, Martin Leutbecher, Marianna Szabó, Zied Ben Bouallègue
Dátum:	2019
ISSN:	0035-9009 1477-870X
Megjegyzések:	The computational cost as well as the probabilistic skill of ensemble forecasts depends on the spatial resolution of the numerical weather prediction model and the ensemble size. Periodically, e.g. when more computational resources become available, it is appropriate to reassess the balance between resolution and ensemble size. Recently, it has been proposed to investigate this balance in the context of dual-resolution ensembles, which use members with two different resolutions to make probabilistic forecasts. This study investigates whether statistical post-processing of such dual-resolution ensemble forecasts changes the conclusions regarding the optimal dual-resolution configuration. Medium-range dual-resolution ensemble forecasts of 2 m temperature have been calibrated using ensemble model output statistics. The forecasts are produced with ECMWF's Integrated Forecast System and have horizontal resolutions between 18 and 45 km. The ensemble sizes range from 8 to 254 members. The forecasts are verified with SYNOP station data. Results show that score differences between various single- and dual-resolution configurations are strongly reduced by statistical post-processing. Therefore, the benefit of some dual-resolution configurations over single-resolution configurations appears to be less pronounced than for raw forecasts. Moreover, the ranking of the ensemble configurations can be affected by the statistical post-processing.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk statisztikai utófeldolgozás valószínűségi időjárás előrejelzés
Megjelenés:	Quarterly Journal of the Royal Meteorological Society. - 145 : 721 (2019), p. 1705-1720. -
További szerzők:	Leutbecher, Martin Szabó Marianna (1995-) (programtervező informatikus) Ben Bouallègue, Zied
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00002 EFOP NKFIH NN125679 egyéb
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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