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001-es BibID:	BIBFORM123764
035-os BibID:	(Scopus)85211113386 (WoS)001348259000003
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Clustering-based spatial interpolation of parametric post-processing models / Sándor Baran, Mária Lakatos
Dátum:	2024
ISSN:	0882-8156
Megjegyzések:	Since the start of the operational use of ensemble prediction systems, ensemblebased probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical post-processing. Nowadays, one can choose from a large variety of post-processing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observationpairs, thuspost-processedforecastsareusuallyavailableonlyatthoselocations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) post-processing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Weather And Forecasting. - 39 : 11 (2024), p. 1591-1604. -
További szerzők:	Nagy-Lakatos Mária (1995-) (programtervező informatikus)
Pályázati támogatás:	K142849 OTKA 2021-1.2.4-TÉT-2021-00020 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM111837
035-os BibID:	(WoS)001042530700001 (Scopus)85172181122
Első szerző:	Szabó Marianna (programtervező informatikus)
Cím:	Parametric Postprocessing of Dual-Resolution Precipitation Forecasts / Marianna Szabó, Estíbaliz Gascón, Sándor Baran
Dátum:	2023
ISSN:	0882-8156
Megjegyzések:	Recently, all major weather centres issue ensemble forecasts, which differ both in ensemble size and spatial resolution - even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the last few years, the plans of upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9 km resolution and a 51-member ensemble with 18 km resolution induced an extensive study of the forecast skill of both raw and post-processed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical post-processing with the help of dual-resolution 24h precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as high-resolution and extend it with a low-resolution (29-km grid) 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS post-processing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analysed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Weather And Forecasting. - 38 : 8 (2023), p. 1313-1322. -
További szerzők:	Gascón, Estíbaliz Baran Sándor (1973-) (matematikus, informatikus)
Pályázati támogatás:	K142849 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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