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001-es BibID:	BIBFORM123764
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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