Contribution
Statistical Post-Processing with Standardized Anomalies Based on a 1 km Gridded Analysis
Dabernig, Markus; Schicker, Irene; Kann, Alexander; Wang, Yong; Lang, Moritz N.
Meteorologische Zeitschrift Vol. 29 No. 4 (2020), p. 265 - 275
44 références bibliographiques
publié: Oct 20, 2020
publication en ligne: May 13, 2020
manuscrit accepté: Mar 27, 2020
revision du manuscrit reçu: Mar 27, 2020
révision du manuscrit demandée: Feb 17, 2020
manuscrit reçu: Dec 17, 2019
Open Access (article peut être télechargé gratuitement)
Abstract
Statistical post-processing is necessary to correct systematic errors of numerical weather prediction models, especially in complex terrains such as the Alps. However, this post-processing is usually applied on every grid point individually, which can be computationally expensive. We want to present a method to forecast all grid points of a certain region simultaneously to expedite operational forecast times. The presented post-processing is part of the project SAPHIR, which provides forecasts from nowcasting up to +72 hours lead time with the same spatial resolution as the analysis. The used analysis is the Integrated Nowcasting through Comprehensive Analysis (INCA) system provided by ZAMG with a spatial resolution of 1 km. The post-processed variables are temperature, precipitation, wind and relative humidity. As a result highly resolved forecasts are presented with a similar performance to station-based forecasts.
Mots-clefs
ensemble forecasts • post-processing • temperature • precipitation • wind speed • relative humidity • standardized anomalies • gridded analysis