Statistical Post-Processing with Standardized Anomalies Based on a 1 km Gridded Analysis

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...

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Bibliographic Details
Main Authors: Markus Dabernig, Irene Schicker, Alexander Kann, Yong Wang, Moritz N. Lang
Format: Article
Language:English
Published: Borntraeger 2020-10-01
Series:Meteorologische Zeitschrift
Subjects:
Online Access:http://dx.doi.org/10.1127/metz/2020/1022
Description
Summary: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.
ISSN:0941-2948