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.

中文翻译：

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基于1 km网格分析的具有标准化异常的统计后处理

统计后处理对于纠正数值天气预报模型的系统误差是必要的，尤其是在阿尔卑斯山等复杂地形中。但是，这种后处理通常分别应用于每个网格点，这在计算上可能会很昂贵。我们想提出一种同时预测某个区域的所有网格点的方法，以加快运行预测时间。提出的后处理是SAPHIR项目的一部分，该项目提供了从临近预报到+72小时的交货期的预测，并且具有与分析相同的空间分辨率。所使用的分析是ZAMG提供的综合分析综合临近预报（INCA）系统，其空间分辨率为1 km。后处理变量是温度，降水，风和相对湿度。