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Localization and flow-dependency on blending techniques
Meteorologische Zeitschrift ( IF 1.2 ) Pub Date : 2020-10-16 , DOI: 10.1127/metz/2019/0987
Aitor Atencia , Yong Wang , Alexander Kann , Florian Meier

Lagrangian extrapolation nowcasting is still the most accurate technique for predicting precipitation for the first hours despite the improvements achieved by data assimilation in high-resolution numerical weather prediction models. Yet, the combination of both sources by blending techniques is necessary for creating a seamless forecast for the nowcasting range. The blending is usually carried by adding one forecast to the other one multiplied previously by a weight. This weight can be computed climatologically or taking into account parameters such as the synoptic situation or the intensity of precipitation from the different sources of forecast. In this paper, a general formulation for computing the blending weight is introduced. This new formulation allows not only to introduce new factors such as the localization (pixel scale) but also to isolate the effect of some parameters such as the flow-dependency (synoptic situation). A sensitivity study of these parameters, separated and combined, is carried out for a whole month. The results show the relative importance separately of each effect (localization and flow-dependency) but, most importantly, the improvements in terms of combining the two sources of information when these two factors are used together in the computation of the blending weights.

中文翻译:

融合技术的本地化和流程依赖性

尽管在高分辨率数值天气预报模型中通过数据同化实现了改进,但拉格朗日外推法临近预报仍是最准确的预报头几个小时降水的技术。然而,通过混合技术将两种来源结合起来对于创建临近预报范围的无缝预报是必要的。通常通过将一个预测值与另一个预测值乘以先前的权重来进行混合。该权重可以通过气候计算或考虑诸如天气状况或来自不同预报源的降水强度之类的参数来计算。本文介绍了计算混合重量的一般公式。这种新的公式不仅允许引入新的因素,例如定位(像素比例),而且可以隔离某些参数的影响,例如流量相关性(天气情况)。这些参数(分离和组合)的敏感性研究进行了一个月。结果显示了每种效应(局部性和流量依赖性)的相对重要性,但是最重要的是,当将这两个因素一起用于混合权重计算时,结合了两种信息源的改进。
更新日期:2020-10-27
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