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Improving QPF by blending techniques at the meteorological service of Catalonia

机译：在加泰罗尼亚的气象部门通过混合技术改善QPF

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The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (+1h and +2h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (+3h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.
机译：加泰罗尼亚气象局（SMC）当前的非常短期和短期定量降水预报（QPF）是通过三种不同的方法得出的：雷达对流场的平流（ADV），对流的识别，跟踪和预报使用观测数据同化（雷达，卫星等）的结构（CST）和数值天气预报（NWP）模型。这些降水预报具有不同的特征，提前期和空间分辨率。这项研究的目的是将这些方法结合起来，以便在每个提前期获得一个单一的优化QPF。雷达预报（ADV和CST）与NWP模型的降水预报的这种组合（混合）是根据预测范围通过不同的方法进行的。首先，为了利用雷达观测的降雨位置和强度，将相位校正技术应用于NWP输出以得出附加的校正预报（MCO）。为了在第一小时和第二小时（+ 1h和+ 2h）中选择最佳的降水估计，雷达平流（ADV）的信息和模型（MCO）的校正输出通过使用不同的权重进行混合，权重会动态变化。量化这些预测质量的索引。该程序能够将雷达反射率场平流所给出的降雨定位和模式技能与利用NWP模型生成新的降水面积的能力相结合。从第三小时开始（+ 3h），由于基于雷达的预报通常技能较低，因此仅使用基于模型的定量降水预报。针对不同类型的事件（对流，中度对流和层状），对这种不同预测来源的混合进行了验证，从而获得了以操作和动态方式实施预测的可靠方法。

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