A clustering and classification method using a Gaussian mixture model (GMM) is used to summarize and simplify meteorological data from a numerical weather prediction (NWP) model. Each horizontal grid in the integration domain of the NWP model is characterized by a feature vector, which consists of a multivariable with multiple pressure levels. All horizontal grids at every forecast time are classified based on the GMM clustering. The classification results show that grids are clustered into air masses or disturbances with the same meteorological characteristics. This paper describes application of the proposed classification method as a framework to verify the forecast of surface solar irradiance from the NWP model. Satellite observation data are used as the reference so that verification can be performed over the integration domain of the NWP model for each air mass or disturbance that moves and changes shape over time. The mean square error (MSE) is decomposed into the square of the mean error and the MSE between variables centered on zero, the square root of which is called the centered root mean square error (CRMSE). The analyses are performed for forecast data over a 2 day forecast horizon. The change in mean error is not significant until the second day, whereas the CRMSE is maintained only during the first day. Each air mass has a different forecast error structure. The proposed framework clarifies the structure of the forecast error of the surface solar irradiance.

中文翻译：

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基于高斯混合模型分类的数值天气预报模型对地面太阳辐照度预测验证的框架

使用高斯混合模型（GMM）的聚类和分类方法用于总结和简化来自数值天气预报（NWP）模型的气象数据。NWP模型的集成域中的每个水平网格都具有一个特征向量，该特征向量由具有多个压力水平的多变量组成。根据GMM聚类对每个预测时间的所有水平网格进行分类。分类结果表明，网格被聚集成具有相同气象特征的气团或扰动。本文描述了所提出的分类方法的应用，以此作为验证NWP模型对地面太阳辐照度预测的框架。卫星观测数据用作参考，因此可以针对随时间变化和变化的每个空气质量或扰动，在NWP模型的积分域上执行验证。均方误差（MSE）分解为以零为中心的变量之间的均方误差和MSE的平方，其平方根称为中心均方根误差（CRMSE）。在2天的预测范围内对预测数据进行分析。平均误差的变化直到第二天才显着，而CRMSE仅在第一天保持不变。每个空气质量都有不同的预测误差结构。提出的框架阐明了地面太阳辐照度预测误差的结构。均方误差（MSE）分解为以零为中心的变量之间的均方误差和MSE的平方，其平方根称为中心均方根误差（CRMSE）。在2天的预测范围内对预测数据进行分析。平均误差的变化直到第二天才显着，而CRMSE仅在第一天保持不变。每个空气质量都有不同的预测误差结构。提出的框架阐明了地面太阳辐照度预测误差的结构。均方误差（MSE）分解为以零为中心的变量之间的均方误差和MSE的平方，其平方根称为中心均方根误差（CRMSE）。在2天的预测范围内对预测数据进行分析。平均误差的变化直到第二天才显着，而CRMSE仅在第一天保持不变。每个空气质量都有不同的预测误差结构。提出的框架阐明了地面太阳辐照度预测误差的结构。平均误差的变化直到第二天才显着，而CRMSE仅在第一天保持不变。每个空气质量都有不同的预测误差结构。提出的框架阐明了地面太阳辐照度预测误差的结构。平均误差的变化直到第二天才显着，而CRMSE仅在第一天保持不变。每个空气质量都有不同的预测误差结构。提出的框架阐明了地面太阳辐照度预测误差的结构。