The goal of this work is to optimize five parameters of expected high impact on COSMO model performance in non-hydrostatic regime using a calibration method based on the so called Meta-Model. The domain of consideration covers the Central-Eastern Mediterranean area with a horizontal mesh of 0.03⁰ (~3.3 km). The optimization technique considers observations at twenty-two Greek and sixty-four Israeli meteorological stations of 24 h accumulated precipitation, minimum and maximum 2-m dry and dew-point temperatures at sixty dates spread over the year 2019, five for every month, in order to comply with the seasonal variability. The optimum model parameters found have been evaluated by application on an independent set of sixty days chosen from the same year-period and in the same manner. An overall model performance score improvement of order 5% was obtained for both sets of test cases. Better verification scores were found regarding almost all meteorological fields considered with respect to average values, mean absolute and standard deviation errors, as well as threat scores regarding precipitation. The use of relatively sparse local observations, instead of the gridded data sets available to places with dense meteorological station networks where the methodology has been successfully applied in numerical weather prediction and climatology, makes this approach, upon its successful realization, suitable to a significantly larger community of model users with modest observation resources. This approach is also expected to be of value for domains with a large marine area fraction, like the domain chosen, where observations are sporadic, if available, by nature as well as to regions of low population density.

这项工作的目标是使用基于所谓的元模型的校准方法来优化在非静水状态下对 COSMO 模型性能有很大影响的五个参数。考虑范围涵盖地中海中东部地区，水平网格为 0.03 ⁰（约 3.3 公里）。该优化技术考虑了在 22 个希腊和 64 个以色列气象站的 24 小时累积降水量、2019 年分布在 60 个日期的最低和最高 2 米干燥和露点温度的观测，每月 5 个，在以适应季节变化。已找到的最佳模型参数已通过应用程序对从同一年期间以相同方式选择的一组独立的 60 天进行了评估。两组测试用例的整体模型性能得分提高了 5%。在考虑平均值、平均绝对和标准偏差误差以及与降水有关的威胁评分方面，几乎所有气象领域都发现了更好的验证分数。使用相对稀疏的本地观测，而不是可用于具有密集气象站网络的地方可用的网格数据集，该方法已成功应用于数值天气预报和气候学，使得该方法在成功实现后适用于更大的拥有适度观察资源的模型用户社区。预计这种方法对于具有较大海洋面积分数的域（例如所选域）具有价值，在这些域中，观察结果是零星的（如果有的话），以及人口密度低的区域。成功实现后，适用于具有适度观测资源的更大的模型用户社区。预计这种方法对于具有较大海洋面积分数的域（例如所选域）具有价值，在这些域中，观察结果是零星的（如果有的话），以及人口密度低的区域。成功实现后，适用于具有适度观测资源的更大的模型用户社区。预计这种方法对于具有较大海洋面积分数的域（例如所选域）具有价值，在这些域中，观察结果是零星的（如果有的话），以及人口密度低的区域。