Abstract. Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity.

中文翻译：

---

通过对流尺度数值天气预报将30秒的非高斯性快速更新

摘要。对于基于集合的数据同化（DA），非高斯预测误差是一个挑战，特别是对于更非线性的对流动力学而言。在这项研究中，我们调查了使用1000个成员的集成卡尔曼滤波器在1 km分辨率下的预测误差分布的非高斯程度，以及它如何受到DA更新频率和观测值的影响。使用SCALE（高级图书馆和环境的可伸缩计算）模型和LETKF（本地整体变换卡尔曼滤波器）进行区域数值天气预报实验，以每30秒相控阵雷达观测为参考。结果表明，非高斯性在对流云中迅速发展，并且对DA频率和同化观测数敏感。