Stochastic weather generators (SWGs) are digital twins of complex weather processes and widely used in agriculture and urban design. Due to improved measuring instruments, an accurate SWG for high-frequency precipitation is now possible. However, high-frequency precipitation data are more zero-inflated, skewed, and heavy-tailed than common (hourly or daily) precipitation data. Therefore, classical methods that either model precipitation occurrence independently of their intensity or assume that the precipitation follows a censored meta-Gaussian process may not be appropriate. In this work, we propose a novel multi-site precipitation generator that drives both occurrence and intensity by a censored non-Gaussian vector autoregression model with skew-symmetric dynamics. The proposed SWG is advantageous in modeling skewed and heavy-tailed data with direct physical and statistical interpretations. We apply the proposed model to 30-second precipitation based on the data obtained from a dense gauge network in Lausanne, Switzerland. In addition to reproducing the high-frequency precipitation, the model can provide accurate predictions as the long short-term memory (LSTM) network but with uncertainties and more interpretable results.

随机天气发生器（SWG）是复杂天气过程的数字孪生，广泛用于农业和城市设计中。由于改进了测量仪器，现在可以实现用于高频降水的精确SWG。但是，高频降水数据比普通（每小时或每天）的降水数据更零膨胀，偏斜和重尾。因此，要么独立于降水强度模拟降水发生的模型，要么假设降水遵循经过审查的亚高斯过程的经典方法可能不合适。在这项工作中，我们提出了一种新颖的多站点降水产生器，该机制通过带有偏对称动力学的被审查的非高斯矢量自回归模型来驱动发生和强度。提议的SWG在通过直接物理和统计解释对偏斜和重尾数据进行建模方面具有优势。我们基于从瑞士洛桑的稠密测量网络获得的数据，将拟议的模型应用于30秒降水。除了再现高频降水外，该模型还可以作为长期短期记忆（LSTM）网络提供准确的预测，但具有不确定性和更可解释的结果。