  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.
