Utility of Multisite Stochastic Simulations to Characterize and Model the Seasonal and Spatial Variability of Short-Duration Precipitation

Abstract

The knowledge of the statistical variability of precipitation (P) at short durations (≤24 h) is necessary to support engineering applications and hydrologic modeling. In this talk, we provide novel insights into the seasonal and spatial variability of two statistical properties of short-duration P that have received less attention, including the spatiotemporal correlation structure (STCS) and the marginal distribution. To this end, we design a framework based on multisite Monte Carlo simulations with the Complete Stochastic Modeling Solution (CoSMoS) which we test using a dense network of 223 high-resolution (30 min) rain gages with more than 20 years of observations in central Arizona. We first show that an analytical model and a three-parameter probability distribution capture the empirical STCS and marginal distribution of P, respectively, across Δt’s from 0.5 to 24 h and the summer and winter seasons. We then conduct Monte Carlo multisite stochastic simulations of P time series with CoSMoS, which reveal that the statistical properties of short-duration P exhibit significant seasonal differences, especially at low Δt. In summer, the STCS of P is weaker and the distributions are heavy-tailed because of the dominance of localized convective thunderstorms. Winter P has instead stronger STCS and lighter tails of the distributions as a result of more widespread and longer frontal systems. The Monte Carlo experiments also demonstrate that, in most cases, P is characterized by a homogeneous and isotropic STCS across the region, and by parameters of the marginal distribution that are constant for the shape and dependent on elevation for scale and P occurrence. The only exception is winter P at Δt ≥ 3 h, where anisotropy could be introduced by the motion of frontal storms, and additional factors are required to explain the variability of the scale parameter. The findings of this work are useful for improving stochastic P models and validating convection-permitting atmospheric models.