A stochastic model for high-resolution space-time precipitation simulation

Abstract

[1] High-resolution space-time stochastic models for precipitation are crucial for hydrological applications related to flood risk and water resources management. In this study, we present a new stochastic space-time model, STREAP, which is capable of reproducing essential features of the statistical structure of precipitation in space and time for a wide range of scales, and at the same time can be used for continuous simulation. The model is based on a three-stage hierarchical structure that mimics the precipitation formation process. The stages describe the storm arrival process, the temporal evolution of areal mean precipitation intensity and wet area, and the evolution in time of the twodimensional storm structure. Each stage of the model is based on appropriate stochastic modeling techniques spanning from point processes, multivariate stochastic simulation and random fields. Details of the calibration and simulation procedures in each stage are provided so that they can be easily reproduced. STREAP is applied to a case study in Switzerland using 7 years of high-resolution (2 3 2 km 2 ; 5 min) data from weather radars. The model is also compared with a popular parsimonious space-time stochastic model based on point processes (space-time Neyman-Scott) which it outperforms mainly because of a better description of spatial precipitation. The model validation and comparison is based on an extensive evaluation of both areal and point scale statistics at hydrologically relevant temporal scales, focusing mainly on the reproduction of the probability distributions of rainfall intensities, correlation structure, and the reproduction of intermittency and wet spell duration statistics. The results shows that a more accurate description of the space-time structure of precipitation fields in stochastic models such as STREAP does indeed lead to a better performance for properties and at scales which are not used in model calibration.

A framework for assessing hydrological regime sensitivity to climate change in a convective rainfall environment: a case study of two medium-sized eastern Mediterranean catchments, Israel

“…Thober et al (2014) presented a daily but spatially high-resolution rainfall generator (4 km 2 ) that satisfies the requirements for assessments of hydrological response at regional and continental scales. Two WG models that were recently presented by Paschalis et al (2013) and , simulate rainfall in high spatial and temporal resolution (4 km 2 and 5 min and 0.25 km 2 and 5 min, respectively). This is a resolution that is adequate for hydrological modeling of small size (less than 50 km 2 ) catchments assuming reliable rainfall estimates (Borga et al, 2014).…”

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

“…The analog-based approach derives the forecast probability density function (pdf) by retrieving a set of similar situations from an archive of precipitation events (Panziera et al, 2011;, the local Lagrangian approach derives the pdf by collecting the precipitation values in a neighborhood of a given grid point in Lagrangian frame of reference (Hohti et al, 2000;Germann and Zawadzki, 2004) and the stochastic approach exploits a random number generator to compute an ensemble of equally likely precipitation fields, for example by adding stochastic perturbations to a deterministic extrapolation nowcast (Pegram and Clothier, 2001a, b;Bowler et al, 2006;Metta et al, 2009;Berenguer et al, 2011;Seed et al, 2013;Atencia and Zawadzki, 2014;Dai et al, 2015). The stochastic approach is also extensively used to produce ensembles of precipitation fields that characterize the radar measurement uncertainty (e.g., Jordan et al, 2003;Germann et al, 2009) and for design storm studies (e.g., Willems, 2001a;Paschalis et al, 2013).…”

A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform

“…A similar approach was implemented within the RainFARM model of Rebora et al (2006b) for stochastic NWP precipitation downscaling in space and time. STREAP (Paschalis et al, 2013) also uses the Fourier transform for design storm generation to produce Gaussian random fields with an exponentially decaying isotropic autocorrelation function. Atencia and Zawadzki (2014) used the power spectrum of the last observed radar rainfall field to generate spatially correlated and anisotropic stochastic perturbations for ensemble precipitation nowcasting.…”