Geostatistical simulation of two-dimensional fields of raindrop size distributions at the meso-¿ scale

The large variability of the raindrop size distribution (DSD) in space and time must be taken into account to improve remote sensing of precipitation. The ability to simulate a large number of 2-D fields of DSDs sharing the same statistical properties provides a very useful simulation framework that nicely complements experimental approaches based on DSD ground measurements. These simulations can be used to investigate radar beam propagation through rain and to evaluate different radar retrieval techniques. The proposed approach uses geostatistical methods to provide structural analysis and stochastic simulation of DSD fields. First, the DSD is assumed to follow a Gamma distribution with three parameters. As a consequence, 2-D fields of DSDs can be described as a multivariate random function. The parameters are normalized using a Gaussian anamorphosis and simulated by taking advantage of fast Gaussian simulation algorithms. Variograms are used to characterize the spatial structure of the DSD fields. The generated fields have identical spatial structure and are consistent with the observations. Because intermittency cannot be simulated using this technique, the size of the simulation domain is limited to the meso-¿ scale (2-20 km). To assess the proposed approach, the method is applied to data collected during intense Mediterranean rainfall. Taylor's hypothesis is invoked to convert time series into 1-D range profiles. The anisotropy of the fields is derived from radar measurements. Simulated and measured reflectivity fields are in good agreement with respect to the mean, the standard deviation, and the spatial structure, demonstrating the promising potential of the proposed stochastic model of DSD field