Combining meteorological radar and network of rain gauges data for space–time model development

Abstract

SUMMARYTechnological developments and the trend to go higher and higher in frequency give rise to the need for true space-time rain field models for testing the dynamics of fade countermeasures. There are many models that capture the spatial correlation of rain fields. Worth mentioning are those models based on cell ensembles. However, the rain rate fields created in this way need the introduction of the time variable to reproduce their dynamics. In this paper, we have concentrated on addressing the time domain effects while we have relied on existing spatial rain field models for creating initial fields, which are propagated (advected) according to proposed models and assumptions, some of which have been drawn from a combined use of a concurrent weather radar and a network of rain gauges. The dynamic modeling presented simulates the advection of a synthetically generated rain field according to dynamic, spatially correlated advection fields extracted from the analysis of weather radar images. Experimental data and model fits have been presented as well. Further ideas on how to improve the realism of the generated dynamic fields have also been presented. Furthermore, the limitations of radar data, especially those related to their limited time resolution, for the required space-time models have been pointed out. These can be overcome by using data from a network of rain gauges. However, it is important to be aware of the similarities and differences between these two sources. A comparative study of these two data sets has also been presented.

Stochastic convective rain‐field simulation using a high‐resolution synoptically conditioned weather generator (HiReS‐WG)

“…[], Pastoriza et al . [], and Yakir and Morin []. A short description of the algorithm is given below, and detailed explanations can be found in Feral et al .…”

“…Ellipses give a good depiction of the shape of convective rain cells [Barnolas et al, 2010;Karklinsky and Morin, 2006;Peleg and Morin, 2012]. The HYCELL model has been used in several studies, for example, in Feral et al [2005Feral et al [ , 2006, Pastoriza et al [2011], and Yakir and Morin [2011]. A short description of the algorithm is given below, and detailed explanations can be found in Feral et al [2003].…”

“…This is crucial in case of catchments with a high sensitivity to high rain intensity for short durations, for example, in arid or urban environments. A more realistic convective cell (e.g., without smooth decay from the center to the boundaries) can be generated, for example, by adding noise to the simulated convective cell [ Pastoriza et al ., ]. Here we apply a different approach in which the pixels are stochastically rearranged for each cell while preserving the spatial correlation of the rainfall (which is an input for this module).…”

Special issue on ‘Channel modelling and propagation impairment simulation activities within the SatNEx project’

“…Vicenze PASTORIZA, Adolfo NUNEZ, F. MACHADO, P. MARINO, Fernando PEREZ FONTAN, Uwe-Carsten FIEBIG: 'Combining meteorological radar and network of rain gauges data for space-time model development' [9] Technological developments and the trend to go higher and higher in frequency, give rise to the need for true space-time rain field models for testing the dynamics of fade countermeasures. There are many models that capture the spatial correlation of rain fields.…”

Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

“…Panofsky and Brier [9] introduced a low-pass filter, which was borrowed from electrical engineering terminology, and removed the high variability of noise from the data and leave only the low frequency components. In the recent years, the filtering method has been applied to reduce noise of radar rainfall in some studies [10][11][12]. The Kalman filtering approach has the main advantage of providing a real time scheme to calibrate radar rainfall estimates based on rain gauge measurements.…”