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

Abstract

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. 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.