Fractal modelling of rain fields: From event-on-demand to annual statistics

Rain is the dominant attenuator for radio systems operating above 10 GHz. Correctly configuring systems that can dynamically compensate for rain fading requires a detailed knowledge of spatial and temporal rain field variation. Ideally, this would be provided by a database of meteorological radar measurements. Unfortunately, such data is scarce. Alternatives to radar measurements are methods for simulating rain fields in time and space. This paper discusses a monofractal, additive (in the logarithmic domain) discrete cascade model for simulating rain fields in two spatial dimensions. The model produces events-on-demand, customised to an input rain rate parameter and desired rain event type (stratiform or convective). In order to test the long term statistics of a proposed radio system, simulated rain field datasets are required which will reproduce the annual rain statistics for the average year. Work towards a method to convert from a set of single events into a set capable of reproducing annual statistics is presented in this paper.