Rainfall is a physical phenomenon resulting from the combination of numerous physical processes involving a wide range of scales, from microphysical processes to the general circulation of the atmosphere. Moreover, unlike other geophysical variables such as water vapor concentration, rainfall is characterized by a relaxation behavior that leads to an alternation of wet and dry periods. It follows that rainfall is a complex process which is highly variable both in time and space. Precipitation is thus characterized by the following features: rain/no-rain intermittency, multiple scaling regimes, and extreme events. All these properties are difficult to model simultaneously, especially when a large time and/or space scale domain is required. The aim of this paper is to develop a simulator capable of generating high-resolution rain-rate time series (15 s), the main statistical properties of which are close to an observed rain-rate time series. We also attempt to develop a model having consistent properties even when the fine-resolutionsimulated time series are aggregated to a coarser resolution. In order to break the simulation problem down into subcomponents, the authors have focused their attention on several key properties of rainfall. The simulator is based on a sequential approach in which, first, the simulation of rain/no-rain durations permits the retrieval of fractal properties of the rain support. Then, the generation of rain rates through the use of a multifractal, Fractionally Integrated Flux (FIF), model enables the restitution of the rainfall's multifractal properties. This second step includes a denormalization process that was added in order to generate realistic rain-rate distributions.
In this paper we present a method for rebuilding rainfall maps at high resolution (500 m × 500 m, 1 min). This method is based on the assimilation of opportunistic measurements of the attenuation that aff ects the signals coming from TV satellites in a model of spatiotemporal advection of rainfall fi elds. At the frequencies used (Ku band), the attenuation aff ecting the signals in the atmosphere is mainly due to rain. We set a sensor (fi eld analyzer) on the ground, and then measured the mean rainfall over the link. This method was applied to a simulated network of sensors. These simulated sensors were realistically set over the Paris area, on a zone assumed to be typical of an area with high socio-economic issues (fl ood prevention, water resources management). We compared the simulated rainfall maps with the maps rebuilt by our algorithm. We then showed the feasibility of our approach for measuring the rainfall in urban areas with high resolution.
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