High resolution space-time rainfall estimation remains an open challenge for hydro-meteorologists. The overall purpose of this project is to improve the quality of radar rainfall estimation at high spatial and temporal resolutions and their applicability to urban storm water management. More specifically, the project will take advantage of two unique and innovative ground rainfall monitoring networks from two teams in France and Taiwan, respectively, to envisage the impacts of the following issues, which are seldom addressed in their whole complexity, in radar rainfall estimation: 1. The variability of rainfall drop size distributions (DSDs): the variability of DSDs will be characterised in both space and time at fine scales, and its impact on radar measurements will be quantified. 2. Wind drift effect: the advection of rain drops throughout their fall from radar observation elevations to the ground will be investigated, and its impact on radar rainfall estimation will be quantified. The investigation of the above two aspects will eventually contribute to the improvement of a 3D+1 (3D in space + 1D in time) model for rain drops fields. The subsequent impact of both aspects on storm water management will be quantified through urban hydro-dynamic modelling. Existing case studies with already calibrated models will be used with a focus on rainfall modelling. In addition to the ground rainfall networks, this project will utilise the complementary research strengths from each team. That is, the spatial and temporal rainfall modelling within a universal multifractal framework from the French team, and the rainfall object tracking using 3D+1 radar data from the Taiwan team. This cooperation will enable an in-depth research on the proposed work, and the diverse weather conditions from the two countries will enable development of a 'scale-able' tools that can provide robust solution to radar rainfall estimation.