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We propose a new statistical protocol for the estimation of precipitationusing lightning data. We first identify rainy events using a scan statistics,then we estimate Rainfall Lighting Ratio (RLR) to convert lightning number intorain volume given the storm intensity. Then we build a hierarchical Bayesianmodel aiming at the prediction of 15- and 30-minutes cumulated precipitation atunobserved locations and time using information on lightning in the same area.More specifically, we build a Bayesian hierarchical model in whichprecipitation is modeled as function of lightning count and space timevariation is handled using specific structured (random) effects. The meancomponent of the model relates precipitation and lightning assuming that thenumber of lightning recorded on a regular grid depends on the number oflightning occurring in neighboring cells. We analyze several model formulationswhere storms propagation speed, spatial dependence and time variationincorporates different descriptions of the phenomena at hand. The space-timevariation is assumed separable. The study area is located in Central Italy,where two storms, that differ for duration and intensity, are presented.

Tools for predicting rainfall from lightning records: events identification and rain prediction using a Bayesian hierarchical model