A geostatistical approach to multisensor rain field reconstruction and downscaling

A rain field reconstruction and downscaling methodology is presented, which allows suitable integration of large scale rainfall information and rain-g auge measurements at the ground. The former data set is assumed to provide probabilistic indicators that are used to infer the parameters of the proba bility density function of the stochastic rain process at each pixel site. Rain-gauge measurements are assumed as the ground truth and used to constra in the reconstructed rain field to the associated point values. Downscaling is performed by assuming the a posteriori estimates of the rain figures at each grid cell as the a priori large-scale conditioning values for reconst ruction of the rain field at finer scale. The case study of an intense rain event recently observed in northern Italy is presented and results are dis cussed with reference to the modelling capabilities of the proposed methodo logy.