Abstract
Digital environmental data are becoming commonplace and the amount of information they provide is huge, yet complex to process, due to the size, variety, and dynamic nature of the data captured by the available sensing devices. Making use of the data largely relies on the availability of efficient methods to extract meaningful information, and requires to process the environmental events at the speed data are acquired. This paper focuses on the evaluation of methods to approximate observed rain data, in real conditions of sparsity of the observations. The novelty stands in the selection of a particularly complex area, Liguria region, located in the north-west of Italy, where the orography and the closeness to the sea causes complex hydro-meteorological events. Approximation results are compared on a fine granularity in terms of cumulated rain interval used, gathered from two different rain gauge networks, with different characteristics and spatial distribution. Moreover, beside traditional cross-validation comparison, we provide a qualitative comparisonbased on the analysis of the number and location of maxima of the approximation. Rain maxima are indeed crucial features of rain fields needed for storm tracking, to support effective monitoring of meteorological events.