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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 largelyrelies on the availability of efficient methods to extract meaningful information, and requires to process the environmental events atthe speed data are acquired. This paper focuses on the evaluation of methods to approximate observed rain data, in real conditions ofsparsity of the observations. The novelty stands in the selection of a particularly complex area, Liguria region, located in the north-westof Italy, where the orography and the closeness to the sea causes complex hydro-meteorological events. Approximation results arecompared on a fine granularity in terms of cumulated rain interval used, gathered from two different rain gauge networks, with differentcharacteristics 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 rainfields needed for storm tracking, to support effective monitoring of meteorological events

A COMPARISON OF METHODS FOR THE APPROXIMATION AND ANALYSIS OF RAINFALL FIELDS IN ENVIRONMENTAL APPLICATIONS