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Most operational models in atmospheric physics, meteorology and climatology nowadays adopt spherical geodesic grids and require "ad hoc" developed interpolation procedures. The author does a comparison between chosen representatives of linear, distance-based and cubic interpolation schemes outlining their advantages and drawbacks in this specific application field. Numerical experiments on a standard test problem, while confirming a good performance of linear and distance-based schemes in a single interpolation step, also show their minor accuracy with respect to the cubic scheme in the more realistic simulation of advection of a meteorological field.

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay how you drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper we propose an approach in order to identify the driver behaviour exploring the usage of unsupervised machine learning techniques. A real world case study is performed to evaluate the e↵ectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

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