Interpolating climate variables by using INLA and the SPDE approach

Gridded observational products of the main climate parameters are essential in climate science. Current interpolation approaches, implemented to derive such products, often lack of a proper uncertainty propagation and representation. In this study, we introduce a Bayesian spatiotemporal approach based on the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE). The method is described and discussed by using a real case study based on high-resolution monthly 2-m maximum (Tmax) and minimum (Tmin) air temperature over Italy in 1961–2020. The INLA-SPDE based approach is able to properly take into account uncertainties in the final gridded products and offers interesting promising advantages to deal with nonstationary and non-Gaussian multisource data.