Spatial prediction and mapping temperature

Temperature, one of the most important atmospheric variables, has a direct impact on physical and biological processes and its analysis in space and time play a crucial role in studying climate change. Here the results of a comparison between two ways of estimating models of spatial dependence are evaluated: kriging methods and Bayesian inference using the Integrated Nested Laplace Approximation (INLA).