Spatiotemporal modeling and prediction of solar radiation : Application of recent advances in space-time statistics to atmospheric data

[1] The radiation budget in the Earth-atmosphere system is what drives Earth's climate, and thus measurements of this balance are needed to improve our knowledge of Earth's climate and climate change. In the present paper we focus on the analysis of the surface shortwave radiation budget (SSRB), which is the amount of energy in the solar region of the electromagnetic spectrum (0.2-4.0 μm) absorbed at the surface. The SSRB has to be modeled from the surface to the top of the atmosphere, jointly with information about the state of the atmosphere and the surface. These data come from satellites orbiting the Earth and are often missing or disturbed. Its interest is not only at global scales; rather, regional, high-spatial-resolution description is also of interest as an indicator of changes and because of its relationship to aridification from well-developed vegetation. The goal of this paper is to estimate and predict the spatiotemporal evolution of SSRB data at a regional scale in eastern Spain. Two different spatiotemporal models with covariates are considered: one is based on modeling the spatiotemporal semivariogram and the other uses the Kalman filter technique for spatiotemporal prediction. We present comparisons between these two models with respect to the simpler, purely spatial model. The results show that there is not a great benefit to use the more complicated models, although there is a marginal improvement with complexity.