Functional spatio-temporal data naturally arise in many environmental and climate applications where data are collected in a three-dimensional space over time. The MATLAB D-STEM v1 software package was first introduced for modeling multivariate space-time data and has been recently extended to D-STEM v2 to handle functional data indexed across space and over time. This paper introduces the new modeling capabilities of D-STEM v2 as well as the complexity reduction techniques required when

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... with large data sets. Model estimation, validation and dynamic kriging are demonstrated in two case studies, one related to ground-level air quality data in Beijing, China, and the other one related to atmospheric profile data collected globally through radio sounding.