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Functional spatio-temporal data naturally arise in many environmental andclimate applications where data are collected in a three-dimensional space overtime. The MATLAB D-STEM v1 software package was first introduced for modellingmultivariate space-time data and has been recently extended to D-STEM v2 tohandle functional data indexed across space and over time. This paperintroduces the new modelling capabilities of D-STEM v2 as well as thecomplexity reduction techniques required when dealing with large data sets.Model estimation, validation and dynamic kriging are demonstrated in two casestudies, one related to ground-level air quality data in Beijing, China, andthe other one related to atmospheric profile data collected globally throughradio sounding.

D-STEM v2: A Software for Modelling Functional Spatio-Temporal Data