  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 modelling
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 modelling capabilities of D-STEM v2 as well as the
complexity reduction techniques required when dealing 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.
