Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: A case study of precipitation in Australia from space to ground sensors

Abstract

We develop a method for discovering a set of optimally representative dynamical locations (ORDL), a small subset of observed locations that are the most informative of the dynamics of a real complex system, as embodied in big spatiotemporal data. We achieve this through a two-pronged approach: (a) by reducing the multivariate time series data into a small set of time series with minimal loss of information on the dynamics of the system, (b) by exploiting the best that remote sensing and in-situ observations can offer. In the former, we extend the recently proposed empirical dynamical quantiles for univariate time series to multivariate data using a directional statistical depth measure and principal eigen-decomposition method. In the latter, we perform data fusion to leverage remotely sensed precipitation from multiple satellite platforms in addition to ground-based rain gauges to improve overall accuracy and spatial coverage. We demonstrate our method in the context of precipitation data over 2003–2021 for Australia. Of the six states, the location, ranking and number of ORDL suggest that Queensland has seen the most significant variability in precipitation while that in Victoria has remained relatively stable. Finally, this study has uncovered ungauged locations in data-sparse regions of Australia where the installation of future rain gauges can optimally represent precipitation dynamics in the region under a changing climate.