Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data
报告题目：Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: A case study of precipitation in Australia from space to ground sensors
主讲人：钱国骐教授（澳大利亚墨尔本大学）
时间：2024年1月16日（周二）9:30 a.m.
地点：北院卓远楼305会议室
主办单位：统计与数学学院
摘要：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.
主讲人简介：
Dr. Qian is a statistician and applied mathematician having research expertise in statistics theory, biostatistics, bioinformatics, computational statistics, spatiotemporal statistics and mathematical and statistical methods for climate and earth sciences. His research is driven by real-world problems such as geohazard events forecasting, seasonal forecasting of tropical cyclones in Australia and Southern Pacific Ocean; precipitation dynamic profiling and forecasting for Australia; temperature dynamic profiling for Antarctica; and genome-wide association studies of genetic disorders; etc. Hence his research involves developing stochastic dynamic models for characterizing big and complex data, developing computationally efficient algorithms for statistical learning and data mining, and developing operational statistical procedures for prediction and early warning. Dr. Qian has published over 90 research papers including two computing software packages, and as principal supervisor, has supervised 15 PhD, 6 Honours and 50 master students to completion.
