Speaker: Dr. Dorit Hammerling
Title: Nonstationary Spatial Modeling of Massive Global Satellite Data
Abstract: Earth-observing satellite instruments obtain a massive number of observations every day. For example, tens of millions of sea surface temperature (SST) observations on a global scale are collected daily by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Despite their size, such datasets are incomplete and noisy, necessitating spatial statistical inference to obtain complete, high-resolution fields with quantified uncertainties. Such inference is challenging due to the high computational cost, the nonstationary behavior of environmental processes on a global scale, and land barriers affecting the dependence of SST. We develop a multi-resolution approximation (M-RA) of a Gaussian process (GP) whose nonstationary, global covariance function is obtained using local fits. The M-RA model requires domain partitioning, which can be set up application-specifically. In the SST case, we partition the domain purposefully to account for and weaken dependence across land barriers. Our M-RA implementation is tailored to distributed-memory computation in high-performance-computing environments. We analyze a MODIS SST dataset consisting of more than 43 million observations, to our knowledge the largest dataset ever analyzed using a probabilistic GP model. We show that our nonstationary model based on local fits provides substantially improved predictive performance relative to a stationary approach.
Biosketch: Dr. Hammerling obtained an M.A. and PhD (2012) from the University of Michigan in Statistics and Engineering, followed by a post-doctoral fellowship at the Statistical Applied Mathematical Sciences Institute (SAMSI) in the program for Statistical Inference for massive data. She then joined the National Center for Atmospheric Research in the Institute for Mathematics Applied to the Geosciences and later worked in the Machine Learning division before becoming an Associate Professor in Applied Mathematics and Statistics at the Colorado School of Mines in January 2019. She received the Early Investigator Award from the American Statistical Association, Section on Statistics and the Environment, in 2018, and Outstanding Associate Professor of the College, in 2024.