Computer simulations, satellites and various other technological advances have paved the way into unprecedented scientific territory by generating volumes of previously uncollectable datasets. Properly utilizing these new data products to promote scientific discovery requires that they, first, be (i) validated and (ii) paired with an appropriate measure of uncertainty. Validating a data product entails comparing "synthetic" data (e.g., computer model simulations, remote sensing measurements, or statistical predictions) with observational counterparts to substantiate the digital data for its use in scientific discovery. Uncertainty quantification (UQ) is a necessary component to validation and entails accounting for and stating the uncertainties associated with scientific conclusions derived from digital or observational data. The purpose of this research is to promote scientific discovery using digital data products by developing statistical methods to perform validation and uncertainty quantification.
Given the strong need to perform, and the substantial challenges facing, statistical validation and UQ, this research will (i) develop new validation strategies for simulated and digital datasets based on scientifically motivated features; (ii) develop multivariate spatio-temporal statistical models that can be used to implement, and perform UQ for spatio-temporal data products; and, (iii) develop scalable computation techniques for fitting the developed spatio-temporal statistical models. This research will implement these techniques on data products in atmospheric, agricultural and environmental sciences to facilitate their use in scientific inquiry.
