Martin A. Fisher School of Physics

February 26, 2020

Jim McElwaine, Professor of Geohazards, Durham University, UK and the Planetary Science Institute, Tucson

*Please note that this seminar will take place in Abelson 229.

Abstract: Over the last few years the HiRISE camera, mounted on the Mars Reconnaissance Orbiter, has returned many amazing pictures from the surface of Mars. Some of these reveal processes with terrestrial analogues, while others suggest completely new phenomena. It has been argued that many of these are caused by liquid water, which offers the possibility that life might be present under current conditions. However, current Martian temperatures and pressures are below the triple point of water so any liquid water could only exist transiently. I give a brief introduction to some of these surface flows and then study three types in detail: recurring slope lineae, dune gullies and debris flows. For each of these I first discuss the field observations then laboratory experiments and then explain theoretical models I have developed. In each case I show that the phenomena cannot be explained by liquid water, but instead are caused by the freezing and sublimation of carbon dioxide.

November 6, 2019

Nan Chen, Department of Mathematics, University of Wisconsin - Madison

Abstract: A nonlinear conditional Gaussian framework for extreme events prediction, state estimation (data assimilation) and uncertainty quantification in complex dynamical systems will be introduced in this talk. Despite the conditional Gaussianity, the models within this framework remain highly nonlinear and are able to capture strongly non-Gaussian features such as intermittency and extreme events. The conditional Gaussian structure allows efficient and analytically solvable conditional statistics that facilitates the real-time data assimilation and prediction.

In the first part of this talk, the general framework of the nonlinear conditional Gaussian systems, including a gallery of examples in geophysics, fluids, engineering, neuroscience and material science, will be presented. This is followed by its wide applications in developing the physics-constrained data-driven nonlinear models and the stochastic mode reduction. In the second part, an efficient statistically accurate algorithm is developed for solving the Fokker-Planck equation in large dimensions, which is an extremely important and challenging topic in prediction, data assimilation and uncertainty quantification. This new efficient algorithm involves a novel hybrid strategy for different subspaces, a judicious block decomposition and statistical symmetry. Rigorous mathematical analysis shows that this method is able to overcome the curse of dimensionality. In the third part of this talk, a low-order model within the nonlinear conditional Gaussian framework is developed to predict the intermittent large-scale monsoon extreme events in nature. The nonlinear low-order model shows higher prediction skill than the operational models and it also succeeds in quantifying the uncertainty in prediction. Other applications of this nonlinear conditional Gaussian framework, such as assimilating multiscale turbulent ocean flows and parameter estimation, will be briefly mentioned at the end of the talk.