Title: Short-term Forecasting of Weather and Cancer: Finding
Initial Conditions and Parameters for Dynamical Models from Noisy Data

Speaker: Eric Kostelich, School of Mathematical & Statistical Sciences,
Arizona State University

Abstract: Computer models are essential to modern weather prediction.
They implement numerical methods to approximate the solutions of the
so-called primitive equations of atmospheric flow, but like any
differential equations, initial conditions must be supplied. However,
it is not possible to measure the state of the atmosphere at every model
grid point. Data assimilation refers to a class of methods to infer the
initial conditions from a sparse set of initial conditions and a set
of numerical forecasts. I will provide an overview of the problem
and describe a particular data assimilation method that is highly
accurate and efficient for numerical weather prediction and related
models. In addition, I will survey some potential applications
(and inherent difficulties) of data assimilation in mathematical
biology, especially differential equation models of prostate cancer
and glioma (brain tumors).

Bio: Eric Kostelich is President’s Professor of Mathematics at
Arizona State University. He received his Ph.D. degree in applied
mathematics from the University of Maryland at College Park and completed
postdoctoral work in physics at the University of Texas, Austin.
His research interests are in nonlinear dynamical systems, mathematical
biology, and high-performance computing, including data assimilation
for geophysical flows. Professor Kostelich was one of the principal
investigators in the Mathematics and Climate Research Network,
supported by the National Science Foundation. He has directed
undergraduate research program in computational mathematics at ASU
since 2008. He is a member of the Society for Industrial and Applied
Mathematics, the American Mathematical Society, and the American
Meteorological Society.