Uncertainty quantification of large models: parameters of weather and climate models

Authors

Heikki Haario, Lappeenranta University of Technology, Finland

Document Type

Presentation Abstract

Presentation Date

9-24-2012

Abstract

A statistical analysis of the uncertainties of modeling, with the nonlinear models arising in engineering and science especially in mind, is becoming routine due to computational methods such as Markov chain Monte Carlo (MCMC) or Bootstrap. However, 'large' models, either in terms of CPU times or the dimension of the state, are still a challenge. We present some recent ideas how to minimize the CPU times of the sampling methods as well as to achieve low memory approximations for high dimensional problems. The primary targets here are weather prediction and climate models, studied in collaboration with FMI (Finnish Meteorological Institute) and ECMWF (European Centre for Medium-Range Weather Forecasts, UK).

Additional Details

Monday, 24 September 2012
3:10 p.m. in Math 103
4:00 p.m. Refreshments in Math Lounge 109

Recommended Citation

Haario, Heikki, "Uncertainty quantification of large models: parameters of weather and climate models" (2012). Colloquia of the Department of Mathematical Sciences. 407.
https://scholarworks.umt.edu/mathcolloquia/407