Midwest Mathematics and Climate Conference - Day 2 Morning Session
12 May 2015 |
Juan Restrepo, Oregon State UniversityData AssimilationAccounting for uncertainties has led us to alter our expectations of what is predictable and how such predictions compare to nature. A significant effort, in recent years, has been placed on creating new uncertainty quantification techniques,...
Midwest Mathematics and Climate Conference - Day 1 Afternoon Session
12 May 2015 |
Graham Feingold, National Oceanic & Atmospheric AdministratonDynamical System Analogues to Cloud SystemsShallow convection exhibits fascinating cellular structures at scales of a few to several hundred kilometers. Configurations of relatively cloud free open cell states, or much cloudier...
Complex Energy Systems
07 May 2015 | | Contributor(s):: Michael Chertkov
This is the opening guest lecture for the Electric Grid Focus Group. Dr. Michael Chertkov of Los Alamos National Lab describes the statistical and mathematical problems arising in complex energy systems, including the electric grid and gas networks. Systems with high penetration of...
Particle filters for geophysical applications
30 Apr 2015 | | Contributor(s):: Javier Amezcua
Avoiding degeneracy is a crucial challenge for particle filters. Results have shown that the number of particles scales exponentially with respect to the number of independent observations. In this talk I will review attempts to counteract this phenomenon by exploiting proposal densities. I will...
Emulators in climate science. Uncertainty, sensitivity, calibration and more
30 Apr 2015 | | Contributor(s):: Peter Challenor
An emulator or a surrogate is a statistical approximation of a complex numerical model. Emulators are fast to run and include a measure of their own uncertainty. This makes them suitable for a number of applications in climate science. Emulators were originally devised for uncertainty...
Correlated Observation Errors in Data Assimilation
30 Apr 2015 | | Contributor(s):: Sarah Dance
Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis...