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 Events
UBC Mathematics
Mon 2 Mar 2015, 3:00pm
Institute of Applied Mathematics
LSK 460
Stochastic domain decomposition for parallel grid generation
LSK 460
Mon 2 Mar 2015, 3:00pm-4:00pm

Abstract

In this talk a method for the parallel generation of adaptive meshes using stochastic domain decomposition is presented. The method rests on numerically evaluating the stochastic representation of the exact solution of a linear elliptic or linear parabolic mesh generator for generating the mesh at the interfaces of the sub-domains. Unlike traditional domain decomposition, this method hence does not require iteration on the sub-domains or optimization of the transmission conditions to generate adaptive meshes over the entire domain. We show the generation of adaptive meshes for prescribed mesh density functions and study the scaling properties of the algorithm. A few physical examples for the parallel generation of adaptive meshes for Burgers equation and the shallow-water equations are presented. This is joint work with Ronald Haynes and Emily Walsh.
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Mathematics, University of Bath
Fri 6 Mar 2015, 4:00pm SPECIAL
Institute of Applied Mathematics
Canfor Policy Rm 1600, SFU Harbour Centre, Downtown Vancouver
Data Assimilation and Adaptivity
Canfor Policy Rm 1600, SFU Harbour Centre, Downtown Vancouver
Fri 6 Mar 2015, 4:00pm-5:00pm

Abstract

Data assimilation is the process of systematically including (often noisy) data into a forecast. It is now widely used in numerical weather prediction and its positive impact on the accuracy of weather forecasts is unquestionable. Indeed improvements in our ability to forecast the weather over the last decade are a reflection on the increasing volume of data available, improved computational methods and (significantly) much improved algorithms for incorporating this data into the forecast. However, many problems remain, including dealing with the sheer volume of the data and the inherent complexity of the weather and climate, understanding the effects of data and model error, and of reducing spurious correlations between the data and the forecast.

In this talk I will give a survey of various techniques that are used operationally to implement data assimilation procedures in weather (and climate) forecasting including the Ensemble Kalman Filter, and the 4D-Var method.

I will discuss their various advantages and disadvantages, the nature of the errors and ways to minimise these. In particular I will show that the use of adaptive numerical methods can significantly improve the performance

of the 4D-Var method. Hopefully I will show that used carefully Data Assimilation techniques can significantly improve our ability to forecast the weather of Planet Earth.

Joint work with Mike Cullen and Chiara Piccolo at the Met Office.

Note for Attendees

Note SFU downtown venue. Reception at 3:30 pm (light refreshments).
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