Data Assimilation Meetings at Reading

Date Meeting type Speakers
8 October 2014
Maths 314
Invited speaker Rossella Arcucci (Imperial College)
A Scalable Approach for Variational Data Assimilation (pdf)
Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large and computationally expensive problem. Here we focus on scalable methods that makes DA applications feasible for a huge number of background data and observations.We present a scalable algorithm for solving variational DA which is highly parallel. We provide a mathematical formalization of this approach and we also present the performance of the resulted algorithm.

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