Imperial College London > Talks@ee.imperial > CAS Talks > A Scalable approach for Data Assimilation methods

A Scalable approach for Data Assimilation methods

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Data Assimilation (DA) is a mathematical method for combining experimental data (in vivo, i.e. observed data) to numerical data (in vitro, i.e. background knowledge or forecasted data) in order to improve knowledge of complex systems or improve the estimate of system states. DA methods has long been playing a crucial role in numerical weather prediction and oceanography and more in general, in climate science; recently, DA is also applied to numerical simulations of geophysical applications, medicine and biological science for improving the reliability of the numerical simulations. 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. The parallel processing is necessary for the numerical solution of these problems, but often it is not sufficient. These large-scale problems are computationally difficult and their solution requires designing of scalable approaches. Many factors contribute to scalability, including the architecture of the parallel computer and the parallel implementation of the algorithm. However, one important issue is the scalability of the algorithm itself. Here I focus on scalable methods that makes DA applications feasible for a huge number of background data and observations. I present a scalable algorithm for solving variational DA which is highly parallel.

This talk is part of the CAS Talks series.

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