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Open Access

Effective Variational Data Assimilation in Air-Pollution Prediction

Data Science Institute, Department of Computing, Imperial College London, London, SW7 2AZ, United Kingdom.
Department of Earth Science & Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
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Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks, and entire cities. To improve prediction for air flows and pollution transport, we propose a Variational Data Assimilation (VarDA) model which assimilates data from sensors into the open-source, finite-element, fluid dynamics model Fluidity. VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information, forecast and observations, have errors that are adequately described by error covariance matrices. The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned. In this paper, a preconditioned VarDA model is presented, it is based on a reduced background error covariance matrix. The Empirical Orthogonal Functions (EOFs) method is used to alleviate the computational cost and reduce the space dimension. Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.


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Big Data Mining and Analytics
Pages 297-307
Cite this article:
Arcucci R, Pain C, Guo Y-K. Effective Variational Data Assimilation in Air-Pollution Prediction. Big Data Mining and Analytics, 2018, 1(4): 297-307.








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Received: 16 March 2018
Accepted: 20 March 2018
Published: 02 July 2018
© The author(s) 2018