Imperial College London
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DrRossellaArcucci

Faculty of Engineering, Department of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Arcucci:2018:10.26599/BDMA.2018.9020025,
author = {Arcucci, R and Pain, C and Guo, Y-K},
doi = {10.26599/BDMA.2018.9020025},
journal = {Big Data Mining and Analytics},
pages = {297--307},
title = {Effective variational data assimilation in air-pollution prediction},
url = {http://dx.doi.org/10.26599/BDMA.2018.9020025},
volume = {1},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Arcucci,R
AU  - Pain,C
AU  - Guo,Y-K
DO  - 10.26599/BDMA.2018.9020025
EP  - 307
PY  - 2018///
SN  - 2096-0654
SP  - 297
TI  - Effective variational data assimilation in air-pollution prediction
T2  - Big Data Mining and Analytics
UR  - http://dx.doi.org/10.26599/BDMA.2018.9020025
VL  - 1
ER  -
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