Effective Variational Data Assimilation in Air-Pollution Prediction
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 (Var...
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| Language: | English |
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Tsinghua University Press
2018-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020025 |
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| author | Rossella Arcucci Christopher Pain Yi-Ke Guo |
| author_facet | Rossella Arcucci Christopher Pain Yi-Ke Guo |
| author_sort | Rossella Arcucci |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-189a9e78e1bf412981fb728d2da237fe |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-189a9e78e1bf412981fb728d2da237fe2025-02-02T23:47:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-12-011429730710.26599/BDMA.2018.9020025Effective Variational Data Assimilation in Air-Pollution PredictionRossella Arcucci0Christopher Pain1Yi-Ke Guo2<institution content-type="dept">Data Science Institute, Department of Computing</institution>, <institution>Imperial College London</institution>, <city>London</city>, <postal-code>SW7 2AZ</postal-code>, <country>United Kingdom</country>.<institution content-type="dept">Department of Earth Science & Engineering</institution>, <institution>Imperial College London</institution>, <city>London</city>, <postal-code>SW7 2AZ</postal-code>, <country>United Kingdom</country>.<institution content-type="dept">Data Science Institute, Department of Computing</institution>, <institution>Imperial College London</institution>, <city>London</city>, <postal-code>SW7 2AZ</postal-code>, <country>United Kingdom</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020025data assimilationreduced order spacebig datapreconditioning |
| spellingShingle | Rossella Arcucci Christopher Pain Yi-Ke Guo Effective Variational Data Assimilation in Air-Pollution Prediction Big Data Mining and Analytics data assimilation reduced order space big data preconditioning |
| title | Effective Variational Data Assimilation in Air-Pollution Prediction |
| title_full | Effective Variational Data Assimilation in Air-Pollution Prediction |
| title_fullStr | Effective Variational Data Assimilation in Air-Pollution Prediction |
| title_full_unstemmed | Effective Variational Data Assimilation in Air-Pollution Prediction |
| title_short | Effective Variational Data Assimilation in Air-Pollution Prediction |
| title_sort | effective variational data assimilation in air pollution prediction |
| topic | data assimilation reduced order space big data preconditioning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020025 |
| work_keys_str_mv | AT rossellaarcucci effectivevariationaldataassimilationinairpollutionprediction AT christopherpain effectivevariationaldataassimilationinairpollutionprediction AT yikeguo effectivevariationaldataassimilationinairpollutionprediction |