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- Publisher Website: 10.1007/s11075-018-0502-6
- Scopus: eid_2-s2.0-85046025538
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Article: Sparse matrix computation for air quality forecast data assimilation
Title | Sparse matrix computation for air quality forecast data assimilation |
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Authors | |
Keywords | Matrix computation Air quality prediction Block matrix Data assimilation Ensemble Kalman filter |
Issue Date | 2019 |
Citation | Numerical Algorithms, 2019, v. 80, n. 3, p. 687-707 How to Cite? |
Abstract | © 2018, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we study the ensemble Kalman filter (EnKF) method for chemical species simulation in air quality forecast data assimilation. The main contribution of this paper is that we study the sparse observation data and make use of the matrix structure of the EnKF update equations to design an algorithm for the purpose of computing the analysis of chemical species in an air quality forecast system efficiently. The proposed method can also handle the combined observations from multiple chemical species together. We applied the proposed method and tested its performance in real air quality data assimilation. Numerical examples are presented to demonstrate the efficiency of the proposed computation method for EnKF updating and the effectiveness of the proposed method for NO 2 , NO, CO, SO 2 , O 3 , PM2.5, and PM10 prediction in air quality forecast data assimilation. |
Persistent Identifier | http://hdl.handle.net/10722/276588 |
ISSN | 2023 Impact Factor: 1.7 2023 SCImago Journal Rankings: 0.829 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Zhu, Zhaochen | - |
dc.date.accessioned | 2019-09-18T08:34:04Z | - |
dc.date.available | 2019-09-18T08:34:04Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Numerical Algorithms, 2019, v. 80, n. 3, p. 687-707 | - |
dc.identifier.issn | 1017-1398 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276588 | - |
dc.description.abstract | © 2018, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we study the ensemble Kalman filter (EnKF) method for chemical species simulation in air quality forecast data assimilation. The main contribution of this paper is that we study the sparse observation data and make use of the matrix structure of the EnKF update equations to design an algorithm for the purpose of computing the analysis of chemical species in an air quality forecast system efficiently. The proposed method can also handle the combined observations from multiple chemical species together. We applied the proposed method and tested its performance in real air quality data assimilation. Numerical examples are presented to demonstrate the efficiency of the proposed computation method for EnKF updating and the effectiveness of the proposed method for NO 2 , NO, CO, SO 2 , O 3 , PM2.5, and PM10 prediction in air quality forecast data assimilation. | - |
dc.language | eng | - |
dc.relation.ispartof | Numerical Algorithms | - |
dc.subject | Matrix computation | - |
dc.subject | Air quality prediction | - |
dc.subject | Block matrix | - |
dc.subject | Data assimilation | - |
dc.subject | Ensemble Kalman filter | - |
dc.title | Sparse matrix computation for air quality forecast data assimilation | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11075-018-0502-6 | - |
dc.identifier.scopus | eid_2-s2.0-85046025538 | - |
dc.identifier.volume | 80 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 687 | - |
dc.identifier.epage | 707 | - |
dc.identifier.eissn | 1572-9265 | - |
dc.identifier.isi | WOS:000461382900001 | - |
dc.identifier.issnl | 1017-1398 | - |