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Sparse matrix computation for air quality forecast data assimilation

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Abstract

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 NO2, NO, CO, SO2, O3, PM2.5, and PM10 prediction in air quality forecast data assimilation.

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Acknowledgments

Research supported in part by HKBU RC-ICRS/16-17/03-MATH and HKRGC GRF 12306616 and 12200317.

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Authors and Affiliations

  1. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Michael K. Ng & Zhaochen Zhu

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  1. Michael K. Ng
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  2. Zhaochen Zhu
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Correspondence to Zhaochen Zhu.

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Ng, M.K., Zhu, Z. Sparse matrix computation for air quality forecast data assimilation. Numer Algor 80, 687–707 (2019). https://doi.org/10.1007/s11075-018-0502-6

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  • DOI: https://doi.org/10.1007/s11075-018-0502-6

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