Sparse matrix computation for air quality forecast data assimilation

Kwok Po NG, Zhaochen Zhu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)687-707
Number of pages21
JournalNumerical Algorithms
Issue number3
Publication statusPublished - 5 Mar 2019

Scopus Subject Areas

  • Applied Mathematics

User-Defined Keywords

  • Air quality prediction
  • Block matrix
  • Data assimilation
  • Ensemble Kalman filter
  • Matrix computation


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