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Advances in Earth Science >

2019 , Vol. 34 >Issue 5: 531 - 539

DOI: https://doi.org/10.11867/j.issn.1001-8166.2019.05.0531

Global Ocean Data Assimilation System Design and Algorithm Acceleration Based on Local Ensemble Transform Kalman Filter

  • Zheng Fan ,
  • Hong Li ,
  • Xiangwen Liu ,
  • Fanghua Xu
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  • 1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
    2. National Climate Center, China Meteorological Administration, Beijing 100081, China
Fan Zheng(1994-), male, Bengbu City, Anhui Province, Master student. Research areas include high performance computing in earth science. E-mail:fan-z16@mails.tsinghua.edu.cn

Received date: 2019-01-21

  Revised date: 2019-04-02

  Online published: 2019-07-04

Supported by

Project supported by the National Key R&D Program of China “Development and evaluation of seamless climate prediction system based on high resolution climate system model”(No. 2016YFA0602100)

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Abstract

An integrated analysis about computational time complexity of the Local Ensemble Transform Kalman Filter (LETKF) was performed. It is found that the calculation step of inverse matrix of the error covariance in ensemble space is the most computationally intensive and time consuming. In a parallel computing environment, the uneven distribution of CPU calculations in this step directly leads to low computational efficiency. To solve this problem, a new load balancing strategy was designed based on the "greedy algorithm". A high-performance parallel ocean data assimilation system based on the LETKF was developed and tested using this strategy. This system was based on the Parallel Ocean Program 2 (POP2) of the Community Earth System Model (CESM). The optimal interpolated sea surface temperature data (OISST) and Argo temperature profile data from January to February, 2004 were assimilated into the POP2. The results show that data assimilation effectively reduces the root mean square error of temperature and salinity. Using the new strategy, the exact same results are obtained but the computation time is reduced by half. At higher resolution (0.1°×0.1°),the computing performance is still doubled, indicating that this load balancing scheme is stable and reliable. In addition, the new method has high scalability and portability with great potential to be applied in operational forecasting.

Key words： Ocean data assimilation; Local Ensemble Transform Kalman filter; Load balancing strategy; High performance

Cite this article

Zheng Fan , Hong Li , Xiangwen Liu , Fanghua Xu . Global Ocean Data Assimilation System Design and Algorithm Acceleration Based on Local Ensemble Transform Kalman Filter[J]. Advances in Earth Science, 2019 , 34(5) : 531 -539 . DOI: 10.11867/j.issn.1001-8166.2019.05.0531

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