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Multi-resolution Hybrid Data Assimilation Core on a Cubed-sphere Grid (HybDA)

  • Hyo-Jong Song
  • Ji-Hyun Ha
  • In-Hyuk Kwon
  • Junghan Kim
  • Jihye Kwun
Article

Abstract

This study illustrates the characteristics of the data assimilation system at the Korea Institute of Atmospheric Prediction Systems (KIAPS), based on the cubed-sphere grid system. The most interesting feature is the use of spherical harmonic functions defined on cubed-sphere grid points, which makes it possible to control the allowable physical wavenumber for the analysis increments. The relevant computational costs and parallel scalability are represented. The multiple-resolution approach is a distinguishable aspect of this data assimilation system. The wavenumber, up to which the analysis is conducted, increases as the outer iteration progresses. This multiresolution strategy is based on an investigation into the change of spectral components of analysis increments. The multi-resolution outer-loop provides cost-effective analysis-improvement, by explicitly controlling the analysis increments entered into the observation operator. To utilize the high-resolution deterministic forecast as a background state, it is subtracted from the forecast ensemble, to produce ensemble forecast perturbation that is hybridized with static background error covariance. Based on the cycled analysis experiments, the higher-resolution deterministic forecast is shown to preserve the high-frequency feature of the analysis increment relative to the ensemble mean forecast.

Key words

Cubed sphere hybrid data assimilation parallel scalability multiple resolutions Korean Integrated Model 

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Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2018

Authors and Affiliations

  • Hyo-Jong Song
    • 1
    • 3
  • Ji-Hyun Ha
    • 1
  • In-Hyuk Kwon
    • 1
  • Junghan Kim
    • 1
  • Jihye Kwun
    • 2
  1. 1.Korea Institute of Atmospheric Prediction Systems (KIAPS)SeoulKorea
  2. 2.Cray Korea, Inc.SeoulKorea
  3. 3.Korea Institute of Atmospheric Prediction SystemsSeoulKorea

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