Advertisement

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
First Online:
Received:
Accepted:

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 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amdahl, G. M., 1967: Validity of the single processor approach to achieving large scale computing capabilities. Proc. the AFIPS 1967 Spring Joint Computer Conference. Vol. 30, New Jersey, USA, AFIPS, 483-485, doi:10.1145/1465482.1465560.Google Scholar
  2. Andersson, E., and J.-N. Thépaut, 2008: ECMWF's 4D-Var data assimilation system-the genesis and ten years in operation. ECMWF Newsletter, 115, 8-12.Google Scholar
  3. Bormann, N., A. Geer, and T. Wilhelmsson, 2011: Operational implementation of RTTOV-10 in the IFS. Tech. Memo. No. 650, 23 pp.Google Scholar
  4. Bonavita, M., E. Hólm, L. Isaksen, and M. Fisher, 2016: The evolution of the ECMWF hybrid data assimilation system. Quart. J. Roy. Meteor. Soc., 142, 287-303, doi:10.1002/qj.2652.CrossRefGoogle Scholar
  5. Charney, J., 1955: The use of the primitive equations of motion in numerical prediction. Tellus, 7, 22-26, doi:10.3402/tellusa.v7i1.8772.CrossRefGoogle Scholar
  6. Choi, S.-J., and S.-Y. Hong, 2016: A global non-hydrostatic dynamical core using the spectral element method on a cubed-sphere grid. Asia-Pac. J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.CrossRefGoogle Scholar
  7. Choi, S.-J., F. X. Giraldo, J. Kim, and S. Shin, 2014: Verification of a nonhydrostatic dynamical core using horizontally spectral element vertically finite difference method: 2-D aspects. Geosci. Model Dev., 7, 2717-2731, doi:10.5194/gmd-7-2717-2014.CrossRefGoogle Scholar
  8. Dee, D. P., and A. M. da Silva, 2003: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131, 155-171, doi:10. 1175/1520-0493(2003)131<0155:TCOVFA>2.0.CO;2.CrossRefGoogle Scholar
  9. Dennis, J., A. Fournier, W. F. Spotz, A. St-Cyr, M. A. Taylor, S. J. Thomas, and H. Tufo, 2005: High-resolution mesh convergence properties and parallel efficiency of a spectral element atmospheric dynamical core. Int. J. High Perform. Comput. Appl., 19, 225-235, doi:10.1177/1094342005056108.CrossRefGoogle Scholar
  10. Evans, K. J., P. H. Lauritzen, S. K. Mishra, R. B. Neale, M. A. Taylor, and J. J. Tribbia, 2013: AMIP simulation with the CAM4 spectral element dynamical core. J. Climate, 26, 689-709, doi: 10.1175/JCLI-D-11-00448.1.CrossRefGoogle Scholar
  11. Fisher, M., 2003: Background error covariance modelling. Proc. the Annual Seminar on Recent Development in Data Assimilation for Atmosphere and Ocean, 8-12 September 2003, Reading, UK, ECMWF, 45-63.Google Scholar
  12. Fisher, M., and S. Gürol, 2017: Parallelization in the time dimension of four-dimensional variational data assimilation. Quart. J. Roy. Meteor. Soc., 143, 1136-1147, doi:10.1002/qj.2997.CrossRefGoogle Scholar
  13. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions, Quart. J. Roy. Meteor. Soc., 125, 723-757. doi:10.1002/qj.49712555417.CrossRefGoogle Scholar
  14. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.CrossRefGoogle Scholar
  15. Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453-1468, doi:10.1002/qj.49712757418.CrossRefGoogle Scholar
  16. Hocking, J., P. Rayer, R. Saunders, M. Matricardi, A. Geer, and P. Brunel, 2012: RTTOV v10 Users Guide. NWPSAF-MO-UD-023, 92 pp.Google Scholar
  17. Holton, J. R., 2004: An Introduction to Dynamic Meteorology 4th Edition. Elsevier, 535 pp.Google Scholar
  18. Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0028-9.Google Scholar
  19. Hunt, B., E. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126, doi:10.1016/j.physd.2006.11.008.CrossRefGoogle Scholar
  20. Isaksen, L., M. Bonavita, R. Buizza, M. Fisher, J. Haseler, M. Leutbecher, and L. Raynaud, 2010: Ensemble of Data Assimilations at ECMWF, Tech. Memo. No. 636, 46 pp.Google Scholar
  21. Kang, J.-H., and Coauthors, 2018: Development of an observation processing package for data assimilation in KIAPS (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0030-2.Google Scholar
  22. Kim, J., Y. C. Kwon, and T.-H. Kim, 2018: A scalable high-performance I/O System for a numerical weather forecast model on the cubed-sphere grid (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0021-3.Google Scholar
  23. Kleist, D. T., and K. Ide, 2015a: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., 143, 452-470, doi: 10.1175/MWR-D-13-00350.1.CrossRefGoogle Scholar
  24. Kleist, D. T., and K. Ide, 2015b: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results. Mon. Wea. Rev., 143, 433-451, doi:10.1175/MWR-D-13-00351.1.CrossRefGoogle Scholar
  25. Kuhl, D. D., T. E. Rosmond, C. H. Bishop, J. McLay, and N. L. Baker, 2013: Comparison of hybrid ensemble/4DVar and 4DVar within the NAVDAS-AR data assimilation framework. Mon. Wea. Rev., 141, 2740-2758, doi:10.1175/MWR-D-12-00182.1.CrossRefGoogle Scholar
  26. Kwon, I.-H., and Coauthors, 2018: Development of operational hybrid data assimilation system at KIAPS (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0029-8.Google Scholar
  27. Lawless, A. S., and N. K. Nichols, 2006: Inner-loop stopping criteria for incremental four-dimensional variational data assimilation. Mon. Wea. Rev., 134, 3425-3435, doi:10.1175/MWR3242.1.CrossRefGoogle Scholar
  28. Lee, S., and H.-J. Song, 2017: Quantifying the inflation factors of observation error variance for COMS and MTSAT atmospheric motion vectors considering spatial observation error correlation. Quart. J. Roy. Meteor. Soc., 143, 2625-2635, doi: 10.1002/qj.3113.CrossRefGoogle Scholar
  29. Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP—a comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183-3203, doi:10.1256/qj.02.132.CrossRefGoogle Scholar
  30. Lorenc, A. C., N. E. Bowler, A. M. Clayton, S. R. Pring, and D. Fairbairn, 2015: Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP. Mon. Wea. Rev., 143, 212-229, doi:10.1175/MWR-D-14-00195.1.CrossRefGoogle Scholar
  31. Lynch, P., 2008: The origins of computer weather prediction and climate modeling. J. Comput. Phys. 227, 3431-3444, doi:10.1016/j.jcp.2007. 02.034.CrossRefGoogle Scholar
  32. Nair, R. D., S. J. Thomas, and R. D. Loft, 2005: A discontinuous Galerkin transport scheme on the cubed sphere. Mon. Wea. Rev. 133, 814-828, doi:10.1175/MWR2890.1.CrossRefGoogle Scholar
  33. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.Google Scholar
  34. Song, H-J, and I.-H. Kwon, 2015: Spectral transformation using a cubedsphere grid for a three-dimensional variational data assimilation system. Mon. Wea. Rev., 143, 2581-2599, doi:10.1175/MWR-D-14-00089.1.CrossRefGoogle Scholar
  35. Song, H-J, I.-H. Kwon, and J. Kim, 2017a: Characteristics of a spectral inverse of the Laplacian using spherical harmonic functions on a cubed-sphere grid for background error covariance modeling. Mon. Wea. Rev., 145, 307-322, doi:10.1175/MWR-D-16-0134.1.CrossRefGoogle Scholar
  36. Song, H-J, J. Kwun, I.-H. Kwon, J.-H. Ha, J.-H. Kang, S. Lee, H.-W. Chun, and S. Lim, 2017b: The impact of the nonlinear balance equation on a 3D-Var cycle during an Australian-winter month as compared with the regressed wind-mass balance. Quart. J. Roy. Meteor. Soc. 143, 2036-2049, doi:10.1002/qj.3036.CrossRefGoogle Scholar
  37. Song, H-J, S. Shin, J.-H. Ha, and S. Lim, 2017c: The advantages of hybrid 4DEnVar in the context of the forecast sensitivity to initial conditions. J. Geophys. Res. 122, 12,226-12,244, doi:10.1002/2017JD027598.Google Scholar
  38. Shin, S., J. S. Kang, and Y. Jo, 2016: The local ensemble transform Kalman filter (LETKF) with a global NWP model on the cubed sphere. Pure Appl. Geophys., 173, 2555-2570, doi:10.1007/s00024-016-1269-0.CrossRefGoogle Scholar
  39. Song, H-J, and Coauthors, 2018: Real data assimilation using the Local Ensemble Transform Kalman Filter (LETKF) system for a global nonhydrostatic NWP model on the cubed-sphere (in press). Asia-Pac. J. Atmos. Sci., 54, doi: 10.1007/s13143-018-0022-2.Google Scholar
  40. Wu, W., R. J. Purser, D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev. 130, 2905-2916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS> 2.0.CO;2.CrossRefGoogle Scholar
  41. Wu, W., D. F. Parrish, E. Rogers, and Y. Lin, 2017: Regional ensemblevariational data assimilation using global ensemble forecasts. Wea. Forecasting, 32, 83-96, doi:10.1175/WAF-D-16-0045.1.CrossRefGoogle Scholar

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

Personalised recommendations