Skip to main content
SpringerLink
Log in

Development of an Operational Hybrid Data Assimilation System at KIAPS

Asia-Pacific Journal of Atmospheric Sciences Aims and scope Submit manuscript

Abstract

This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.

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

Access this article

Log in via an institution

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

References

  • Amezcua, J., and P. J. Van Leeuwen, 2014: Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach. Tellus A, 66, 23493, doi:10.3402/tellusa.v66.23493.

    Article  Google Scholar 

  • Bishop, C. H., 2016: The GIGG-EnKF: ensemble Kalman filtering for highly skewed non-negative uncertainty distributions. Quart. J. Roy. Meteor. Soc., 142, 1395-1412, doi:10.1002/qj.2742.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bormann, N., A. Geer, and T. Wilhelmsson, 2011: Operational implementation of RTTOV-10 in the IFS. ECMWF Tech. Memo. 650, Reading, UK, ECMWF, 23 pp.

    Google Scholar 

  • Bowler, N. E., and Coauthors, 2017: Inflation and localization tests in the development of an ensemble of 4D-ensemble variational assimilations. Quart. J. Roy. Meteor. Soc., 143, 1280-1302, doi:10.1002/qj.3004.

    Article  Google Scholar 

  • Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131,1013-1043.

    Article  Google Scholar 

  • Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlin. Processes Geophys., 20, 669-682, doi:10.5194/npg-20-669-2013.

    Article  Google Scholar 

  • Buehner, M., and Coauthors, 2015: Implementation of deterministic weather forecasting systems based on ensemble-variational data assimilation at Environment Canada. Part I: The global system. Mon. Wea. Rev., 143, 2532-2559, doi:10.1175/MWR-D-14-00354.1.

    Google Scholar 

  • Choi, S.-J., F. X. Giraldo, J. Kim, and S. Shin, 2014: Verification of a nonhydrostatic dynamical core using the horizontal spectral element method and vertical finite difference method. Geosci. Model Dev., 7, 2717-2731, doi:10.5194/gmd-7-2717-2014.

    Article  Google Scholar 

  • Clayton, A. M., A. C. Lorenc, and D. M. Barker, 2013: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Quart. J. Roy. Meteor. Soc., 139, 1445-1461, doi:10.1002/qj.2054.

    Article  Google Scholar 

  • Culverwell, I. D., H. W. Lewis, D. Offiler, C. Marquardt, and C. P. Burrows, 2015: The Radio Occultation Processing Package, ROPP. Atmos. Meas. Tech., 8, 1887-1899.

    Article  Google Scholar 

  • Dee, D. P. and A. M. Da Silva, 2003: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131, 155-171.

    Article  Google Scholar 

  • Derber, J., and F. Bouttier, 1999: A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus A, 51, 195-221, doi:10.3402/tellusa.v51i2.12316.

    Article  Google Scholar 

  • Fletcher, S. J., 2010: Mixed Gaussian-lognormal four-dimensional data assimilation. Tellus, 62, 266-287, doi:10.1111/j.1600-0870.2009.00439.x.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Heming, J. T., 2016: Met Office Unified Model tropical cyclone performance following major changes to the initialization scheme and model upgrade. Wea. Forecasting, 31, 1433-1449, doi:10.1175/WAF-D-16-0040.1.

    Article  Google Scholar 

  • 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 

  • Holton, J. R., 2004: An Introduction to Dynamic Meteorology. Elsevier, 535 pp.

    Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Kang, J.-H., H.-W. Chun, S. Lee, H.-J. Song, J.-H. Ha, I.-H. Kwon, H.-J. Han, H. Jeong, and H.-N. Kwon, 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.

  • Kleist, D. T., 2011: Assimilation of tropical cyclone advisory minimum sea level pressure in the NCEP Global Data Assimilation System. Wea. Forecasting, 26, 1085-1091, doi:10.1175/WAF-D-11-00045.1.

    Article  Google Scholar 

  • Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variationalensemble 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.

    Google Scholar 

  • Kwon, I.-H., S. English, W. Bell, R. Potthast, A. Collard, and B. Ruston, 2018: Assessment of progress and status of data assimilation in Numerical Weather Prediction. Bull. Amer. Meteor. Soc., 99, ES75-ES79, doi:10.1175/BAMS-D-17-0266.1.

    Article  Google Scholar 

  • Lazzara, M. A., R. Dworak, D. A. Santek, B. T. Hoover, C. S. Velden, and J. R. Key, 2014: High-latitude atmospheric motion vectors from composite satellite data. J. Appl. Meteor. Climatol., 53, 534-547, doi:10.1175/JAMC-D-13-0160.1.

    Article  Google Scholar 

  • Lee, M.-S., and D. M. Barker, 2005: Preliminary tests of first guess at appropriate time (FGAT) with WRF 3DVAR and WRF model. J. Korean Meteor. Soc., 41, 495-505.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Marshall, A. G., and A. A. Scaife, 2009: Impact of the QBO on surface winter climate. J. Geophys. Res., 114, D18110, doi:10.1029/2009-JD011737.

    Article  Google Scholar 

  • Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519-1535, doi:10.1175/2010MWR3570.1.

    Article  Google Scholar 

  • Parlett, B. N., 1980: The symmetric eigenvalue problem, Prentice-Hall, 368 pp.

    Google Scholar 

  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763

    Article  Google Scholar 

  • Penny, S. G., and T. M. Hamill, 2017: Coupled data assimilation for integrated earth system analysis and prediction. Bull. Amer. Meteor. Soc., 98, ES169-ES172.

    Article  Google Scholar 

  • Polavarapu, S., and M. Pulido, 2015: Stratospheric and mesospheric data assimilation: The role of middle atmospheric dynamics. In Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), Springer, 429-454.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shin, S., 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.

  • 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.

    Article  Google Scholar 

  • Song, H.-J., G.-H. Lim, D.-I. LEE, and H.-S. Lee, 2009: Comparison of retrospective optimal interpolation with four-dimensional variational assimilation. Tellus A, 61, 428-437.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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, 12226-12244, doi:10.1002/2017JD027598.

    Google Scholar 

  • Song, H.-J., J.-H. Ha, I.-H. Kwon, J. Kim, and J. Kwun, 2018: Multi-resolution hybrid data assimilation core on a cubed-sphere grid (HybDA) (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0018-y.

  • Tripathi, O. P., and Coauthors, 2014: The predictability of the extratropical stratosphere on monthly time-scales and its impact on the skill of tropospheric forecasts. Quart. J. Roy. Meteor. Soc., 141, 987-1003, doi:10.1002/qj.2432.

    Article  Google Scholar 

  • Wu, W-S, 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, Korea

    In-Hyuk Kwon, Hyo-Jong Song, Ji-Hyun Ha, Hyoung-Wook Chun, Jeon-Ho Kang, Sihye Lee, Sujeong Lim, Youngsoon Jo, Hyun-Jun Han, Hanbyeol Jeong, Hui-Nae Kwon, Seoleun Shin & Tae-Hun Kim

  2. Korea Institute of Atmospheric Prediction Systems, 4F Park Square, 35 Boramaero 5-gil, Dongjak-gu, Seoul, 07071, Korea

    Hyo-Jong Song

Authors
  1. In-Hyuk Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyo-Jong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ji-Hyun Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyoung-Wook Chun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeon-Ho Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sihye Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sujeong Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Youngsoon Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hyun-Jun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hanbyeol Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hui-Nae Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Seoleun Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tae-Hun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyo-Jong Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwon, IH., Song, HJ., Ha, JH. et al. Development of an Operational Hybrid Data Assimilation System at KIAPS. Asia-Pacific J Atmos Sci 54 (Suppl 1), 319–335 (2018). https://doi.org/10.1007/s13143-018-0029-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13143-018-0029-8

Key words

search

Navigation