DOI QR코드

DOI QR Code

Development of an Operational Hybrid Data Assimilation System at KIAPS

  • Kwon, In-Hyuk (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Song, Hyo-Jong (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Ha, Ji-Hyun (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Chun, Hyoung-Wook (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Lee, Sihye (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Lim, Sujeong (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Jo, Youngsoon (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Han, Hyun-Jun (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Jeong, Hanbyeol (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kwon, Hui-Nae (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Shin, Seoleun (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kim, Tae-Hun (Korea Institute of Atmospheric Prediction Systems (KIAPS))
  • Received : 2018.02.28
  • Accepted : 2018.06.21
  • Published : 2018.06.30

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.

Keywords

References

  1. 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.
  2. 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.
  3. Bonavita, M., E. Holm, 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.
  4. 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.
  5. 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.
  6. 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. https://doi.org/10.1256/qj.04.15
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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. https://doi.org/10.5194/amt-8-1887-2015
  12. Dee, D. P. and A. M. Da Silva, 2003: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131, 155-171. https://doi.org/10.1175/1520-0493(2003)131<0155:TCOVFA>2.0.CO;2
  13. 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.
  14. Fletcher, S. J., 2010: Mixed Gaussian-lognormal four-dimensional data assimilation. Tellus, 62, 266-287, doi:10.1111/j.1600-0870.2009.00439.x.
  15. 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.
  16. 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.
  17. Hocking, J., P. Rayer, R. Saunders, M. Matricardi, A. Geer, and P. Brunel, 2012: RTTOV v10 Users Guide. NWPSAF-MO-UD-023, 92 pp.
  18. Holton, J. R., 2004: An Introduction to Dynamic Meteorology. Elsevier, 535 pp.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. Parlett, B. N., 1980: The symmetric eigenvalue problem, Prentice-Hall, 368 pp.
  32. Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763 https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2
  33. 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. https://doi.org/10.1175/BAMS-D-17-0036.1
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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. https://doi.org/10.1111/j.1600-0870.2009.00396.x
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.

Cited by

  1. Multi-resolution Hybrid Data Assimilation Core on a Cubed-sphere Grid (HybDA) vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-018-0018-y
  2. The Korean Integrated Model (KIM) System for Global Weather Forecasting vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-018-0028-9
  3. Effects of the wind-mass balance constraint on ensemble forecasts in the hybrid‐4DEnVar vol.145, pp.719, 2018, https://doi.org/10.1002/qj.3440
  4. Extended representation of wind-mass correlation by ensemble forecasting for data assimilation vol.145, pp.722, 2018, https://doi.org/10.1002/qj.3541
  5. Existence of multiple scales in uncertainty of numerical weather prediction vol.9, pp.1, 2018, https://doi.org/10.1038/s41598-019-52157-x
  6. Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale vol.8, pp.None, 2018, https://doi.org/10.3389/feart.2020.00070
  7. A Tropical Cyclone Initialization in Multi-Scale Localization with Hybrid Four Dimensional Ensemble-Variational System: Preliminary Results vol.16, pp.None, 2018, https://doi.org/10.2151/sola.2020-025
  8. All‐sky microwave humidity sounder assimilation in the Korean Integrated Model forecast system vol.146, pp.732, 2018, https://doi.org/10.1002/qj.3862
  9. Model Error Representation Using the Stochastically Perturbed Hybrid Physical-Dynamical Tendencies in Ensemble Data Assimilation System vol.10, pp.24, 2018, https://doi.org/10.3390/app10249010
  10. Impact of Soil Moisture Data Assimilation on Analysis and Medium-Range Forecasts in an Operational Global Data Assimilation and Prediction System vol.12, pp.9, 2018, https://doi.org/10.3390/atmos12091089
  11. Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia vol.271, pp.None, 2018, https://doi.org/10.1016/j.atmosenv.2021.118921