Asia-Pacific Journal of Atmospheric Sciences

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Development of an Observation Processing Package for Data Assimilation in KIAPS

  • Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Chun, Hyoung-Wook (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Lee, Sihye (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Ha, Ji-Hyun (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Song, Hyo-Jong (Korea Institute of Atmospheric Prediction Systems (KIAPS)) ;
  • Kwon, In-Hyuk (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)) ;
  • Kim, Tae-Hun (Korea Institute of Atmospheric Prediction Systems (KIAPS))
  • Received : 2018.02.28
  • Accepted : 2018.06.22
  • Published : 2018.06.30
⟨ Previous Next ⟩

Abstract

A new observation processing system, the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), has been developed to provide optimal observation datasets to the data assimilation (DA) system for the Korean Integrated Model, KIM. This paper presents the KPOP's conceptual design, how the principal modules have been developed, and some of their preliminary results. Currently, the KPOP is capable of processing almost all observation types used by the Korea Meteorological Administration (KMA) and some new observation types that have a positive impact in other operational centers. We have developed an adaptive bias correction (BC) method that only uses the background of the analysis time and selects the best observations through the consecutive iteration of BC and quality control (QC); it has been verified that this method will be the best suited for the KIAPS DA system until the development of variational BC (VarBC) has been completed. The requirement of considering the radiosonde balloon drift in the DA according to the increase of spatial resolution of the NWP model was accounted for using a balloon drift estimation method that considers the pressure difference and wind speed; thus the distance error was less than 1% in the sample test. Some kind of widely used methods were tested for height adjustment of the SURFACE observation, and a new method for temperature adjustment was outlined that used the correlation between temperature and relative humidity. In addition, three types of map projection were compared: the cubed-sphere (CS), equidistance (ED), and equirectangular (ER) projection for thinning. Data denial experiments were conducted to investigate how the KPOP affected the quality of the analysis fields in the threedimensional variational data assimilation system (3D-Var). Qualified observations produced by the KPOP had a positive impact by reducing the analysis error.

Keywords

References

  1. Auligne, T., and A. P. McNally, 2007: Interaction between bias correction and quality control. Quart. J. Roy. Meteor. Soc. 133, 643-653. https://doi.org/10.1002/qj.57
  2. Auligne, T., and A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc. 133, 631-642. https://doi.org/10.1002/qj.56
  3. Baker, N. L., 1992: Quality control for the navy operational atmospheric database. Wea. Forecasting, 7, 250-261. https://doi.org/10.1175/1520-0434(1992)007<0250:QCFTNO>2.0.CO;2
  4. Bedard, J., S. Laroche, and P. Gauthier, 2015: A geo-statistical observation operator for the assimilation of near-surface wind data. Quart. J. Roy. Meteor. Soc., 141, 2857-2868, doi:10.1002/qj.2569.
  5. Bormann, N., K. Salonen, C. Peubey, T. McNally, and C. Lupu, 2012: An overview of the status of the operational assimilation of AMVs at ECMWF. Proc. the 11th International Wind Workshop, Auckland, New Zealand, ECMWF, 20-24.
  6. Cameron, J., and W. Bell, 2016: The testing and planned implementation of variational bias correction (VarBC) at the Met Office. Proc. the 20th International TOVS Study Conferences, Wisconsin, USA, ITWG, 139-140.
  7. Choi, S.-J., and S.-Y. Hong, 2016: A global non-hydrostatic dynamic 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.
  8. 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.
  9. 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, doi:10.5194/amt-8-1887-2015.
  10. Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc. 131, 3323-3343. https://doi.org/10.1256/qj.05.137
  11. 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:10.1177/1094-342005056108.
  12. Derber, J. C., D. F. Parrish, and S. J. Lord, 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6, 538-547. https://doi.org/10.1175/1520-0434(1991)006<0538:TNGOAS>2.0.CO;2
  13. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev. 126, 2287-2299. https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2
  14. Eyre, J. R., 1992: A bias correction scheme for simulated TOVS brightness temperatures. ECMWF Tech. Memo. No. 186, 35 pp.
  15. Geer, A. J., and Coauthors, 2018: Allsky satellite data assimilation at operational weather forecasting centres (in press). Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.3202.
  16. Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc. 127, 1453-1468. https://doi.org/10.1002/qj.49712757418
  17. Hollingsworth, A., D. B. Shaw, P. Lonnberg, L. Illari, K. Arpe, and A. J. Simmons, 1986: Monitoring of Observation and Analysis Quality by a Data Assimilation System. Mon. Wea. Rev., 114, 861-879. https://doi.org/10.1175/1520-0493(1986)114<0861:MOOAAQ>2.0.CO;2
  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.
  19. Howard, T. and P. Clark, 2003: Improvement to the Nimrod wind nowcasting scheme over high ground. Forecasting Research Technical Report No. 406, 31 pp.
  20. Jarvinen, H., and P. Unden, 1997: Observation screening and background quality control in the ECMWF 3DVar data assimilation system. ECMWF Tech. Memo. No. 236, 33 pp.
  21. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge Univ. Press, 341 pp.
  22. Kang, J.-H., H.-J. Song, J.-H. Ha, and H.-J. Han, 2016: A study on the relationship among surface variables to adjust the height of surface temperature for data assimilation. Proc. the American Geophysical Union Fall meeting 2016, San Francisco, USA,. AGU.
  23. Kelly, G., J. N. Thepaut, R. Buizza, and C. Cardinali, 2007: The value of observations. I: Data denial experiments for the Atlantic and the Pacific. Quart. J. Roy. Meteor. Soc. 133, 1803-1815. https://doi.org/10.1002/qj.150
  24. Kwon, H. J.-S. Kang, Y. Jo, and J.-H. Kang, 2015: Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system. Atmos. Meas. Tech. 8, 1259-1273, doi:10.5194/amt-8-1259-2015.
  25. Laroche, S., and R. Sarrazin, 2013: Impact of radiosonde balloon drift on numerical weather prediction and verification. Wea. Forecasting, 28, 772-782, doi:10.1175/WAF-D-12-00114.1.
  26. Lee, S., S. Kim, H.-W. Chun, J.-H. Kim, and J.-H. Kang, 2014: Preprocessing and bias correction for AMSUA radiance data based on statistical methods. Atmosphere, 24, 491-502, doi:10.14191/Atmos.2014.24.4.491.
  27. Lorenc, A. C., and O. Hammon, 1988: Objective quality control of observations using Bayesian methods. Theory, and a practical implementation. Quart. J. Roy. Meteor. Soc., 114, 515-543. https://doi.org/10.1002/qj.49711448012
  28. McNally, A. P., and P. D. Watts, 2003: A cloud detection algorithm for high-spectral-resolution infrared sounders. Quart. J. Roy. Meteor. Soc., 129, 3411-3423. https://doi.org/10.1256/qj.02.208
  29. Ochotta, T., C. Gebhardt, D. Saupe, and W. Wergen, 2005: Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods. Quart. J. Roy. Meteor. Soc. 131, 3427-3437. https://doi.org/10.1256/qj.05.94
  30. Rabier, F., 2011: Pre- and Post-Processing in Data Assimilation. Preprints, Seminar on Data assimilation for atmosphere and ocean, Reading, UK, ECMWF, 45-59.
  31. Saunders, R. W., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiance observations. Quart. J. Roy. Meteor. Soc. 125, 1407-1425. https://doi.org/10.1002/qj.1999.49712555615
  32. Seidel, D. J., B. Sun, M. Pettey, and A. Reale, 2011: Global radiosonde balloon drift statistics. J. Geophys. Res., 116, D07102, doi:10.1029/2010JD014891.
  33. 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.
  34. 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.
  35. 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.
  36. Song, H.-J., S. Shin, J.-H. Ha, and S. Lim S, 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.
  37. Song, H.-J., J.-H. Ha, I.-H. Kwon, J. Kim, and J. Kwun, 2018: Multiresolution 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.
  38. Tenenbaum, J., 1996: Jet Stream Winds: Comparisons of aircraft observations with analysis. Wea. Forecasting, 11, 188-197. https://doi.org/10.1175/1520-0434(1996)011<0188:JSWCOA>2.0.CO;2
  39. Verspeek, J., A. Stoffelen, M. Portabella, H. Bonekamp, C. Anderson, and J. Figa-Saldana, 2010: Validation and calibration of ASCAT using CMOD5.n. IEEE Trans. Geosci. Remote Sens., 48, 386-395, doi:10.1109/TGRS.2009.2027896.
  40. Warrick, F., 2015: Options for filling the LEO-GEO AMV Coverage Gap. NWP SAF Tech. Doc., NWP SAF-MO-TR-030, 21 pp.

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. Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-018-0022-2
  3. The Korean Integrated Model (KIM) System for Global Weather Forecasting vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-018-0028-9
  4. Development of an Operational Hybrid Data Assimilation System at KIAPS vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-018-0029-8
  5. 앙상블 자료동화 시스템에서 ASCAT 해상풍 자료동화가 분석장에 미치는 효과 분석 vol.28, pp.3, 2018, https://doi.org/10.14191/atmos.2018.28.3.263
  6. 항공기 온도 관측 자료의 편향 보정 Part I: 존데와 비교를 통한 온도 편향 특성 분석 vol.28, pp.4, 2018, https://doi.org/10.14191/atmos.2018.28.4.357
  7. 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
  8. Extended representation of wind-mass correlation by ensemble forecasting for data assimilation vol.145, pp.722, 2018, https://doi.org/10.1002/qj.3541
  9. Effects of Modified Surface Roughness Length over Shallow Waters in a Regional Model Simulation vol.10, pp.12, 2018, https://doi.org/10.3390/atmos10120818
  10. Evaluating the effects and outcome of technological innovation on a web-based e-learning system vol.7, pp.1, 2018, https://doi.org/10.1080/2331186x.2020.1836729
  11. 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
  12. 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
  13. 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
  14. 한국형모델의 항공기 관측 온도의 정적 편차 보정 연구 vol.30, pp.4, 2018, https://doi.org/10.14191/atmos.2020.30.4.319
  15. 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
  16. Suppressing Grid-Point Storms in a Numerical Forecasting Model vol.12, pp.9, 2018, https://doi.org/10.3390/atmos12091194