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Korean Meteorological Society (한국기상학회)
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A Study of Iterative QC-BC Method for AMSU-A in the KIAPS Data Assimilation System

KIAPS 자료동화 시스템에서 AMSU-A의 품질검사 및 편향보정 반복기법에 관한 연구

  • 정한별 ((재)한국형수치예보모델개발사업단) ;
  • 전형욱 ((재)한국형수치예보모델개발사업단) ;
  • 이시혜 ((재)한국형수치예보모델개발사업단)
  • Received : 2019.02.07
  • Accepted : 2019.07.02
  • Published : 2019.09.30
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Abstract

Bias correction (BC) and quality control (QC) are essential steps for the proper use of satellite observations in data assimilation (DA) system. BC should be calculated over quality controlled observation. And also QC should be performed for bias corrected observation. In the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), we adopted an adaptive BC method that calculates the BC coefficients with background at the analysis time rather than using static BC coefficients. In this study, we have developed an iterative QC-BC method for Advanced Microwave Sounding Unit-A (AMSU-A) to reduce the negative feedback from the interaction between BC and QC. The new iterative QC-BC is evaluated in the KIAPS 3-dimensional variational (3DVAR) DA cycle for January 2016. The iterative QC-BC method for AMSU-A shows globally significant benefits for error reduction of the temperature. The positive impacts for the temperature were predominant at latitudes of $30^{\circ}{\sim}90^{\circ}$ of both hemispheres. Moreover, the background warm bias across the troposphere is decreased. Even though AMSU-A is mainly designed for atmospheric temperature sounding, the improvement of AMSU-A pre-processing module has a positive impact on the wind component over latitudes of $30^{\circ}S$ near upper-troposphere, respectively. Consequently, the 3-day-forecast-accuracy is improved about 1% for temperature and zonal wind in the troposphere.

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References

  1. Andersson, E., and J.-N. Thepaut, 2008: ECMWF's 4D-Var data assimilation system - the genesis and ten years in operations. ECMWF Newsletter, 115, 8-12.
  2. Atkinson, N., J. Cameron, B. Candy, and S. English, 2005: Bias correction of satellite data at the Met Office. Presentation, ECMWF/NWP-SAF Workshop on bias estimation and correction in data assimilation, UK, ECMWF [Available online at https://www.ecmwf.int/node/15846].
  3. Auligne, T., and A. P. McNally, 2007: Interaction between bias correction and quality control. Q. J. R. Meteorol. Soc., 133, 643-653. https://doi.org/10.1002/qj.57
  4. Auligne, T.,A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Q. J. R. Meteorol. Soc., 133, 631-642. https://doi.org/10.1002/qj.56
  5. Baker, N. L., T. F. Hogan, W. F. Campbell, R. L. Pauley, and S. D. Swadley, 2005: The impact of AMSU-A radiance assimilation in the U.S. Navy's Operational Global Atmospheric Prediction System (NOGAPS). NRL Memorandum Report. NRL/MR/7530--05-8836, 22 pp.
  6. Bauer, P., R. Buizza, C. Cardinali, and J.-N. Thepaut, 2011: Impact of singular-vector-based satellite data thinning on NWP. Q. J. R. Meteorol. Soc., 137, 286-302. https://doi.org/10.1002/qj.733
  7. Cameron, J., and W. Bell, 2016: The testing and planned implementation of variational bias correction (VarBC) at the Met Office. Proceedings of the 20th International TOVS Study Conferences, Wisconsin, USA, 21 pp.
  8. Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Q. J. R. Meteorol. Soc., 135, 239-250. https://doi.org/10.1002/qj.366
  9. 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.
  10. Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. Proceedings of the ECMWF workshop on assimilation of high spectral resolution sounders in NWP, Reading, UK, ECMWF, 97-112.
  11. Dee, D. P., 2005: Bias and data assimilation. Q. J. R. Meteorol. Soc., 131, 3323-3343. https://doi.org/10.1256/qj.05.137
  12. Gelaro, R., R. H. Langland, S. Pellerin, and R. Todling, 2010: The THORPEX observation impact intercomparison experiment. Mon. Wea. Rev., 138, 4009-4025, doi:10.1175/2010MWR3393.1.
  13. Grody, N., F. Weng, and R. Ferraro, 1999: Application of AMSU for observation water vapor, cloud liquid water, precipitation, snow cover and sea ice concentration. Proceedings of the 10th International TOVS Study Conference, Boulder, CO, 230-240.
  14. Grody, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, 2001: Determination of precipitatable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit. J. Geophy. Res., 106, 2943-2953. https://doi.org/10.1029/2000JD900616
  15. Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Q. J. R. Meteorol. Soc., 127, 1453-1468. https://doi.org/10.1002/qj.49712757418
  16. Hilton, F., N. C. Atkinson, S. J. English, and J. R. Eyre, 2009: Assimilation of IASI at the Met Office and assessment of its impact through observing system experiments. Q. J. R. Meteorol. Soc., 135, 495-505. https://doi.org/10.1002/qj.379
  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. Asia-Pac. J. Atmos. Sci., 54, 267-292, doi:10.1007/s13143-018-0028-9.
  19. Isaksen, L., 2011: Data assimilation on future computer architectures. Proc. Seminar on Data Assimilation for Atmosphere and Ocean, ECMWF Seminar on Data assimilation for atmosphere and ocean, Reading, United Kingdom, ECMWF, 301-322.
  20. Joo, S., J. Eyre, and R. Marriott, 2013: The impact of Metop and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev., 141, 3331-3342, doi:10.1175/MWR-D-12-00232.1.
  21. Kang, J.-H., and Coauthors, 2018: Development of an observation processing package for data assimilation in KIAPS. Asia-Pac. J. Atmos. Sci., 54, 303-318, doi:10.1007/s13143-018-0030-2.
  22. Kelly, G., J.-N. Thepaut, R. Buizza, and C. Cardinali, 2007: The value of observations. І: data denial experiments for the Atlantic and the Pacific. Q. J. R. Meteorol. Soc., 133, 1803-1815. https://doi.org/10.1002/qj.150
  23. Kim, Y.-J., W. F. Campbell, and S. D. Swadley, 2010: Reduction of middle-atmospheric forecast bias through improvement in satellite radiance quality control. Wea. Forecasting, 25, 681-700, doi:10.1175/2009WAF2222329.1.
  24. Lorenc, A. C., and O. Hammon, 1988: Objective quality control of observations using Bayesian methods. Theory, and a practical implementation. Q. J. R. Meteorol. Soc., 114, 515-543. https://doi.org/10.1002/qj.49711448012
  25. Lorenc, A. C., and R. T. Marriott, 2014: Forecast sensitivity to observations in the Met Office global numerical weather prediction system. Q. J. R. Meteorol. Soc., 140, 209-224, doi:10.1002/qj.2122.
  26. Lee, S., J.-H. Kim, J.-H. Kang, and H.-W. Chun, 2013: Development of pre-processing and bias correction modules for AMSU-A satellite data in the KIAPS observation processing system. Atmosphere, 23, 453-470 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2013.23.4.453
  27. Lee, S., S. Kim, H.-W. Chun, J.-H. Kim, and J.-H. Kang, 2014: Pre-processing and bias correction for AMSUA radiance data based on statistical methods. Atmosphere, 24, 491-502 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2014.24.4.491
  28. Rabier, F., 2011: Pre- and post-processing in data assimilation. Conf. paper, Seminar on data assimilation for atmosphere and ocean, Reading, UK, ECMWF, 45-59 [Available online at https://www.ecmwf.int/node/11785].
  29. Rabier, F., H. Jarvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: experimental results with simplified physics. Q. J. R. Meteorol. Soc., 126, 1143-1170. https://doi.org/10.1002/qj.49712656415
  30. Rawlins, F., S. P. Ballard, K. J. Bovis, A. M. Clayton, D. Li, G. W. Inverarity, A. C. Lorenc, and T. J. Payne, 2007: The Met Office global four-dimensional variational data assimilation scheme. Q. J. R. Meteorol. Soc., 133, 347-362. https://doi.org/10.1002/qj.32
  31. Song, H.-J., and I.-H. Kwon, 2015: Spectral transformation using a cubed-sphere grid for a three-dimensional variational data assimilation system. Mon. Wea. Rev., 143, 2581-2599, doi:10.1175/MWR-D-14-00089.1.
  32. Song, H.-J., J. Kwon, I.-H. Kwon, J.-H. Ha, J.-H. Kang, S. Lee, H.-W. Chun, and S. Lim, 2017: 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. Q. J. R. Meteorol. Soc., 143, 2036-2049, doi:10.1002/qj.3065.