Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Ground-based GNSS Data Assimilation System for KIM and their Impacts

KIM을 위한 지상 기반 GNSS 자료 동화 체계 개발 및 효과

  • Han, Hyun-Jun (Korea Institute of Atmospheric Prediction Systems) ;
  • Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems) ;
  • Kwon, In-Hyuk (Korea Institute of Atmospheric Prediction Systems)
  • 한현준 (차세대수치예보모델개발사업단) ;
  • 강전호 (차세대수치예보모델개발사업단) ;
  • 권인혁 (차세대수치예보모델개발사업단)
  • Received : 2022.04.25
  • Accepted : 2022.08.11
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Assimilation trials were performed using the Korea Institute of Atmospheric Prediction Systems (KIAPS) Korea Integrated Model (KIM) semi-operational forecast system to assess the impact of ground-based Global Navigation Satellite System (GNSS) Zenith Total Delay (ZTD) on forecast. To use the optimal observation in data assimilation of KIM forecast system, in this study, the ZTD observation were pre-processed. It involves the bias correction using long term background of KIM, the quality control based on background and the thinning of ZTD data. Also, to give the effect of observation directly to data assimilation, the observation operator which include non-linear model, tangent linear model, adjoint model, and jacobian code was developed and verified. As a result, impact of ZTD observation in both analysis and forecast was neutral or slightly positive on most meteorological variables, but positive on geopotential height. In addition, ZTD observations contributed to the improvement on precipitation of KIM forecast, specially over 5 mm/day precipitation intensity.

Keywords

Acknowledgement

본 연구는 기상청 출연사업인 (재)차세대수치예보모델개발사업단의 4차원 고품질 기상분석을 위한 최신 자료동화기술 개발(KMA2020-02211)의 지원을 받아 수행되었음.

References

  1. Bennitt, G. V., and A. Jupp, 2012: Operational assimilation of GPS zenith total delay observations into the met office numerical weather prediction models. Mon. Wea. Rev., 140, 2706-2719, doi:10.1175/MWR-D-11-00156.1.
  2. Bennitt, G. V., H. R. Johnson, P. P. Weston, J. Jones, and E. Pottiaux, 2017: An assessment of ground-based GNSS zenith total delay observation errors and their correlations using the met office UKV model. Quart. J. Roy. Meteor. Soc., 143, 2436-2447, doi:10.1002/qj.3097.
  3. Bevis, M., S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, 1992: GPS meteorology: Remote sensing of atmospheric water vapor using the Global positioning system. J. Geophys. Res. Atmos., 97, D14, 15787-15801, doi:10.1029/92JD01517.
  4. 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-Pacific J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.
  5. Chrust, M., 2020: Tangent linear and adjoint models for variational data assimilation. ECMWF Training course: Data assimilation [Available online at https://events.ecmwf.int/event/153/contributions/907/attachments/470/858/Training_course_2020_TLAD.pdf]
  6. 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.
  7. Eresmaa, R., S. Healy, H. Jarvinen, and K. Salonen, 2008: Implementation of a ray-tracing operator for groundbased GPS Slant Delay observation modeling. J. Geophys. Res. Atmos., 113, D11114, doi:10.1029/2007JD009256.
  8. Jo, Y., J.-S. Kang, and H. Kwon, 2015: Optimization of the vertical localization scale for GPS-RO data assimilation within KIAPS-LETKF system. Atmosphere, 25, 529-541, doi:10.14191/Atmos.2015.25.3.529 (in Korean with English abstract).
  9. Jo, Y., S. Lim, I.-H. Kwon, and H.-J. Han, 2018: Investigation of analysis effects of ASCAT data assimilation within KIAPS-LETKF system. Atmosphere, 28, 263-272, doi:10.14191/Atmos.2018.28.3.263 (in Korean with English abstract).
  10. Han, H.-J., I.-H. Kwon, J.-H. Kang, H.-W. Chun, S. Lee, S. Lim, and T. Kim, 2019: Analysis of forecast performance by altered conventional observation set. Atmosphere, 29, 21-39, doi:10.14191/Atmos.2019.29.1.021 (in Korean with English abstract).
  11. Hdidou, F. Z., S. Mordane, P. Moll, J.-F. Mahfouf, H. Erraji, and Z. Dahmane, 2020: Impact of the variational assimilation of ground-based GNSS zenith total delay into AROME-Morocco model. Tellus A, 72, 1-13, doi:10.1080/16000870.2019.1707854.
  12. Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for Global weather forecasting. Asia-Pacific J. Atmos. Sci., 54, 267-292, doi:10.1007/s13143-018-0028-9.
  13. Hunt, B. R., E. J. 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.
  14. Kang, J.-H., and Coauthors, 2018: Development of an observation processing package for data assimilation in KIAPS. Asia-Pacific J. Atmos. Sci., 54, 303-318, doi:10.1007/s13143-018-0030-2.
  15. Kleespies, T. J., 2006: Jacobian (or K) Coding. Sub Group for Radiative Transfer and Surface Property Models: Tangent Linear and Adjoint Coding Short Course [Available online at https://cimss.ssec.wisc.edu/itwg/groups/rtwg/tl_ad_lectures/K_Lectures.pdf].
  16. Kwon, I.-H., and Coauthors, 2018: Development of an operational hybrid data assimilation system at KIAPS. Asia-Pacific J. Atmos. Sci., 54, 319-335, doi:10.1007/s13143-018-0029-8.
  17. 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, doi:10.14191/Atmos.2013.23.4.453 (in Korean with English abstract).
  18. 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, doi:10.14191/Atmos.2014.24.4.491 (in Korean with English abstract).
  19. Poil, P., P. Moll, F. Rabier, G. Desroziers, B. Chapnik, L. Berre, S. B. Healy, E. Andersson, and Z.-Z. El Guelai, 2007: Forecast impact studies of zenith total delay data from European near real-time GPS stations in Meteo France 4DVAR. J. Geophys. Res. Atmos., 112, D06114, doi:10.1029/2006JD007430.
  20. Rohm, W., J. Guzikowski, K. Wilgan, and M. Kryza, 2019: 4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF. Atmos. Meas. Tech., 12, 345-361, doi:10.5194/amt-12-345-2019.
  21. 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, doi:10.1002/qj.1999.49712555615.
  22. Tralli, D. M. and S. M. Lichten, 1990: Stochastic estimation of tropospheric path delays in Global positioning system geodetic measurements. B. Geodesique, 64, 127-152, doi:10.1007/BF02520642.
  23. Vedel, H., K. S. Mogensen, and X.-Y. Huang, 2001: Calculation of zenith delays from meteorological data, comparison of NWP model, radiosonde and GPS delays. Phys. Chem. Earth, 26, 6-8, 497-502, doi:10.1016/S1464-1895(01)00091-6.
  24. Vedel, H., and X.-Y. Huang, 2004: Impact of ground based GPS data on numerical weather prediction. J. Meteorol. Soc. Jpn., 82, 1B, 459-472, doi:10.2151/jmsj.2004.459.
  25. Xie, P., M. Chen, and W. Shi, 2010: CPC global unified gauge-based analysis of daily precipitation. 24th Conf. on Hydrology [Available online at https://ams.confex.com/ams/90annual/techprogram/paper_163676.htm, Available download at https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html]