Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Verification of the Global Numerical Weather Prediction Using SYNOP Surface Observation Data

SYNOP 지상관측자료를 활용한 수치모델 전구 예측성 검증

  • Lee, Eun-Hee (Korea Institute of Atmospheric Prediction Systems) ;
  • Choi, In-Jin (Korea Institute of Atmospheric Prediction Systems) ;
  • Kim, Ki-Byung (Korea Institute of Atmospheric Prediction Systems) ;
  • Kang, Jeon-Ho (Korea Institute of Atmospheric Prediction Systems) ;
  • Lee, Juwon (Korea Institute of Atmospheric Prediction Systems) ;
  • Lee, Eunjeong (Korea Institute of Atmospheric Prediction Systems) ;
  • Seol, Kyung-Hee (Korea Institute of Atmospheric Prediction Systems)
  • 이은희 ((재) 한국형수치예보모델개발사업단) ;
  • 최인진 ((재) 한국형수치예보모델개발사업단) ;
  • 김기병 ((재) 한국형수치예보모델개발사업단) ;
  • 강전호 ((재) 한국형수치예보모델개발사업단) ;
  • 이주원 ((재) 한국형수치예보모델개발사업단) ;
  • 이은정 ((재) 한국형수치예보모델개발사업단) ;
  • 설경희 ((재) 한국형수치예보모델개발사업단)
  • Received : 2017.02.16
  • Accepted : 2017.06.05
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

Keywords

References

  1. Breon, F., and S. Colzy, 1999: Cloud detection from the spaceborne POLDER instrument and validation against surface synoptic observations. J. Appl. Meteor. Climatol., 38, 777-785. https://doi.org/10.1175/1520-0450(1999)038<0777:CDFTSP>2.0.CO;2
  2. Bromwich, D. H., A. J. Monaghan, K. W. Manning, and J. G. Powers, 2005: Real-time forecasting for the Antarctic: An evaluation of the Antarctic Mesoscale Prediction System (AMPS). Mon. Wea. Rev., 133, 579-603. https://doi.org/10.1175/MWR-2881.1
  3. Choi, H.-J., and S.-Y. Hong, 2015: An updated subgrid orographic parameterization for global atmospheric forecast models. J. Geophys. Res., 120, 12445-12457, doi:10.1002/2015JD024230.
  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-Pac. J. Atmos. Sci., 52, 291-307, doi:10.1007/s13143-016-0005-0.
  5. Chun, H.-Y., and J.-J. Baik, 1998: Momentum flux by thermally induced internal gravity waves and its approximation for large-scale models. J. Atmos. Sci., 55, 3299-3310. https://doi.org/10.1175/1520-0469(1998)055<3299:MFBTII>2.0.CO;2
  6. ECMWF, 2015: Evaluation of ECMWF forecasts, including 2014-2015 upgrades, ECMWF Technical Memorandum No. 765, 51 pp.
  7. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, doi:10.1029/2002JD003296.
  8. Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520-533, doi:10.1175/WAF-D-10-05038.1.
  9. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120. https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  10. Hong, S.-Y., J. Choi, E.-C. Chang, H. Park, and Y.-J. Kim, 2008: Lower-tropospheric enhancement of gravity wave drag in a global spectral atmospheric forecast model. Wea. Forecasting, 23, 523-531, doi:10.1175/2007WAF2007030.1.
  11. Hong, S.-Y., and Coauthors, 2013: The global/regional integrated model system (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219-243, doi:10.1007/s13143-013-0023-0.
  12. Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shepard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.
  13. Jeon, J.-H., S.-Y. Hong, H.-Y. Chun, and I.-S. Song, 2010: Test of a convectively forced gravity wave drag parameterization in a general circulation model. Asia-Pac. J. Atmos. Sci., 46, 1-10, doi:10.1007/s13143-010-0001-8.
  14. Kim, E.-J., and S.-Y. Hong, 2010: Impact of air-sea interaction on East Asian summer monsoon climate in WRF. J. Geophys. Res., 115, D19118, doi:10.1029/2009JD013253.
  15. Liljequist, G. H., and K. Cehak, 1990: Allgemeine Meteo rologie (General Meteorology). Vieweg, Braunschweig, Germany, 396 pp.
  16. Lim, K.-S., S.-Y. Hong, J.-H. Yoon, and J. Han, 2014: Simulation of the summer monsoon rainfall over East Asia using the NCEP GFS cumulus parameterization at different horizontal resolution. Wea. Forecasting, 29, 1143-1154, doi:10.1175/WAF-D-13-00143.1.
  17. Ma, L., T. Zhang, Q. Li, O. W. Frauenfeld, and D. Qin, 2008: Evaluation of ERA-40, NCEP-1, and NCEP-2 reanalysis air temperatures with ground-based measurements in China. J. Geophys. Res., 113, D15115, doi:10.1029/2007JD009549.
  18. Meerkotter, R., C. Konig, P. Bissolli, G. Gesell, and H. Mannstein, 2004: A 14-year European Cloud Climatology from NOAA//AVHRR data in comparison to surface observations. J. Geophys. Res., 31, L15103, doi:10.1029/2004GL020098.
  19. Park, R.-S., J.-H. Chae, and S.-Y. Hong, 2016: A revised prognostic cloud fraction scheme in a global forecasting system. Mon. Wea. Rev., 114, 1219-1229, doi:10.1175/MWR-D-15-0273.1.
  20. Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250-270, doi:10.1175/MWR-D-14-00116.1.
  21. Simmons, A. J., P. D. Jones, V. da Costa Bechtold, A. C. M. Beljaars, P. W. Kallberg, S. Saarinen, S. M. Uppala, P. Viterbo, and N. Wedi, 2004: Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature. J. Geophys. Res., 109, D24115, doi:10.1029/2004JD005306.
  22. Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2011: Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. J. Geophys. Res., 116, D11112, doi:10.1029/2010JD015013.
  23. World Meteorological Organization, 2012: "WMO: standardised verification system for long-range forecasts," in Manual on the Global Data-Processing System, WMO no. 485, World Meteorological Organization, Geneva, Switzerland.
  24. You, Q., K. Fraedrich, G. Ren, N. Pepin, and S. Kang, 2013: Variability of temperature in the Tibetan Plateau based on homogenized surface stations and reanalysis data. Int. J. Climatol., 33, 1337-1347, doi:10.1002/joc.3512.
  25. Zhu, J.-H., S.-P. Ma, H. Zou, L.-B. Zhou, and P. Li, 2014: Evaluation of reanalysis products with in situ GPS sounding observations in the Eastern Himalayas. Atmos. Ocean. Sci. Lett., 7, 17-22, doi:10.3878/j.issn.1674-2834.13.0050.