Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Impact of SAPHIR Data Assimilation in the KIAPS Global Numerical Weather Prediction System

KIAPS 전지구 수치예보모델 시스템에서 SAPHIR 자료동화 효과

  • Lee, Sihye (Korea Institute of Atmospheric Prediction Systems) ;
  • Chun, Hyoung-Wook (Korea Institute of Atmospheric Prediction Systems) ;
  • Song, Hyo-Jong (Korea Institute of Atmospheric Prediction Systems)
  • 이시혜 ((재) 한국형예보모델개발사업단) ;
  • 전형욱 ((재) 한국형예보모델개발사업단) ;
  • 송효종 ((재) 한국형예보모델개발사업단)
  • Received : 2018.03.05
  • Accepted : 2018.05.23
  • Published : 2018.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The KIAPS global model and data assimilation system were extended to assimilate brightness temperature from the Sondeur $Atmosph{\acute{e}}rique$ du Profil $d^{\prime}Humidit{\acute{e}}$ Intertropicale par $Radiom{\acute{e}}trie$ (SAPHIR) passive microwave water vapor sounder on board the Megha-Tropiques satellite. Quality control procedures were developed to assess the SAPHIR data quality for assimilating clear-sky observations over the ocean, and to characterize observation biases and errors. In the global cycle, additional assimilation of SAPHIR observation shows globally significant benefits for 1.5% reduction of the humidity root-mean-square difference (RMSD) against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analysis. The positive forecast impacts for the humidity and temperature in the experiment assimilating SAPHIR were predominant at later lead times between 96- and 168-hour. Even though its spatial coverage is confined to lower latitudes of $30^{\circ}S-30^{\circ}N$ and the observable variable is humidity, the assimilation of SAPHIR has a positive impact on the other variables over the mid-latitude domain. Verification showed a 3% reduction of the humidity RMSD with assimilating SAPHIR, and moreover temperature, zonal wind and surface pressure RMSDs were reduced up to 3%, 5% and 7% near the tropical and mid-latitude regions, respectively.

Keywords

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. Andersson, E., E. Holm, P. Bauer, A. Beljaars, G. A. Kelly, A. P. McNally, A. J. Simmons, J.-N. Thepaut, and A. M. Tompkins, 2007: Analysis and forecast impact of the main humidity observing systems. Quart. J. Roy. Meteor. Soc., 133, 1473-1485, doi:10.1002/qj.112.
  3. Bormann, N., M. Bonavita, R. Dragani, R. Eresmaa, M. Matricardi, and A. McNally, 2016: Enhancing the impact of IASI observations through an updated observation-error covariance matrix. Quart. J. Roy. Meteor. Soc., 142, 1767-1780, doi:10.1002/qj.2774.
  4. Brogniez, H., G. Clain, and R. Roca, 2015: Validation of upper-Tropospheric humidity from SAPHIR on board Megha-Tropiques using Tropical sounding. J. App. Met. Clim., 54, 896-908, doi:10.1175/JAMC-D-14-0096.1.
  5. Chambon, P., L.-F. Meunier, F. Guillaume, J.-M. Piriou, R. Roca, and J.-F. Mahfouf, 2015: Investigating the impact of the water-vapour sounding observations from SAPHIR on board Megha-Tropiques for the ARPEGE global model. Quart. J. Roy. Meteor. Soc., 141, 1769-1779, doi:10.1002/qj.2478.
  6. Chang, C.-P., Y. Zhang, and T. Li, 2000: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge. J. Climate, 13, 4310-4325. https://doi.org/10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2
  7. 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.
  8. Geer, A. J., F. Boardo, N. Bormann, and S. English, 2014: All-sky assimilation of microwave humidity sounders. ECMWF Tech. Memo 741, 57 pp.
  9. Hoskins, B. J., and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 1661-1671. https://doi.org/10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2
  10. Jones, E., K. Garrett, and S.-A. Boukabara, 2017: Assimilation of Megha-Tropiques SAPHIR observation in the NOAA global model. Mon. Wea. Rev., 145, 3725-3744, doi:10.1175/MWR-D-16-0148.1.
  11. Lee, M.-H., S. Lee, H.-J. Song, and C.-H. Ho, 2017: The recent increase in the occurrence of a boreal summer teleconnection and its relationship with temperature extremes. J. Clim., 30, 7493-7504, doi:10.1175/JCLI-D-16-0094.1.
  12. Lee, S., S. Kim, H.-W. Chun, J.-H. Kim, and J.-H. Kang, 2014: Pre-processing and bias correction for AMSU-A radiance data based on statistical methods. Atmosphere, 24, 491-502, doi:10.14191/Atmos.2014.24.4.491 (in Korean with English abstract).
  13. Qu, X., 2017: The intermodel diversity of East Asia's summer rainfall among CMIP5 models. J. Climate, 30, 9287-9301, doi:10.1175/JCLI-D-17-0094.1.
  14. Ratnam, M. V., G. Basha, B. V. Krishna Murthy, and A. Jayaraman, 2013: Relative humidity distribution from SAPHIR experiment on board Megha-Tropiques satellite mission: Comparison with global radiosonde and other satellite and reanalysis data sets. J. Geophys. Res., 118, 9622-9630, doi:10.1002/jgrd.50699.
  15. Singh, R., S. P. Ojha, C. M. Kishtawal, and P. K. Pal, 2013: Quality assessment and assimilation of Megha-Tropiques SAPHIR radiances into WRF assimilation system. J. Geophys. Res., 118, 6957-6969, doi:10.1002/jgrd.50502.
  16. 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.
  17. Song, H.-J., J. Kwun, 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. Quart. J. Roy. Meteor. Soc., 143, 2036-2049, doi:10.1002/qj.3065.