• \
    Start Submission Become a Reviewer

    Reading: Data assimilation experiments of precipitable water vapour using the LETKF system: intense r...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008

    Authors:

    Hiromu Seko ,

    Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
    X close

    Takemasa Miyoshi,

    University of Maryland, College Park, MD 20742, US
    X close

    Yoshinori Shoji,

    Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
    X close

    Kazuo Saito

    Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, JP
    X close

    Abstract

    We performed data assimilation experiments using data from an intense rainfall event in western Japan on 28 July 2008 with the Local Ensemble Transform Kalman Filter (LETKF) and analysed fields of the mesoscale four-dimensional variational assimilation (4DVAR) system of the Japan Meteorological Agency (JMA). We supplemented conventional observation data with precipitable water vapour (PWV) data derived from the Global Positioning System (GPS) Earth Observation Network of the Geospatial Information Authority of Japan. Because the LETKF system assimilates fewer data than the 4DVAR system, ensemble mean fields of the LETKF cycle experiment were replaced with analysed fields from the 4DVAR system each day at 12 UTC. PWV values were converted to relative humidity profiles for assimilation by the LETKF. The addition of PWV data tended to increase low-level water vapour and improve the precipitation forecast. We attempted to reproduce the intense rainfall band using downscale forecast experiments with the JMA non-hydrostatic model (JMANHM) with grid spacings of 5 and 1.6 km. The experiment with 5-km resolution generated a rainfall band in western Japan that was not reproduced using conventional data, although the rainfall was smaller than observations. The experiment with 1.6-km resolution faithfully reproduced the observed band of intense rainfall.

    How to Cite: Seko, H., Miyoshi, T., Shoji, Y. and Saito, K., 2011. Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.402–414. DOI: http://doi.org/10.1111/j.1600-0870.2010.00508.x
    22
    Citations
      Published on 01 Jan 2011
    Peer Reviewed
     CC BY 4.0
     Accepted on 21 Dec 2010            Submitted on 26 Apr 2010

    References

    1. Bauer , H.-S. , Wulfmeyer , V. , Schwitalla , T. , Zus , F. and Grzeschilc , M . 2011 . Operational assimilation of GPS slant path delay mea-surements into the MM5 4DVAR system , Tellus A, this issue , https://doi.org/10.1111/j.1600-0870.2010.00489.x  

    2. Dowell , D. C. , Zhang , E , Wicker , L. J. , Snyder , C. and Crook , N. A . 2004 . Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, Supercell: ensemble Kalman filter experiments. Mon . Wea. Re v . 132 , 1982 – 2005 .  

    3. Fujita , T. , Tsuguchi , H. , Miyoshi , T. , Seko , H. and Saito , K . 2009 . De-velopment of a mesoscale ensemble data assimilation system at JMA. Report of the Grant-in-Aid for Scientific Research (B) (2005-2008), No. 17110035,232-235. (in Japanese)  

    4. Hunt , B. R. , Kostelich , E. J. and Szunyogh , I . 2007 . Efficient data assim-ilation for spatiotemporal chaos: a local ensemble transform Kalman filter . Physica D , 230 , 112 – 126 .  

    5. Kawabata , T. , Seko , H. , Saito , K. , Kuroda , T. , Tamiya , K. and co-authors . 2007 . An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensional varia-tional data assimilation system. J. Meteorol. Soc. Japan , 85 , 255 - 276 .  

    6. Meng , Z. and Zhang , F . 2008 . Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part DI: Comparison with 3DVar in a real-data case study. Mon . Wea. Re v . 136 , 522 – 540 .  

    7. Miyoshi , T . 2010 . NHM-LETKE , Tech. Rep. MRI , 62 , 159 – 163 .  

    8. Miyoshi , T. and Aranami , K . 2006 . Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKE) to the .TMA nonhy-drostatic model (NI-IM) . SOLA , 2 , 128 – 131 .  

    9. Miyoshi , T. , Yamane , S. and Enomoto , T . 2007 . Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF) . SOLA , 3 , 89 – 92 .  

    10. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V. , Kostelich , E. J. and co-authors . 2004 . A local ensemble Kalman filter for atmospheric data assimilation. Tellus , 56A , 415 - 428 .  

    11. Saito , K. , Fujita , T. , Yamada , Y. , Ishida , J. , Kumagai , Y. and co-authors . 2006. The operational .TMA nonhydrostatic mesoscale model. Mon. Wea. Rev . 134 , 1266-129 8 .  

    12. Saito , K. , Kunii , M. , Hara , M. , Seko , H. , Hara , T. and co-authors . 2010 . WWRP Beijing 2008 Olympics Forecast Demonstration/Research and Development Project (B08FDP/RDP). Tech. Rep. MRI , 62 , 210 pp .  

    13. Saito , K. , Hara , M. , Kunii , M. , Seko , H. and Yamaguchi , M . 2011 . Com-parison of initial perturbation methods for the mesoscale ensemble prediction system of the Meteorological Research Institute for the WWRP Beijing 2008 Olympics Research and Development Project (B08RDP). Tellus , 63A , this issue .  

    14. Seko , H . 2010 . Local ensemble transform Kalman filter (LET) method . Tech. Rep. MRI , 62 , 80 – 84 .  

    15. Seko , H. and Nakamura , H . 2005 . Analytical and numerical studies on meso-fl scale precipitation bands observed over southern Kyushu on 7 July 1996 . Papers in Meteorology and Geophysics , 55 , 55 – 74 .  

    16. Seko , H. , Kawabata , T. , Tsuyuki , T. , Nakamura , H. , Koizumi , K. and co-authors . 2004 . Impacts of GPS-derived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation. J. Meteorol. Soc. Japan , 82 , 473 - 489 .  

    17. Seko , H. , Kunii , M. , Shoji , Y. and Saito , K . 2010 . Improvement of rainfall forecast by assimilations of ground-based GPS data and radio occultation data . SOLA , 6 , 81 – 84 .  

    18. Shoji , Y. , Nakamura , H. , Iwabuchi , T. , Aonashi , K. , Seko , H. and co-authors . 2004 , Tsukuba GPS dense net campaign observation: im-provement in GPS analysis of Slant Path delay by stacking one-way postfit phase residuals. J. Meteorol. Soc. Japan , 82 , 301 - 314 .  

    19. Shoji , Y. , Kunii , M. and Saito , K . 2009 . Assimilation of nationwide and global GPS PWV data for heavy rain event on 28 July 2008 in Hokuriku and Kinki, Japan . SOLA , 5 , 45 – 48 .  

    20. Snyder , C. and Zhang , F . 2003 . Assimilation of simulated doppler radar observations with an ensemble Kalman filter. Mon . Wea. Re v . 131 , 1663 – 1677 .  

    21. Tippett , M. K. , Anderson , J. L. , Bishop , C. H. , Hamill , T. M. and Whitaker , J. S . 2003 . Ensemble square root filters. Mon . Wea. Re v . 131 , 1485 – 1490 .  

    22. Tong , M. and Xue , M . 2005 . Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon . Wea. Re v . 133 , 1789 – 1807 .  

    23. Xue , M. , Tong , M. and Droegemeier , K. K . 2006 . An OSSE framework based on the ensemble square root Kalman filter for evaluating the impact of data from radar networks on thunderstorm analysis and forecasting . J. Atmos. Oceanic Tech . 23 , 46 – 66 .  

    24. Yan , X. , Ducrocq , V. , Jaubert , G. , Brousseau , P. , Poli , P. and co-authors . 2009 . The benefit of GPS zenith delay assimilation to high-resolution quantitative precipitation forecasts: a case-study from COPS IOP 9 . Q. J. R. Meteorol. Soc . 135 , 1788– 1800 ,  

    25. Zhang , F. , Snyder , C. and Sun , J . 2004 . Impacts of initial estimate and observation availability with an ensemble Kalman filter. Mon . Wea. Re v . 132 , 1238 – 1253 .  

    26. Zhang , F. , Meng , Z. and Alcsoy , A . 2006 . Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: perfect model experiments. Mon . Wea. Re v . 134 , 722 – 736 .  

    27. Zhang , M. , Ni , Y. and Zhang , F . 2007 . Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a meso-fi-scale heavy precipitation event during the 2002 Mei-Yu season. Adv . Atmos. Sc i . 24 , 509 – 526 .  

    Jump to Discussions Related content
    comments powered by Disqus