• Baker, W. E., and Coauthors, 2014: Lidar-measured wind profiles—The missing link in the global observing system. Bull. Amer. Meteor. Soc.,95, 543–564, doi:10.1175/BAMS-D-12-00164.1.

  • Banta, R. M., , L. D. Olivier, , P. H. Gudiksen, , and R. Lange, 1996: Implications of small-scale flow features to modeling dispersion over complex terrain. J. Appl. Meteor., 35, 330342, doi:10.1175/1520-0450(1996)035<0330:IOSSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., , J. B. Houser, , M. M. French, , and J. C. Snyder, 2014: Observations of the boundary layer near tornadoes and in supercells using a mobile, collocated, pulsed Doppler lidar and radar. J. Atmos. Oceanic Technol., 31, 302325, doi:10.1175/JTECH-D-13-00112.1.

    • Search Google Scholar
    • Export Citation
  • Done, J., , C. A. Davis, , and M. Weisman, 2004: The next-generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmos. Sci. Lett., 5, 110117, doi:10.1002/asl.72.

    • Search Google Scholar
    • Export Citation
  • Drechsel, S., , M. Chong, , G. J. Mayr, , M. Weissmann, , R. Calhoun, , and A. Dörnbrack, 2009: Three-dimensional wind retrieval: Application of MUSCAT to dual-Doppler lidar. J. Atmos. Oceanic Technol., 26, 635646, doi:10.1175/2008JTECHA1115.1.

    • Search Google Scholar
    • Export Citation
  • Drechsel, S., , G. J. Mayr, , M. Chong, , and F. K. Chow, 2010: Volume scanning strategies for 3D wind retrieval from dual-Doppler lidar measurements. J. Atmos. Oceanic Technol., 27, 18811892, doi:10.1175/2010JTECHA1495.1.

    • Search Google Scholar
    • Export Citation
  • Fujibe, T., 2002: Surface wind patterns preceding short-time heavy rainfall in Tokyo in the afternoon of midsummer days (in Japanese). Tenki, 49, 393405.

    • Search Google Scholar
    • Export Citation
  • Hara, T., and Coauthors, 2013: The operational convection-permitting regional model at JMA. Res. Act. Atmos. Oceanic Modell.,43, 5.5–5.6. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2013/individual-articles/05_Hara_Tabito_wgne_2013_lfm.pdf.]

  • Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299314, doi:10.1175/2008MWR2577.1.

    • Search Google Scholar
    • Export Citation
  • Ishii, S., and Coauthors, 2006: Development of 2-micron airborne coherent Doppler lidar at NICT. Lidar Remote Sensing for Environmental Monitoring VII, U. N. Singh, T. Itabe, and D. N. Rao, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6409), 64090J, doi:10.1117/12.693526.

    • Search Google Scholar
    • Export Citation
  • Ishii, S., and Coauthors, 2010: Coherent 2 μm differential absorption and wind lidar with conductively cooled laser and two-axis scanning device. Appl. Opt., 49, 18091817, doi:10.1364/AO.49.001809.

    • Search Google Scholar
    • Export Citation
  • Iwai, H., , S. Ishii, , R. Oda, , K. Mizutani, , S. Sekizawa, , and Y. Murayama, 2013: Performance and technique of coherent 2-μm differential absorption and wind lidar for wind measurement. J. Atmos. Oceanic Technol., 30, 429449, doi:10.1175/JTECH-D-12-00111.1.

    • Search Google Scholar
    • Export Citation
  • Kato, T., , and K. Aranami, 2005: Formation factors of 2004 Niigata-Fukushima and Fukui heavy rainfalls and problems in the predictions using a cloud-resolving model. SOLA, 1, 14, doi:10.2151/sola.2005-001.

    • Search Google Scholar
    • Export Citation
  • Kawabata, T., , H. Seko, , K. Saito, , T. Kuroda, , K. Tamiya, , T. Tsuyuki, , Y. Honda, , and Y. Wakazuki, 2007: An assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensional variational data assimilation system. J. Meteor. Soc. Japan, 85, 255276, doi:10.2151/jmsj.85.255.

    • Search Google Scholar
    • Export Citation
  • Kawabata, T., , T. Kuroda, , H. Seko, , and K. Saito, 2011: A cloud-resolving 4D-Var assimilation experiment for a local heavy rainfall event in the Tokyo metropolitan area. Mon. Wea. Rev., 139, 19111931, doi:10.1175/2011MWR3428.1.

    • Search Google Scholar
    • Export Citation
  • Kawabata, T., , Y. Shoji, , H. Seko, , and K. Saito, 2013: A numerical study on a mesoscale convective system over a subtropical island with 4D-Var assimilation of GPS slant total delays. J. Meteor. Soc. Japan, 91, 705721, doi:10.2151/jmsj.2013-510.

    • Search Google Scholar
    • Export Citation
  • Kusunoki, K., 2002: A preliminary survey of clear-air echo appearances over the Kanto Plain in Japan from July to December 1997. J. Atmos. Oceanic Technol., 19, 10631072, doi:10.1175/1520-0426(2002)019<1063:APSOCA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., , and R. M. Banta, 2004: Assimilating coherent Doppler lidar measurements into a model of the atmospheric boundary layer. Part I: Algorithm development and sensitivity to measurement error. J. Atmos. Oceanic Technol., 21, 13281345, doi:10.1175/1520-0426(2004)021<1328:ACDLMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rennie, S., , J. S. L. Dance, , A. J. Illingworth, , S. P. Ballard, , and D. Simonin, 2011: 3D-Var assimilation of insect-derived Doppler radar radial winds in convective cases using a high-resolution model. Mon. Wea. Rev., 139, 11481163, doi:10.1175/2010MWR3482.1.

    • Search Google Scholar
    • Export Citation
  • Saito, K., 2012: The JMA nonhydrostatic model and its application to operation and research. Atmospheric Model Applications, I. Yucel, Ed., Intech, 85–110, doi:10.5772/35368.

  • Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 12661298, doi:10.1175/MWR3120.1.

    • Search Google Scholar
    • Export Citation
  • Saito, K., , J. Ishida, , K. Aranami, , T. Hara, , T. Segawa, , M. Narita, , and Y. Honda, 2007: Nonhydrostatic atmospheric models and operational development at JMA. J. Meteor. Soc. Japan, 85B, 271304, doi:10.2151/jmsj.85B.271.

    • Search Google Scholar
    • Export Citation
  • Seko, H., , T. Kawabata, , T. Tsuyuki, , H. Nakamura, , K. Koizumi, , and T. Iwabuchi, 2004: Impacts of GPS-derived water vapor and radial wind measured by Doppler radar on numerical prediction of precipitation. J. Meteor. Soc. Japan, 82, 473489, doi:10.2151/jmsj.2004.473.

    • Search Google Scholar
    • Export Citation
  • Shoji, Y., 2013: Retrieval of water vapor anisotropy using the Japanese nationwide GPS array and its potential for prediction of convective precipitation. J. Meteor. Soc. Japan, 91, 4362, doi:10.2151/jmsj.2013-103.

    • Search Google Scholar
    • Export Citation
  • Shun, C. M., , and P. W. Chan, 2008: Applications of an infrared Doppler lidar in detection of wind shear. J. Atmos. Oceanic Technol., 25, 637655, doi:10.1175/2007JTECHA1057.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W., C., J. B. Klemp, , J. Dudhia, , D. O. Gill, , D. M. Barker, , W. Wang, , and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp. [Available online at www2.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]

  • Stoffelen, A., and Coauthors, 2005: The atmospheric dynamics mission for global wind measurement. Bull. Amer. Meteor. Soc., 86, 7387, doi:10.1175/BAMS-86-1-73.

    • Search Google Scholar
    • Export Citation
  • Sun, J., , and A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 16421661, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Takahashi, T., 1981: Warm rain study in Hawaii—Rain initiation. J. Atmos. Sci., 38, 347369, doi:10.1175/1520-0469(1981)038<0347:WRSIHI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tang, Y., , H. W. Lean, , and J. Bornemann, 2013: The benefits of the Met Office variable resolution NWP model for forecasting convection. Meteor. Appl., 20, 417426, doi:10.1002/met.1300.

    • Search Google Scholar
    • Export Citation
  • Wang, H., , T. Auligne, , and H. Morrision, 2012: Impact of microphysics scheme complexity on the propagation of initial perturbations. Mon. Wea. Rev., 140, 22872296, doi:10.1175/MWR-D-12-00005.1.

    • Search Google Scholar
    • Export Citation
  • Weissmann, M., , R. H. Langland, , C. Cardinali, , P. M. Pauley, , and S. Rahm, 2012: Influence of airborne Doppler wind lidar profiles near Typhoon Sinlaku on ECMWF and NOGAPS forecasts. Quart. J. Roy. Meteor. Soc., 138, 118130, doi:10.1002/qj.896.

    • Search Google Scholar
    • Export Citation
  • Yamada, Y., 2012: A unique cumulonimbus producing a localized heavy rainfall in Tokyo metropolitan during TOMACS. Proc. 16th Int. Conf. on Clouds and Precipitation, Leipzig, Germany, ICCP.

  • Zhang, F., , A. M. Odins, , and J. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166, doi:10.1175/WAF909.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., , and Z. Pu, 2011: Four-dimensional assimilation of multitime wind profiles over a single station and numerical simulation of a mesoscale convective system observed during IHOP_2002. Mon. Wea. Rev., 139, 33693388, doi:10.1175/2011MWR3569.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 19 19 12
PDF Downloads 13 13 9

Cloud-Resolving 4D-Var Assimilation of Doppler Wind Lidar Data on a Meso-Gamma-Scale Convective System

View More View Less
  • 1 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki, Japan
  • 2 National Institute of Information and Communications Technology, Koganei, Tokyo, Japan
  • 3 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Ibaraki, Japan
  • 4 National Institute of Information and Communications Technology, Koganei, Tokyo, Japan
© Get Permissions
Restricted access

Abstract

The authors evaluated the effects of assimilating three-dimensional Doppler wind lidar (DWL) data on the forecast of the heavy rainfall event of 5 July 2010 in Japan, produced by an isolated mesoscale convective system (MCS) at a meso-gamma scale in a system consisting of only warm rain clouds. Several impact experiments using the nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) and the Japan Meteorological Agency nonhydrostatic model with a 2-km horizontal grid spacing were conducted in which 1) no observations were assimilated (NODA), 2) radar reflectivity and radial velocity determined by Doppler radar and precipitable water vapor determined by GPS satellite observations were assimilated (CTL), and 3) radial velocity determined by DWL were added to the CTL experiment (LDR) and five data denial and two observational error sensitivity experiments. Although both NODA and CTL simulated an MCS, only LDR captured the intensity, location, and horizontal scale of the observed MCS. Assimilating DWL data improved the wind direction and speed of low-level airflows, thus improving the accuracy of the simulated water vapor flux. The examination of the impacts of specific assimilations and assigned observation errors showed that assimilation of all data types is important for forecasting intense MCSs. The investigation of the MCS structure showed that large amounts of water vapor were supplied to the rainfall event by southerly flow. A midlevel inversion layer led to the production of exclusively liquid water particles in the MCS, and in combination with the humid airflow into the MCS, this inversion layer may be another important factor in its development.

Corresponding author address: Takuya Kawabata, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tkawabat@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Abstract

The authors evaluated the effects of assimilating three-dimensional Doppler wind lidar (DWL) data on the forecast of the heavy rainfall event of 5 July 2010 in Japan, produced by an isolated mesoscale convective system (MCS) at a meso-gamma scale in a system consisting of only warm rain clouds. Several impact experiments using the nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) and the Japan Meteorological Agency nonhydrostatic model with a 2-km horizontal grid spacing were conducted in which 1) no observations were assimilated (NODA), 2) radar reflectivity and radial velocity determined by Doppler radar and precipitable water vapor determined by GPS satellite observations were assimilated (CTL), and 3) radial velocity determined by DWL were added to the CTL experiment (LDR) and five data denial and two observational error sensitivity experiments. Although both NODA and CTL simulated an MCS, only LDR captured the intensity, location, and horizontal scale of the observed MCS. Assimilating DWL data improved the wind direction and speed of low-level airflows, thus improving the accuracy of the simulated water vapor flux. The examination of the impacts of specific assimilations and assigned observation errors showed that assimilation of all data types is important for forecasting intense MCSs. The investigation of the MCS structure showed that large amounts of water vapor were supplied to the rainfall event by southerly flow. A midlevel inversion layer led to the production of exclusively liquid water particles in the MCS, and in combination with the humid airflow into the MCS, this inversion layer may be another important factor in its development.

Corresponding author address: Takuya Kawabata, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tkawabat@mri-jma.go.jp

This article is included in the Sixth WMO Data Assimilation Symposium Special Collection.

Save