A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area

Takuya Kawabata Meteorological Research Institute/Japan Meteorological Agency, Tsukuba, Ibaraki, Japan

Search for other papers by Takuya Kawabata in
Current site
Google Scholar
PubMed
Close
,
Tohru Kuroda Meteorological Research Institute/Japan Meteorological Agency, Tsukuba, Ibaraki, Japan

Search for other papers by Tohru Kuroda in
Current site
Google Scholar
PubMed
Close
,
Hiromu Seko Meteorological Research Institute/Japan Meteorological Agency, Tsukuba, Ibaraki, Japan

Search for other papers by Hiromu Seko in
Current site
Google Scholar
PubMed
Close
, and
Kazuo Saito Meteorological Research Institute/Japan Meteorological Agency, Tsukuba, Ibaraki, Japan

Search for other papers by Kazuo Saito in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity.

The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity.

An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals.

The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

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 Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter special collection.

Abstract

A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity.

The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity.

An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals.

The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

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 Intercomparisons of 4D-Variational Assimilation and the Ensemble Kalman Filter special collection.

Save
  • Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 18051824.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., J. N. Thepaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and A. M. Da Silva, 2003: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131, 155171.

  • Doms, G., and U. Schaettler, 1999: The nonhydrostatic limited area model LM of DWD. Part I. Scientific Documentation, DWD, 172 pp.

  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State–NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and clold front. Mon. Wea. Rev., 121, 14931513.

    • Search Google Scholar
    • Export Citation
  • Gal-Chen, T., and R. C. J. Somerville, 1975: On the use of a coordinate transform for the solution of the Navier–Stokes equation. J. Comput. Phys., 17, 209228.

    • Search Google Scholar
    • Export Citation
  • Honda, Y., M. Nishijima, K. Koizumi, Y. Ohta, K. Tamiya, T. Kawabata, and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a nonhydrostatic model at Japan Meteorological Agency: Formulation and preliminary results. Quart. J. Roy. Meteor. Soc., 131, 34653475.

    • Search Google Scholar
    • Export Citation
  • Huang, X., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299314.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Koizumi, K., Y. Ishikawa, and T. Tsuyuki, 2005: Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts. SOLA, 1, 4548.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 17471763.

    • 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.

    • 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, 82-1B, 473489.

    • Search Google Scholar
    • Export Citation
  • Shoji, Y., 2009: A study of near real-time water vapor analysis using a nationwide dense GPS network of Japan. J. Meteor. Soc. Japan, 87, 118.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 11731185.

    • Search Google Scholar
    • Export Citation
  • Sokol, Z., and D. Rezacova, 2009: Assimilation of the radar-derived water vapour mixing ratio into the LM COSMO model with a high horizontal resolution. Atmos. Res., 92, 331342.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55, 835852.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and Y. Zhang, 2008: Analysis and prediction of a squall line observed during IHOP using multiple WSR-88D observations. Mon. Wea. Rev., 136, 23642388.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1996a: Variational data assimilation in the tropics using precipitation data. Part I: Column model. Meteor. Atmos. Phys., 60, 87104.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1996b: Variational data assimilation in the tropics using precipitation data. Part II: 3D model. Mon. Wea. Rev., 124, 25452561.

    • Search Google Scholar
    • Export Citation
  • Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, and D. M. Barker, 2007: An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall. J. Appl. Meteor. Climatol., 46, 1422.

    • Search Google Scholar
    • Export Citation
  • Xu, Q., 1996: Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Atmos. Sci., 53, 11231142.

    • Search Google Scholar
    • Export Citation
  • Xue, M., M. Tong, and K. K. Droegemeier, 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 Technol., 23, 4466.

    • Search Google Scholar
    • Export Citation
  • Zou, X., and Y. H. Kuo, 1996: Rainfall assimilation through and optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. Rev., 124, 28592882.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1359 520 12
PDF Downloads 240 74 10