• Aksoy, A., F. Zhang, and J. W. Nielsen-Gammon, 2006: Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon. Wea. Rev., 134, 29512970, doi:10.1175/MWR3224.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., 2005: Southern high-latitude ensemble data assimilation in the Antarctic Mesoscale Prediction System. Mon. Wea. Rev., 133, 34313449, doi:10.1175/MWR3042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonavita, M., L. Torrisi, and F. Marcucci, 2008: The ensemble Kalman filter in an operational regional NWP system: Preliminary results with real observations. Quart. J. Roy. Meteor. Soc., 134, 17331744, doi:10.1002/qj.313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., and K. R. Mylne, 2009: Ensemble transform Kalman filter perturbation for a regional ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 757766, doi:10.1002/qj.404.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S. S., W. Zhao, M. A. Donelan, and H. L. Tolman, 2013: Directional wind–wave coupling in fully coupled atmosphere–wave–ocean models: Results from CBLAST-Hurricane. J. Atmos. Sci., 70, 31983215, doi:10.1175/JAS-D-12-0157.1.

    • Crossref
    • 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, doi:10.1002/qj.49712051912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • D’Asaro, E., and et al. , 2011: Typhoon–ocean interaction in the western North Pacific: Part 1. Oceanography, 24, 2431, doi:10.5670/oceanog.2011.91.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dare, R. A., and J. L. McBride, 2011: Sea surface temperature response to tropical cyclones. Mon. Wea. Rev., 139, 37983838, doi:10.1175/MWR-D-10-05019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 19822005, doi:10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 14310 162, doi:10.1029/94JC00572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, M. Fiorino, and S. G. Benjamin, 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668688, doi:10.1175/2010MWR3456.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX-98 ensemble forecasts. Mon. Wea. Rev., 129, 7391, doi:10.1175/1520-0493(2001)129<0073:OVOTSE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunt, B. R., E. J. Kostelich, and I. Syzunogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112126, doi:10.1016/j.physd.2006.11.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ikawa, M., and K. Saito, 1991: Description of a nonhydrostatic model developed at the Forecast Research Department of the MRI. MRI Tech. Rep. 28, 238 pp.

  • Ito, K., T. Kuroda, K. Saito, and A. Wada, 2015: Forecasting a large number of tropical cyclone intensities around Japan using a high-resolution atmosphere–ocean coupled model. Wea. Forecasting, 30, 793808, doi:10.1175/WAF-D-14-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Japan Meteorological Agency, 2013: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. Appendix to WMO technical progress report on the global data-processing and forecasting system and numerical weather prediction. Japan Meteorological Agency, 187 pp. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm.]

  • Kawai, Y., and A. Wada, 2007: Diurnal sea surface temperature variation and its impact on the atmosphere and ocean: A review. J. Oceanogr., 63, 721744, doi:10.1007/s10872-007-0063-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunii, M., 2014a: Mesoscale data assimilation for a local severe rainfall event with the NHM–LETKF system. Wea. Forecasting, 29, 10931105, doi:10.1175/WAF-D-13-00032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunii, M., 2014b: The 1000-member ensemble Kalman filtering with the JMA nonhydrostatic mesoscale model on the K computer. J. Meteor. Soc. Japan, 92, 623633, doi:10.2151/jmsj.2014-607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunii, M., and T. Miyoshi, 2012: Including uncertainties of sea surface temperature in an ensemble Kalman filter: A case study of Typhoon Sinlaku (2008). Wea. Forecasting, 27, 15861597, doi:10.1175/WAF-D-11-00136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., T. Sakurai, and T. Kuragano, 2006: Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in-situ observations (in Japanese). Wea. Service Bull., 73, 118.

    • Search Google Scholar
    • Export Citation
  • Lewis, J. M., and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A, 309322, doi:10.1111/j.1600-0870.1985.tb00430.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., Z. Liu, S. Zhang, R. Jacob, F. Lu, X. Rong, and S. Wu, 2014: Ensemble-based parameter estimation in a coupled general circulation model. J. Climate, 27, 71517162, doi:10.1175/JCLI-D-13-00406.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLay, J. G., M. K. Flatau, C. A. Reynolds, J. Cummings, T. Hogan, and P. J. Flatau, 2012: Inclusion of sea-surface temperature variation in the U.S. Navy ensemble-transform global ensemble prediction system. J. Geophys. Res., 117, D19120, doi:10.1029/2011JD016937.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miyoshi, T., and M. Kunii, 2012: The local ensemble transform Kalman filter with the Weather Research and Forecasting model: Experiments with real observations. Pure Appl. Geophys., 169, 321333, doi:10.1007/s00024-011-0373-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagata, K., 2011: Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan meteorological agency. RSMC Tokyo–Typhoon Cent. Tech. Rev.,13, 37–50. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text13-2.pdf.]

  • Nakanishi, M., and H. Niino, 2004: An improved Mellor-Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131, doi:10.1023/B:BOUN.0000020164.04146.98.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2006: An improved Mellor-Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, doi:10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ohmori, S., and Y. Yamada, 2006: Development of cumulus parameterization scheme in the nonhydrostatic mesoscale model at the Japan Meteorological Agency. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 35, 4.214.22.

    • Search Google Scholar
    • Export Citation
  • Ouaraini, R. E., L. Berre, C. Fischer, and E. H. Sayouty, 2015: Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions. Tellus, 67A, 28502, doi:10.3402/tellusa.v67.28502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., G. J. Hakim, D. S. Battisti, and G. Roe, 2012: Coupled air–mixed layer temperature predictability for climate reconstruction. J. Climate, 25, 459472, doi:10.1175/2011JCLI4094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, J. F., R. A. Weller, and R. Pinkel, 1986: Diurnal cycling: Observations and models of the upper ocean response to diurnal heating, cooling, and wind mixing. J. Geophys. Res., 91, 84118427, doi:10.1029/JC091iC07p08411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, J. F., T. Sanford, and G. Forristall, 1994: Forced stage response to a moving hurricane. J. Phys. Oceanogr., 24, 233260, doi:10.1175/1520-0485(1994)024<0233:FSRTAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, J. F., J. Morzel, and P. P. Niiler, 2008: Warming of SST in the cool wake of a moving hurricane. J. Geophys. Res., 113, C07010, doi:10.1029/2007JC004393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pun, I.-F., Y.-T. Chang, I.-I. Lin, T. Y. Tang, and R.-C. Lien, 2011: Typhoon-ocean interaction in the western North Pacific: Part 2. Oceanography, 24, 3241, doi:10.5670/oceanog.2011.92.

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

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

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saito, K., H. Seko, M. Kunii, and T. Miyoshi, 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11594, doi:10.3402/tellusa.v64i0.11594.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 11581181, doi:10.1175/WAF-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sekiyama, T. T., M. Kunii, M. Kajino, and T. Shimbori, 2015: Horizontal resolution dependence of atmospheric simulations of the Fukushima nuclear accident using 15-km, 3-km, and 500-m grid models. J. Meteor. Soc. Japan, 93, 4964, doi:10.2151/jmsj.2015-002.

    • Crossref
    • 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, 16631677, doi:10.1175//2555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tardif, R., G. J. Hakim, and C. Snyder, 2014: Coupled atmosphere-ocean data assimilation experiments with a low-order climate model. Climate Dyn., 43, 16311643, doi:10.1007/s00382-013-1989-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502, doi:10.1175/MWR3187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wada, A., and M. Kunii, 2014: Introduction of an atmosphere-wave-ocean coupled model into the NHM-LETKF. CAS/JSC WGNE Res. Activ. Atmos. Oceanic Modell., 44, 9.039.04.

    • Search Google Scholar
    • Export Citation
  • Wada, A., T. Uehara, and S. Ishizaki, 2014: Typhoon-induced sea surface cooling during the 2011 and 2012 typhoon seasons: Observational evidence and numerical investigations of the sea surface cooling effect using typhoon simulations. Prog. Earth Planet. Sci., 1, 11, doi:10.1186/2197-4284-1-11.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, doi:10.1175/MWR-D-11-00276.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, X., S. Zhang, Z. Liu, A. Rosati, T. Delworth, and Y. Liu, 2012: Impact of geographic-dependent parameter optimization on climate estimation and prediction: Simulation with an intermediate coupled model. Mon. Wea. Rev., 140, 39563971, doi:10.1175/MWR-D-11-00298.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 35413564, doi:10.1175/MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S., Z. Liu, A. Rosati, and T. Delworth, 2012: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. Tellus, 64A, 10963, doi:10.3402/tellusa.v64i0.10963.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Preliminary Test of a Data Assimilation System with a Regional High-Resolution Atmosphere–Ocean Coupled Model Based on an Ensemble Kalman Filter

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  • 1 Forecast Research Department, Meteorological Research Institute, Tsukuba, Japan
  • | 2 Forecast Research Department, Meteorological Research Institute, Tsukuba, and Department of Physics and Geosciences, University of the Ryukyus, Okinawa, Japan
  • | 3 Typhoon Research Department, Meteorological Research Institute, Tsukuba, Japan
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Abstract

An ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which time a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Masaru Kunii, mkunii@mri-jma.go.jp

Abstract

An ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which time a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Masaru Kunii, mkunii@mri-jma.go.jp
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