Editorial Type:
Article
Article Type:
Research Article

Assimilation of Tropical Cyclone Track and Wind Radius Data with an Ensemble Kalman Filter

Masaru Kunii Forecast Research Department, Meteorological Research Institute, Tsukuba, Ibaraki, Japan

Search for other papers by Masaru Kunii in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/WAF-D-14-00088.1
Page(s):
1050–1063
Restricted access

Abstract

Improving tropical cyclone (TC) forecasts is one of the most important issues in meteorology, but TC intensity forecasting is a challenging task. Because the lack of observations near TCs usually results in degraded accuracy of the initial fields, utilizing TC advisory data in data assimilation typically has started with an ensemble Kalman filter (EnKF). In this study, TC minimum sea level pressure (MSLP) and position information were directly assimilated using the EnKF, and the impacts of these observations were investigated by comparing different assimilation strategies. Another experiment with TC wind radius data was carried out to examine the influence of TC shape parameters. Sensitivity experiments indicated that the direct assimilation of TC MSLP and position data yielded results that were superior to those based on conventional assimilation of TC MSLP as a standard surface pressure observation. Assimilation of TC radius data modified the outer circulation of TCs closer to observations. The impacts of these TC parameters were also evaluated by using the case of Typhoon Talas in 2011. The TC MSLP, position, and wind radius data led to improved TC track forecasts and therefore to improved precipitation forecasts. These results imply that initialization with these TC-related observations benefits TC forecasting, offering promise for the prevention and mitigation of natural disasters caused by TCs.

Corresponding author address: Masaru Kunii, Forecast Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: mkunii@mri-jma.go.jp

Abstract

Improving tropical cyclone (TC) forecasts is one of the most important issues in meteorology, but TC intensity forecasting is a challenging task. Because the lack of observations near TCs usually results in degraded accuracy of the initial fields, utilizing TC advisory data in data assimilation typically has started with an ensemble Kalman filter (EnKF). In this study, TC minimum sea level pressure (MSLP) and position information were directly assimilated using the EnKF, and the impacts of these observations were investigated by comparing different assimilation strategies. Another experiment with TC wind radius data was carried out to examine the influence of TC shape parameters. Sensitivity experiments indicated that the direct assimilation of TC MSLP and position data yielded results that were superior to those based on conventional assimilation of TC MSLP as a standard surface pressure observation. Assimilation of TC radius data modified the outer circulation of TCs closer to observations. The impacts of these TC parameters were also evaluated by using the case of Typhoon Talas in 2011. The TC MSLP, position, and wind radius data led to improved TC track forecasts and therefore to improved precipitation forecasts. These results imply that initialization with these TC-related observations benefits TC forecasting, offering promise for the prevention and mitigation of natural disasters caused by TCs.

Corresponding author address: Masaru Kunii, Forecast Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: mkunii@mri-jma.go.jp
Save
Cover Weather and Forecasting
Weather and Forecasting

  • Chen, S. H., 2007: The impact of assimilating SSM/I and QuikSCAT satellite winds on Hurricane Isidore simulation. Mon. Wea. Rev., 135, 549–566, doi:10.1175/MWR3283.1.

    • Search Google Scholar
    • Export Citation

  • Chen, Y., and Snyder C. , 2007: Assimilating vortex position with an ensemble Kalman filter. Mon. Wea. Rev., 135, 1828–1845, doi:10.1175/MWR3351.1.

    • Search Google Scholar
    • Export Citation

  • Greybush, S. J., Wilson R. J. , Hoffman R. N. , Hoffman M. J. , Miyoshi T. , Ide K. , McConnochie T. , and Kalnay E. , 2012: Ensemble Kalman filter data assimilation of Thermal Emission Spectrometer (TES) temperature retrievals into a Mars GCM. J. Geophys. Res., 117, E11008, doi:10.1029/2012JE004097.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., Whitaker J. S. , Fiorino M. , and Benjamin S. G. , 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668–688, doi:10.1175/2010MWR3456.1.

    • Search Google Scholar
    • Export Citation

  • Hunt, B. R., and Coauthors, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273–277, doi:10.1111/j.1600-0870.2004.00066.x.

    • Search Google Scholar
    • Export Citation

  • Hunt, B. R., Kostelich E. J. , and Syzunogh I. , 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–126, doi:10.1016/j.physd.2006.11.008.

    • Search Google Scholar
    • Export Citation

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

  • Isaksen, L., and Janssen P. A. E. M. , 2004: Impact of ERS scatterometer winds in ECMWF’s assimilation system. Quart. J. Roy. Meteor. Soc., 130, 1793–1814, doi:10.1256/qj.03.110.

    • Search Google Scholar
    • Export Citation

  • Ito, K., Kuroda T. , Saito K. , and Wada A. , 2015: Forecasting a large number of tropical cyclone intensities around Japan using a high-resolution atmosphere–ocean coupled model. Wea. Forecasting, 30, 793–808, doi:10.1175/WAF-D-14-00034.1.

    • Search Google Scholar
    • Export Citation

  • Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng., 82, 35–45, doi:10.1115/1.3662552.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., 2010: Ensemble Kalman filter: Current status and potential. Data Assimilation: Making Sense of Observations, W. A. Lahoz, B. Khattatov, and R. Ménard, Eds., Springer, 69–92.

  • Kang, J.-S., Kalnay E. , Liu J. , Fung I. , Miyoshi T. , and Ide K. , 2011: “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.

    • Search Google Scholar
    • Export Citation

  • Kleist, D. T., 2011: Assimilation of tropical cyclone advisory minimum sea level pressure in the NCEP Global Data Assimilation System. Wea. Forecasting, 26, 1085–1091, doi:10.1175/WAF-D-11-00045.1.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., Brown D. P. , Courtney J. , Gallina G. M. , and Beven J. L. III, 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25, 1362–1379, doi:10.1175/2010WAF2222375.1.

    • Search Google Scholar
    • Export Citation

  • Kunii, M., 2014: Mesoscale data assimilation for a local severe rainfall event with the NHM–LETKF system. Wea. Forecasting, 29, 1093–1105, doi:10.1175/WAF-D-13-00032.1.

    • Search Google Scholar
    • Export Citation

  • Kunii, M., and Miyoshi T. , 2012: Including uncertainties of sea surface temperature in an ensemble Kalman filter: A case study of Typhoon Sinlaku (2008). Wea. Forecasting, 27, 1586–1597, doi:10.1175/WAF-D-11-00136.1.

    • Search Google Scholar
    • Export Citation

  • Kunii, M., Seko H. , Ueno M. , Shoji Y. , and Tsuda T. , 2012: Impact of assimilation of GPS radio occultation refractivity on the forecast of Typhoon Usagi in 2007. J. Meteor. Soc. Japan, 90, 255–273, doi:10.2151/jmsj.2012-207.

    • Search Google Scholar
    • Export Citation

  • Leslie, L. M., and Holland G. J. , 1995: On the bogussing of tropical cyclones in numerical models: A comparison of vortex profiles. Meteor. Atmos. Phys., 56, 101–110, doi:10.1007/BF01022523.

    • Search Google Scholar
    • Export Citation

  • Lorenc, A. C., 2003: The potential of the ensemble Kalman filter for NWP—A comparison with 4D-Var. Quart. J. Roy. Meteor. Soc., 129, 3183–3203, doi:10.1256/qj.02.132.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., 2011: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter. Mon. Wea. Rev., 139, 1519–1535, doi:10.1175/2010MWR3570.1.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., and Aranami K. , 2006: Applying a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM). SOLA, 2, 128–131, doi:10.2151/sola.2006-033.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., and Kunii M. , 2012a: The local ensemble transform Kalman filter with the Weather Research and Forecasting Model: Experiments with real observations. Pure Appl. Geophys., 169, 321–333, doi:10.1007/s00024-011-0373-4.

    • Search Google Scholar
    • Export Citation

  • Miyoshi, T., and Kunii M. , 2012b: Using AIRS retrievals in the WRF–LETKF system to improve regional numerical weather prediction. Tellus, 64A, 18 408. 64, doi:10.3402/tellusa.v64i0.

    • Search Google Scholar
    • Export Citation

  • Nakanishi, M., and Niino H. , 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 1–31, doi:10.1023/B:BOUN.0000020164.04146.98.

    • Search Google Scholar
    • Export Citation

  • Nakanishi, M., and Niino H. , 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397–407, doi:10.1007/s10546-005-9030-8.

    • Search Google Scholar
    • Export Citation

  • Ohmori, S., and Yamada Y. , 2006: Development of cumulus parameterization scheme in the nonhydrostatic mesoscale model at the Japan Meteorological Agency. Research Activities in Atmospheric and Oceanic Modeling (Blue Book), J. Côté, Ed., CAS/JSC WGNE, 4.21–4.22. [Available online at http://www.wcrp-climate.org/WGNE/BlueBook/2006/individual-articles/04_Ohmori_Shiro___12487.pdf.]

  • Park, K., and Zou X. , 2004: Toward developing an objective 4DVAR BDA scheme for hurricane initialization based on TPC observed parameters. Mon. Wea. Rev., 132, 2054–2069, doi:10.1175/1520-0493(2004)132<2054:TDAODB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Penny, S. G., Kalnay E. , Carton J. A. , Hunt B. R. , Ide K. , Miyoshi T. , and Chepurin G. A. , 2013: The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model. Nonlinear Processes Geophys., 20, 1031–1046, doi:10.5194/npg-20-1031-2013.

    • Search Google Scholar
    • Export Citation

  • Pu, Z.-X., and Braun S. A. , 2001: Evaluation of bogus vortex techniques with four-dimensional variational data assimilation. Mon. Wea. Rev., 129, 2023–2039, doi:10.1175/1520-0493(2001)129<2023:EOBVTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395–419, doi:10.1175/2008WAF2222128.1.

    • Search Google Scholar
    • Export Citation

  • Reale, O., Lau W. K. , Susskind J. , Brin E. , Liu E. , Riishojgaard L. P. , Fuentes M. , and Rosenberg R. , 2009: AIRS impact on the analysis and forecast track of Tropical Cyclone Nargis in a global data assimilation and forecast system. Geophys. Res. Lett., 36, L06812, doi:10.1029/2008GL037122.

    • 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.

  • Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 1266–1298, doi:10.1175/MWR3120.1.

    • Search Google Scholar
    • Export Citation

  • Saito, K., Ishida J. , Aranami K. , Hara T. , Segawa T. , Narita M. , and Honda Y. , 2007: Nonhydrostatic atmospheric models and operational development at JMA. J. Meteor. Soc. Japan, 85B, 271–304, doi:10.2151/jmsj.85B.271.

    • Search Google Scholar
    • Export Citation

  • Saito, K., Seko H. , Kunii M. , and Miyoshi T. , 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11 594, doi:10.3402/tellusa.v64i0.

    • Search Google Scholar
    • Export Citation

  • Shoji, Y., Kunii M. , and Saito K. , 2011: Mesoscale data assimilation of Myanmar Cyclone Nargis. Part II: Assimilation of GPS-derived precipitable water vapor. J. Meteor. Soc. Japan, 89, 67–88, doi:10.2151/jmsj.2011-105.

    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., Klemp J. B. , Dudhia J. , Gill D. O. , Barker D. M. , Wang W. , and Powers J. G. , 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note TN-468+STR, 88 pp. [Available online at http://www2.mmm.ucar.edu/wrf/users/docs/arw_v2_070111.pdf.]

  • Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA High-Resolution Hurricane test. Mon. Wea. Rev., 138, 4375–4392, doi:10.1175/2010MWR3361.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., and Hakim G. J. , 2009: Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817–2829, doi:10.1175/2009MWR2656.1.

    • Search Google Scholar
    • Export Citation

  • Torn, R. D., and Snyder C. , 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting, 27, 715–729, doi:10.1175/WAF-D-11-00085.1.

    • Search Google Scholar
    • Export Citation

  • Ueno, M., 1989: Operational bogussing and numerical prediction of typhoon in JMA. JMA/NPD Tech. Rep. 28, 48 pp.

  • Ueno, M., 1995: A study on the impact of asymmetric components around tropical cyclone center on the accuracy of bogus data and the track forecast. Meteor. Atmos. Phys., 56, 125–134, doi:10.1007/BF01022525.

    • Search Google Scholar
    • Export Citation

  • Wang, Y., 1998: On the bogusing of tropical cyclones in numerical models: The influence of vertical structure. Meteor. Atmos. Phys., 65, 153–170, doi:10.1007/BF01030785.

    • Search Google Scholar
    • Export Citation

  • Wu, C.-C., Chou K.-H. , Wang Y. , and Kuo Y.-H. , 2006: Tropical cyclone initialization and prediction based on four-dimensional variational data assimilation. J. Atmos. Sci., 63, 2383–2395, doi:10.1175/JAS3743.1.

    • Search Google Scholar
    • Export Citation

  • Wu, C.-C., Lien G.-Y. , Chen J.-H. , and Zhang F. , 2010: Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF). J. Atmos. Sci., 67, 3806–3822, doi:10.1175/2010JAS3444.1.

    • Search Google Scholar
    • Export Citation

  • Wu, T.-C., Liu H. , Majumdar S. J. , Velden C. S. , and Anderson J. L. , 2014: Influence of assimilating satellite-derived atmospheric motion vector observations on numerical analyses and forecasts of tropical cyclone track and intensity. Mon. Wea. Rev., 142, 49–71, doi:10.1175/MWR-D-13-00023.1.

    • Search Google Scholar
    • Export Citation

  • Xiao, Q., Zou X. , and Wang B. , 2000: Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme. Mon. Wea. Rev., 128, 2252–2269, doi:10.1175/1520-0493(2000)128<2252:IASOAL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Xiao, Q., Kuo Y.-H. , Zhang Y. , and Barker D. M. , 2006: A tropical cyclone bogus data assimilation scheme in the MM5 3D-Var system and numerical experiments with Typhoon Rusa (2002) near landfall. J. Meteor. Soc. Japan, 84, 671–689, doi:10.2151/jmsj.84.671.

    • Search Google Scholar
    • Export Citation

  • Xue, M., and Dong J. , 2013: Assimilating best track minimum sea level pressure data together with Doppler radar data using an ensemble Kalman filter for Hurricane Ike (2008) at a cloud-resolving resolution. Acta Meteor. Sin., 27, 379–399, doi:10.1007/s13351-013-0304-7.

    • Search Google Scholar
    • Export Citation

  • Yang, S.-C., Kalnay E. , and Miyoshi T. , 2012: Accelerating the EnKF spinup for typhoon assimilation and prediction. Wea. Forecasting, 27, 878–897, doi:10.1175/WAF-D-11-00153.1.

    • Search Google Scholar
    • Export Citation

  • Yang, S.-C., Lin K.-J. , Miyoshi T. , and Kalnay E. , 2013: Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008). Tellus, 65A, 20 804, doi:10.3402/tellusa.v65i0.20804.

    • Search Google Scholar
    • Export Citation

  • Zhang, F., Weng Y. , Sippel J. A. , Meng Z. , and Bishop C. H. , 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105–2125, doi:10.1175/2009MWR2645.1.

    • Search Google Scholar
    • Export Citation

  • Zou, X., and Xiao Q. , 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836–860, doi:10.1175/1520-0469(2000)057<0836:SOTIAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 252 70 9
PDF Downloads 169 49 9