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Uncertainty analysis using the WRF maximum likelihood ensemble filter system and comparison with dropwindsonde observations in Typhoon Sinlaku (2008)

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Abstract

In this study, the maximum likelihood ensemble filter (MLEF) is applied to a tropical cyclone case to identify the uncertainty areas in the context of targeting observations, using the WRF model. Typhoon Sinlaku (2008), from which dropwindsonde data are collected through THORPEX Pacific Asian Regional Campaign (TPARC), is selected for the case study. For the uncertainty analysis, a measurement called the deep layer mean (DLM) wind variance is employed. With assimilation of conventional rawinsonde data, the MLEF-WRF system demonstrated stable data assimilation performance over multiple data assimilation cycles and produced high uncertainties mostly in data-void areas, for the given tropical cyclone case. Dropwindsonde deployment through T-PARC turned out to occur inside or near the weak uncertainty areas that are identified through the MLEF-WRF system. The uncertainty analysis using the MLEF method can provide a guide for identifying more effective targeting observation areas.

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References

  • Aberson, S. D., 2003: Targeted observations to improve operational tropical cyclone track forecast guidance. Mon. Wea. Rev., 131, 1613–1628.

    Article  Google Scholar 

  • _____, and B. J. Etherton, 2006: Targeting and data assimilation studies during Hurricane Humberto (2001). J. Atmos. Sci., 63, 175–186.

    Article  Google Scholar 

  • _____, and J. L. Franklin, 1999: Impact on hurricane track and intensity forecasts of GPS dropwindsonde observations from the first-season flights of the NOAA Gulfstream-IV jet aircraft. Bull. Amer. Meteor. Soc., 80, 421–427.

    Article  Google Scholar 

  • Burpee, R. W., J. L. Franklin, S. J. Lord, R. E. Tuleya, and S. D. Aberson, 1996: The impact of Omega dropwindsondes on operational hurricane track forecast models. Bull. Amer. Meteor. Soc., 77, 925–933.

    Article  Google Scholar 

  • Chou, K.-H., and C.-C. Wu, 2008: Typhoon initialization in a mesoscale model-Combination of the bogused vortex and the dropwindsonde data in DOTSTAR. Mon. Wea. Rev., 136, 865–879.

    Article  Google Scholar 

  • Dee, D., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123, 1128–1145.

    Article  Google Scholar 

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143–10162.

    Article  Google Scholar 

  • Fourrié, N., D. Marchal, F. Rabier, and B. Chapnik, 2006: Impact study of the 2003 North Atlantic THORPEX Regional Campaign. Quart. J. Roy. Meteor. Soc., 132, 275–295.

    Article  Google Scholar 

  • Hong, S.-Y., J. Dudia, and S.-H. Chen, 2004: A revised approach to microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120.

    Article  Google Scholar 

  • Joly, A., and Coauthors, 1999: Overview of the field phase of the Fronts and Atlantic Storm-Track Experiment (FASTEX) project. Quart. J. Roy. Meteor. Soc., 125, 3131–3164.

    Article  Google Scholar 

  • Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Atmos. Sci., 43, 170–181.

    Google Scholar 

  • Langland, R. H., and Coauthors, 1999: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80, 1363–1384.

    Article  Google Scholar 

  • Le Dimet, F.-X. and O. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97–110.

    Article  Google Scholar 

  • Lewis, J. M., and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A, 309–322.

    Article  Google Scholar 

  • Lorenc, A. C., 1997 Development of an operational variational assimilation scheme. J. Meteor. Soc. Japan, 75(1B), 339–346.

    Google Scholar 

  • Majumdar, S. J., S. D. Aberson, C. H. Bishop, R. Buizza, M. S. Peng, and C. A. Reynolds, 2006: A comparison of adaptive observing guidance for Atlantic tropical cyclones. Mon. Wea. Rev., 134, 2354–2372.

    Article  Google Scholar 

  • Menard, R., S. E. Cohn, L.-P. Chang, and P. M. Lyster, 2000: Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part I: Formulation. Mon. Wea. Rev., 128, 2654–2671.

    Google Scholar 

  • NCAR, cited 2009: T-PARC. [Available online at http://www.eol.ucar.edu/deployment/field-deployments/field-projects/t-parc.]

  • Oczkowski M, I. Szunyogh, and D. J. Patil, 2005: Mechanism for the development of locally low-dimensional atmospheric dynamics. J. Atmos. Sci., 62, 1135–1156.

    Article  Google Scholar 

  • Ott, E., B. R. Hunt, I. Szunyogh, A.V. Zimin, E. J. Kostelich, M. Corazza, E. Kalnay, D.J. Patil, and J. A. Yorke, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 273–277.

    Google Scholar 

  • Park, S. K., and D. Zupanski, 2003: Four-dimensional variational data assimilation for mesoscale and storm-scale applications. Meteor. Atmos. Phys., 82, 173–208.

    Article  Google Scholar 

  • _____, and L. Xu, 2009: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer-Verlag, 475 pp.

  • _____, D.-L. Zhang, and H. H. Kim, 2008: Impact of dropwindsonde data on the track forecasts of a tropical cyclone: An observing-systems simulation experiment study. Asia-Pacific J. Atmos. Sci., 44, 85–92.

    Google Scholar 

  • Rabier, F., Thepaut, J. N. and Courtier, P. 1998: Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Meteor. Soc., 124, 1861–1887.

    Article  Google Scholar 

  • Reynolds, C. A., M. S. Peng, S. J. Majumdar, S. D. Aberson, C. H. Bishop, and R. Buizza, 2007: Interpretation of adaptive observing guidance for Atlantic tropical cyclones, Mon. Wea. Rev., 135, 4006–4029.

    Article  Google Scholar 

  • Rodgers, C. D., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific, 238 pp.

  • 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, 468+STR, National Center for Atmospheric Research, Boulder, CO, 88 pp. [Available at http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]

  • Tuleya, R. E., and S. J. Lord, 1997: The impact of dropwindsonde data on GFDL hurricane model forecasts using global analyses. Wea. Forecasting, 12, 307–323.

    Article  Google Scholar 

  • Wu, C.-C., and Coauthors, 2005: Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR): An overview. Bull. Amer. Meteor. Soc., 86, 787–790.

    Article  Google Scholar 

  • _____, K.-H. Chou, P.-H. Lin, S. D. Aberson, M. S. Peng, and T. Nakazawa, 2007: The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR. Wea. Forecasting, 22, 1157–1176.

    Article  Google Scholar 

  • Yang, S. C., E. Kalnay, B. Hunt, and N. Bowler, 2009: Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter. Quart. J. Roy. Meteor. Soc., 135, 251–262.

    Article  Google Scholar 

  • Zupanski D., 2009: Information measures in ensemble data assimilation. In: Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, Park, S. K., and L. Xu, Eds., Springer-Verlag, 85–95.

  • _____, and M. Zupanski, 2006: Model error estimation employing an ensemble data assimilation approach. Mon. Wea. Rev., 134, 1337–1354.

    Article  Google Scholar 

  • _____, A. Y. Hou, S. Q. Zhang, M. Zupanski, C. D. Kummerow, and S.H. Cheung, 2007: Applications of information theory in ensemble data assimilation. Quart. J. Roy. Meteor. Soc., 133, 1533–1545.

    Article  Google Scholar 

  • Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 1710–1726.

    Article  Google Scholar 

  • _____, I. M. Navon, and D. Zupanski, 2008a: The maximum likelihood ensemble filter as a non-differentiable minimization algorithm. Quart. J. Roy. Meteor. Soc., 134, 1039–1050.

    Article  Google Scholar 

  • _____, D. Zupanski, S. J. Fletcher, M. DeMaria, and R. Dumais, 2008b: Ensemble data assimilation with the Weather Research and Forecasting (WRF) model: The Hurricane Katrina case. J. Geophys. Res., submitted.

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Correspondence to Seon Ki Park.

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Kim, H.H., Park, S.K., Zupanski, D. et al. Uncertainty analysis using the WRF maximum likelihood ensemble filter system and comparison with dropwindsonde observations in Typhoon Sinlaku (2008). Asia-Pacific J Atmos Sci 46, 317–325 (2010). https://doi.org/10.1007/s13143-010-1004-1

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  • DOI: https://doi.org/10.1007/s13143-010-1004-1

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