• Aberson, S. D., and M. DeMaria, 1994: Verification of a nested barotropic hurricane track forecast model (VICBAR). Mon. Wea. Rev., 122, 28042815, https://doi.org/10.1175/1520-0493(1994)122<2804:VOANBH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B., and G. J. Hakim, 2007: Comparing adjoint and ensemble sensitivity analysis with applications to observation targeting. Mon. Wea. Rev., 135, 41174134, https://doi.org/10.1175/2007MWR1904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anwender, D., P. A. Harr, and S. C. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Case studies. Mon. Wea. Rev., 136, 32263247, https://doi.org/10.1175/2008MWR2249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Archambault, H. M., D. Keyser, L. F. Bosart, and J. M. Cordeira, 2013: A climatological analysis of the extratropical flow response to recurving western North Pacific tropical cyclones. Mon. Wea. Rev., 141, 23252346, https://doi.org/10.1175/MWR-D-12-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bassill, N. P., 2014: Accuracy of early GFS and ECMWF Sandy (2012) track forecasts: Evidence for a dependence on cumulus parameterization. Geophys. Res. Lett., 41, 32743281, https://doi.org/10.1002/2014GL059839.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, R., 2016: Hurricane Joaquin (AL112015) 28 September–7 October 2015. National Hurricane Center Tropical Cyclone Rep., 36 pp., https://www.nhc.noaa.gov/data/tcr/AL112015_Joaquin.pdf.

  • Blake, E. S., T. B. Kimberlain, R. J. Berg, J. P. Cangialosi, and J. L. Beven, 2013: Hurricane Sandy (AL182012) 22–29 October 2013. National Hurricane Center Tropical Cyclone Rep., 157 pp., http://www.nhc.noaa.gov/data/tcr/AL182012_Sandy.pdf.

  • Bougeault, P., and Coauthors, 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 10591072, https://doi.org/10.1175/2010BAMS2853.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carr, L. E., and R. L. Elsberry, 2000: Dynamical tropical cyclone track forecast errors. Part I: Tropical region error sources. Wea. Forecasting, 15, 641661, https://doi.org/10.1175/1520-0434(2000)015<0641:DTCTFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., and W. M. Gray, 1982: Tropical cyclone movement and surrounding flow relationships. Mon. Wea. Rev., 110, 13541374, https://doi.org/10.1175/1520-0493(1982)110<1354:TCMASF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J.-H., M. S. Peng, C. A. Reynolds, and C.-C. Wu, 2009: Interpretation of tropical cyclone forecast sensitivity from the singular vector perspective. J. Atmos. Sci., 66, 33833400, https://doi.org/10.1175/2009JAS3063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, K., and C. J. Neumann, 1986: The relationship between tropical cyclone motion and environmental geostrophic flows. Mon. Wea. Rev., 114, 115122, https://doi.org/10.1175/1520-0493(1986)114<0115:TRBTCM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galarneau, T. J., and C. A. Davis, 2013: Diagnosing forecast errors in tropical cyclone motion. Mon. Wea. Rev., 141, 405430, https://doi.org/10.1175/MWR-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • George, J. E., and W. M. Gray, 1976: Tropical cyclone motion and surrounding parameter relationships. J. Appl. Meteor., 15, 12521264, https://doi.org/10.1175/1520-0450(1976)015<1252:TCMASP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gombos, D., R. N. Hoffman, and J. A. Hansen, 2012: Ensemble statistics for diagnosing dynamics: Tropical cyclone track forecast sensitivities revealed by ensemble regression. Mon. Wea. Rev., 140, 26472669, https://doi.org/10.1175/MWR-D-11-00002.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grams, C. M., S. C. Jones, C. A. Davis, P. A. Harr, and M. Weissmann, 2013: The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part II: Downstream evolution. Quart. J. Roy. Meteor. Soc., 139, 21652180, https://doi.org/10.1002/qj.2119.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau, Y. Zhu, and W. Lapenta, 2013: NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull. Amer. Meteor. Soc., 94, 15531565, https://doi.org/10.1175/BAMS-D-12-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harr, P. A., D. Anwender, and S. C. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Methodology and a case study of Typhoon Nabi (2005). Mon. Wea. Rev., 136, 32053225, https://doi.org/10.1175/2008MWR2248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, J. M., G. M. Lackmann, and J. R. Gyakum, 1999: An analysis of Hurricane Opal’s forecast track errors using quasi-geostrophic potential vorticity inversion. Mon. Wea. Rev., 127, 292307, https://doi.org/10.1175/1520-0493(1999)127<0292:AAOHOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1983: Tropical cyclone motion: Environmental interaction plus beta effect. J. Atmos. Sci., 40, 328342, https://doi.org/10.1175/1520-0469(1983)040<0328:TCMEIP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoover, B. T., C. S. Velden, and S. J. Majumdar, 2013: Physical mechanisms underlying selected adaptive sampling techniques for tropical cyclones. Mon. Wea. Rev., 141, 40084027, https://doi.org/10.1175/MWR-D-12-00269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ido, K., and C.-C. Wu, 2013: Typhoon-position-oriented sensitivity analysis. Part I: Theory and verification. J. Atmos. Sci., 70, 25252546, https://doi.org/10.1175/JAS-D-12-0301.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kimberlain, T. B., 2013: Tropical Storm Debby (AL042012) 23–27 June 2012. National Hurricane Center Tropical Cyclone Rep., 51 pp., http://www.nhc.noaa.gov/data/tcr/AL042012_Debby.pdf.

  • Komaromi, W. A., S. J. Majumdar, and E. D. Rappin, 2011: Diagnosing initial condition sensitivity of Typhoon Sinlaku (2008) and Hurricane Ike (2008). Mon. Wea. Rev., 139, 32243242, https://doi.org/10.1175/MWR-D-10-05018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. P. Cangialosi, 2018: Have we reached the limits of predictability for tropical cyclone track forecasting? Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-17-0136.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 23542372, https://doi.org/10.1175/MWR3193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Munsell, E. B., and F. Zhang, 2014: Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations. J. Adv. Model. Earth Syst., 6, 3858, https://doi.org/10.1002/2013MS000297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nystrom, R. G., F. Zhang, E. B. Munsell, S. A. Braun, J. A. Sippel, Y. Weng, and K. Emanuel, 2018: Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. J. Atmos. Sci., 75, 401424, https://doi.org/10.1175/JAS-D-17-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, M. S., and C. A. Reynolds, 2006: Sensitivity of tropical cyclone forecasts as revealed by singular vectors. J. Atmos. Sci., 63, 25082528, https://doi.org/10.1175/JAS3777.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Podlaha, A., S. Bowen, C. Darbinyan, and M. Lorinc, 2016: Global catastrophe recap, October 2016. Aon Benfield Tech. Rep., 18 pp., http://thoughtleadership.aonbenfield.com/Documents/20161109-ab-analytics-if-october-global-recap.pdf.

  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419, https://doi.org/10.1175/2008WAF2222128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riemer, M., and S. C. Jones, 2014: Interaction of a tropical cyclone with a high-amplitude, midlatitude wave pattern: Waviness analysis, trough deformation and track bifurcation. Quart. J. Roy. Meteor. Soc., 140, 13621376, https://doi.org/10.1002/qj.2221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and G. J. Hakim, 2008: Ensemble-based sensitivity analysis. Mon. Wea. Rev., 136, 663677, https://doi.org/10.1175/2007MWR2132.1.

  • Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting, 27, 715729, https://doi.org/10.1175/WAF-D-11-00085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., J. S. Whitaker, P. Pegion, T. M. Hamill, and G. J. Hakim, 2015: Diagnosis of the source of GFS medium-range track errors in Hurricane Sandy (2012). Mon. Wea. Rev., 143, 132152, https://doi.org/10.1175/MWR-D-14-00086.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C., and L. M. Leslie, 1991: The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea. Forecasting, 6, 244253, https://doi.org/10.1175/1520-0434(1991)006<0244:TBRBTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., and K. A. Emanuel, 1995a: Potential vorticity diagnostics of hurricane movement. Part I: A case study of Hurricane Bob (1991). Mon. Wea. Rev., 123, 6992, https://doi.org/10.1175/1520-0493(1995)123<0069:PVDOHM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., and K. A. Emanuel, 1995b: Potential vorticity diagnostics of hurricane movement. Part II: Tropical Storm Ana (1991) and Hurricane Andrew (1992). Mon. Wea. Rev., 123, 93109, https://doi.org/10.1175/1520-0493(1995)123<0093:PVDOHM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., T.-S. Huang, and K.-H. Chou, 2004: Potential vorticity diagnosis of the key factors affection the motion of Typhoon Sinlaku (2002). Mon. Wea. Rev., 132, 20842093, https://doi.org/10.1175/1520-0493(2004)132<2084:PVDOTK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., and Coauthors, 2005: Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR): An overview. Bull. Amer. Meteor. Soc., 86, 787790, https://doi.org/10.1175/BAMS-86-6-787.

    • Search Google Scholar
    • Export Citation
  • Wu, C. C., J.-H. Chen, P.-H. Lin, and K.-H. Chou, 2007: Targeted observations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector. J. Atmos. Sci., 64, 26112626, https://doi.org/10.1175/JAS3974.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C. C., and Coauthors, 2009: Intercomparison of targeted observation guidance for tropical cyclones in the northwestern Pacific. Mon. Wea. Rev., 137, 24712492, https://doi.org/10.1175/2009MWR2762.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamaguchi, M., J. Ishida, H. Sato, and M. Nakagawa, 2017: WGNE intercomparison of tropical cyclone forecasts by operational NWP models: A quarter century and beyond. Bull. Amer. Meteor. Soc., 98, 23372349, https://doi.org/10.1175/BAMS-D-16-0133.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Tropical Cyclone Track Sensitivity in Deformation Steering Flow

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  • 1 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

Previous studies have suggested that tropical cyclones (TCs) in deformation steering flows can be associated with large position errors and uncertainty. The goal of this study is to evaluate the sensitivity of position forecasts for three TCs within deformation wind fields [Debby (2012), Joaquin (2015), and Lionrock (2016)] using the ensemble-based sensitivity technique applied to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. In all three cases, the position forecasts are sensitive to uncertainty in the steering wind within 500 km of the 0-h TC position. Subsequently, the TC moves onto either side of the axis of contraction due to the ensemble perturbation steering flow. As a TC moves away from the saddle point, the ensemble members subsequently experience different ensemble-mean steering winds, which act to move the TC away from the ensemble-mean TC position along the axis of dilatation. By contrast, the position forecasts appear to exhibit less sensitivity to the steering wind more than 500 km from the initial TC position, even though the TC may interact with these features later in the forecast. Furthermore, forecasts initialized at later times are characterized by significantly lower position errors and uncertainty once it becomes clear on which side of the axis of contraction the TC will move. These results suggest that TCs in deformation steering flow could be inherently unpredictable and may benefit from densely sampling the near-storm steering flow and TC structure early in their lifetimes.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Current affiliation: Naval Research Laboratory, Monterey, California.

Corresponding author: Ryan Torn, rtorn@albany.edu

Abstract

Previous studies have suggested that tropical cyclones (TCs) in deformation steering flows can be associated with large position errors and uncertainty. The goal of this study is to evaluate the sensitivity of position forecasts for three TCs within deformation wind fields [Debby (2012), Joaquin (2015), and Lionrock (2016)] using the ensemble-based sensitivity technique applied to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. In all three cases, the position forecasts are sensitive to uncertainty in the steering wind within 500 km of the 0-h TC position. Subsequently, the TC moves onto either side of the axis of contraction due to the ensemble perturbation steering flow. As a TC moves away from the saddle point, the ensemble members subsequently experience different ensemble-mean steering winds, which act to move the TC away from the ensemble-mean TC position along the axis of dilatation. By contrast, the position forecasts appear to exhibit less sensitivity to the steering wind more than 500 km from the initial TC position, even though the TC may interact with these features later in the forecast. Furthermore, forecasts initialized at later times are characterized by significantly lower position errors and uncertainty once it becomes clear on which side of the axis of contraction the TC will move. These results suggest that TCs in deformation steering flow could be inherently unpredictable and may benefit from densely sampling the near-storm steering flow and TC structure early in their lifetimes.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Current affiliation: Naval Research Laboratory, Monterey, California.

Corresponding author: Ryan Torn, rtorn@albany.edu
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