Relationship between the Track and Structural Evolution of Hurricane Sandy (2012) Using a Regional Ensemble

Alex M. Kowaleski Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Alex M. Kowaleski in
Current site
Google Scholar
PubMed
Close
and
Jenni L. Evans Department of Meteorology and Atmospheric Science, and Institute for CyberScience, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Jenni L. Evans in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

© 2018 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: Jenni L. Evans, jle7@psu.edu

Abstract

An ensemble of 72 Weather Research and Forecasting (WRF) Model simulations is evaluated to examine the relationship between the track of Hurricane Sandy (2012) and its structural evolution. Initial and boundary conditions are obtained from ECMWF and GEFS ensemble forecasts initialized at 0000 UTC 25 October. The 5-day WRF simulations are initialized at 0000 UTC 27 October, 48 h into the global model forecasts. Tracks and cyclone phase space (CPS) paths from the 72 simulations are partitioned into 6 clusters using regression mixture models; results from the 4 most populous track clusters are examined. The four analyzed clusters vary in mean landfall location from southern New Jersey to Maine. Extratropical transition timing is the clearest difference among clusters; more eastward clusters show later Sandy–midlatitude trough interaction, warm seclusion formation, and extratropical transition completion. However, the intercluster variability is much smaller when examined relative to the landfall time of each simulation. In each cluster, a short-lived warm seclusion forms and contracts through landfall while lower-tropospheric potential vorticity concentrates at small radii. Despite the large-scale similarity among the clusters, relevant intercluster differences in landfall-relative extratropical transition are observed. In the easternmost cluster the Sandy–trough interaction is least intense and the warm seclusion decays the most by landfall. In the second most eastward cluster Sandy retains the most intact warm seclusion at landfall because of a slightly later (relative to landfall) and weaker trough interaction compared to the two most westward clusters. Nevertheless, the remarkably similar large-scale evolution of Sandy among the four clusters indicates the high predictability of Sandy’s warm seclusion extratropical transition before landfall.

© 2018 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: Jenni L. Evans, jle7@psu.edu
Save
  • Arnott, J. M., J. L. Evans, and F. Chiaromonte, 2004: Characterization of extratropical transition using cluster analysis. Mon. Wea. Rev., 132, 29162937, https://doi.org/10.1175/MWR2836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blake, E. S., T. B. Kimberlain, R. J. Berg, J. P. Cangialosi, and J. L. Beven II, 2013: Tropical Cyclone Report Hurricane Sandy (22–29 October 2012). Tech. Rep. AL182012, National Hurricane Center, 157 pp., https://www.nhc.noaa.gov/data/tcr/AL182012_Sandy.pdf.

  • Demuth, J., M. DeMaria, and J. A. Knaff, 2006: Improvement of Advanced Microwave Sounder Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 15731581, https://doi.org/10.1175/JAM2429.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ECMWF, 2011: ECMWF’s Operational Model Analysis, starting in 2011. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 30 March 2018, https://doi.org/10.5065/D6ZG6Q9F.

    • Crossref
    • Export Citation
  • ECMWF, 2017: Changes in ECMWF model. ECMWF, accessed 17 March 2017, http://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model.

  • Evans, J. L., and R. E. Hart, 2003: Objective indicators of the life cycle evolution of extratropical transition for Atlantic tropical cyclones. Mon. Wea. Rev., 131, 909925, https://doi.org/10.1175/1520-0493(2003)131<0909:OIOTLC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaffney, S. J., A. W. Robertson, P. Smith, S. J. Camargo, and M. Ghil, 2007: Probabilistic clustering of extratropical cyclones using regression mixture models. Climate Dyn., 29, 423440, https://doi.org/10.1007/s00382-007-0235-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galarneau, T. J., C. A. Davis, and M. A. Shapiro, 2013: Intensification of Hurricane Sandy (2012) through extratropical warm core seclusion. Mon. Wea. Rev., 141, 42964321, https://doi.org/10.1175/MWR-D-13-00181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanley, D., J. Molinari, and D. Keyser, 2001: A composite study of the interactions between tropical cyclones and upper-tropospheric troughs. Mon. Wea. Rev., 129, 25702584, https://doi.org/10.1175/1520-0493(2001)129<2570:ACSOTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, R. E., 2003: A cyclone phase space derived from thermal wind and thermal asymmetry. Mon. Wea. Rev., 131, 585616, https://doi.org/10.1175/1520-0493(2003)131<0585:ACPSDF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, R. E., J. L. Evans, and C. Evans, 2006: Synoptic composites of the extratropical transition life cycle of North Atlantic tropical cyclones: Factors determining posttransition evolution. Mon. Wea. Rev., 134, 553578, https://doi.org/10.1175/MWR3082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B., 1997: A potential vorticity view of synoptic development. Meteor. Appl., 4, 325334, https://doi.org/10.1017/S1350482797000716.

  • Hoskins, B., and P. Berriford, 1988: A potential vorticity perspective of the storm of 15–16 October 1987. Weather, 43, 122129, https://doi.org/10.1002/j.1477-8696.1988.tb03890.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hubert, L., and P. Arabie, 1985: Comparing partitions. J. Classif., 2, 193218, https://doi.org/10.1007/BF01908075.

  • Kimball, S. K., and J. L. Evans, 2002: Idealized numerical simulations of hurricane–trough interaction. Mon. Wea. Rev., 130, 22102227, https://doi.org/10.1175/1520-0493(2002)130<2210:INSOHT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, P., P. A. Harr, and R. L. Elsberry, 2002: Extratropical transition of western North Pacific tropical cyclones: Midlatitude and tropical cyclone contributions to reintensification. Mon. Wea. Rev., 130, 22402259, https://doi.org/10.1175/1520-0493(2002)130<2240:ETOWNP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kofron, D. E., E. A. Ritchie, and J. S. Tyo, 2010: Determination of a consistent time for the extratropical transition of tropical cyclones. Part II: Potential vorticity metrics. Mon. Wea. Rev., 138, 43444361, https://doi.org/10.1175/2010MWR3181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kowaleski, A. M., and J. L. Evans, 2016: Regression mixture model clustering of multimodel ensemble forecasts of hurricane Sandy: Partition characteristics. Mon. Wea. Rev., 144, 38253846, https://doi.org/10.1175/MWR-D-16-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuruppumullage Don, P., J. L. Evans, F. Chiaromonte, and A. M. Kowaleski, 2016: Mixture-based path clustering for synthesis of ECMWF ensemble forecasts of tropical cyclone evolution. Mon. Wea. Rev., 144, 33013320, https://doi.org/10.1175/MWR-D-15-0214.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leroux, M.-D., M. Plu, and F. Roux, 2016: On the sensitivity of tropical cyclone intensification under upper-level trough forcing. Mon. Wea. Rev., 144, 11791202, https://doi.org/10.1175/MWR-D-15-0224.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magnusson, L., J.-R. Bidlon, S. T. K. Lang, A. Thorpe, N. Wedi, and M. Yamaguchi, 2014: Evaluation of medium-range forecasts for Hurricane Sandy. Mon. Wea. Rev., 142, 19621981, https://doi.org/10.1175/MWR-D-13-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchok, T. P., 2002: How the NCEP tropical cyclone tracker works. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., P1.13, https://ams.confex.com/ams/25HURR/techprogram/paper_37628.htm.

  • Milrad, S. M., E. A. Atallah, and J. R. Gyakum, 2009: Dynamical and precipitation structures of poleward-moving tropical cyclones in eastern Canada, 1979–2005. Mon. Wea. Rev., 137, 836851, https://doi.org/10.1175/2008MWR2578.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinari, J., S. Skubis, and D. Vollaro, 1995: External influences on hurricane intensity. Part III: Potential vorticity structure. J. Atmos. Sci., 52, 35933606, https://doi.org/10.1175/1520-0469(1995)052<3593:EIOHIP>2.0.CO;2.

    • 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
  • NCEI, 2018: Global Ensemble Forecast System (GEFS). NOAA/NCEI, accessed 30 March 2018, https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-ensemble-forecast-system-gefs.

  • Rand, W. M., 1971: Objective criteria for the evaluation of clustering methods. J. Amer. Stat. Assoc., 66, 846850, https://doi.org/10.1080/01621459.1971.10482356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ritchie, E. A., and R. L. Elsberry, 2003: Simulations of the extratropical transition of tropical cyclones: Contributions by the midlatitude upper-level trough to reintensification. Mon.Wea. Rev., 131, 21122128, https://doi.org/10.1175/1520-0493(2003)131,2112:SOTETO.2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ritchie, E. A., and R. L. Elsberry, 2007: Simulations of the extratropical transition of tropical cyclones: Phasing between the upper-level trough and tropical cyclones. Mon. Wea. Rev., 135, 862876, https://doi.org/10.1175/MWR3303.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • 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
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 325 86 6
PDF Downloads 282 80 7