A Description of the Real-Time HFIP Corrected Consensus Approach (HCCA) for Tropical Cyclone Track and Intensity Guidance

Anu Simon Cyberdata Technologies, Inc., Herndon, Virginia, and National Hurricane Center, Miami, Florida

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Andrew B. Penny Systems Research Group, Inc., Colorado Springs, Colorado, and National Hurricane Center, Miami, Florida

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Mark DeMaria National Hurricane Center, Miami, Florida

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James L. Franklin National Hurricane Center, Miami, Florida

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Richard J. Pasch National Hurricane Center, Miami, Florida

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Edward N. Rappaport National Hurricane Center, Miami, Florida

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David A. Zelinsky National Hurricane Center, Miami, Florida

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Abstract

This study discusses the development of the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA) for tropical cyclone track and intensity forecasts. The HCCA technique relies on the forecasts of separate input models for both track and intensity and assigns unequal weighting coefficients based on a set of training forecasts. The HCCA track and intensity forecasts for 2015 were competitive with some of the best-performing operational guidance at the National Hurricane Center (NHC); HCCA was the most skillful model for Atlantic track forecasts through 48 h. Average track input model coefficients for the 2015 forecasts in both the Atlantic and eastern North Pacific basins were largest for the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic model and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble mean, but the relative magnitudes of the intensity coefficients were more varied. Input model sensitivity experiments conducted using retrospective HCCA forecasts from 2011 to 2015 indicate that the ECMWF deterministic model had the largest positive impact on the skill of the HCCA track forecasts in both basins. The most important input models for HCCA intensity forecasts are the Hurricane Weather Research and Forecasting (HWRF) Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclone (COAMPS-TC) model initialized from the GFS. Several updates were incorporated into the HCCA formulation prior to the 2016 season. Verification results indicate HCCA continued to be a skillful model, especially for short-range (12–48 h) track forecasts in both basins.

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

Corresponding author: Anu Simon, anu.simon@noaa.gov

Abstract

This study discusses the development of the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA) for tropical cyclone track and intensity forecasts. The HCCA technique relies on the forecasts of separate input models for both track and intensity and assigns unequal weighting coefficients based on a set of training forecasts. The HCCA track and intensity forecasts for 2015 were competitive with some of the best-performing operational guidance at the National Hurricane Center (NHC); HCCA was the most skillful model for Atlantic track forecasts through 48 h. Average track input model coefficients for the 2015 forecasts in both the Atlantic and eastern North Pacific basins were largest for the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic model and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble mean, but the relative magnitudes of the intensity coefficients were more varied. Input model sensitivity experiments conducted using retrospective HCCA forecasts from 2011 to 2015 indicate that the ECMWF deterministic model had the largest positive impact on the skill of the HCCA track forecasts in both basins. The most important input models for HCCA intensity forecasts are the Hurricane Weather Research and Forecasting (HWRF) Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclone (COAMPS-TC) model initialized from the GFS. Several updates were incorporated into the HCCA formulation prior to the 2016 season. Verification results indicate HCCA continued to be a skillful model, especially for short-range (12–48 h) track forecasts in both basins.

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

Corresponding author: Anu Simon, anu.simon@noaa.gov
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  • Aberson, S., 1998: Five-day tropical cyclone track forecasts in the North Atlantic basin. Wea. Forecasting, 13, 10051015, https://doi.org/10.1175/1520-0434(1998)013<1005:FDTCTF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, R. J., 2016: Hurricane Joaquin. National Hurricane Center Tropical Cyclone Rep. AL112015, 36 pp., http://www.nhc.noaa.gov/data/tcr/AL112015_Joaquin.pdf.

  • Cane, D., and M. Milelli, 2010: Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region. Nat. Hazards Earth Syst. Sci., 10, 265273, https://doi.org/10.5194/nhess-10-265-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cangialosi, J. P., and J. L. Franklin, 2016: 2015 hurricane season. National Hurricane Center Forecast Verification Rep., 69 pp., http://www.nhc.noaa.gov/verification/pdfs/Verification_2015.pdf.

  • Cartwright, T. J., and T. N. Krishnamurti, 2007: Warm season mesoscale superensemble precipitation forecasts in the southeastern United States. Wea. Forecasting, 22, 873886, https://doi.org/10.1175/WAF1023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Development Testbed Center, 2015: MET: Version 5.1 Model Evaluation Tools users guide. DTC Rep., 316 pp., http://www.dtcenter.org/met/users/docs/users_guide/MET_Users_Guide_v5.1.pdf.

  • Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2013: The Hurricane Forecast Improvement Project. Bull. Amer. Meteor. Soc., 94, 329343, https://doi.org/10.1175/BAMS-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goerss, J. S., 2000: Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev., 128, 11871193, https://doi.org/10.1175/1520-0493(2000)128<1187:TCTFUA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S., and Coauthors, 2016: 2015 HFIP R&D activities summary: Recent results and operational implementation. Hurricane Forecast Improvement Project Tech. Rep. HFIP2016-1, NOAA/Hurricane Forecast Improvement Program, 44 pp., http://www.hfip.org/documents/HFIP_AnnualReport_FY2015.pdf.

  • Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea. Forecasting, 18, 10931108, https://doi.org/10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaplan, J., and Coauthors, 2015: Evaluating environmental impacts on tropical cyclone rapid intensification predictability utilizing statistical models. Wea. Forecasting, 30, 13741396, https://doi.org/10.1175/WAF-D-15-0032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., M. DeMaria, B. Sampson, and J. M. Gross, 2003: Statistical, 5-day tropical cyclone intensity forecasts derived from climatology and persistence. Wea. Forecasting, 18, 8092, https://doi.org/10.1175/1520-0434(2003)018<0080:SDTCIF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., 2003: Methods, systems and computer program products for generating weather forecasts from a multi-model superensemble. U.S. Patent 6535817 B1, filed 13 November 2000, issued 18 Mar 2003.

  • Krishnamurti, T. N., C. M. Kishtawal, T. LaRow, D. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285, 15481550, https://doi.org/10.1126/science.285.5433.1548.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, D. W. Shin, and C. E. Williford, 2000a: Improving tropical precipitation forecasts from a multianalysis superensemble. J. Climate, 13, 42174227, https://doi.org/10.1175/1520-0442(2000)013<4217:ITPFFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, C. E. Williford, S. Gadgil, and S. Surendran, 2000b: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13, 41964216, https://doi.org/10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., and Coauthors, 2001: Real-time multianalysis–multimodel superensemble forecasts of precipitation using TRMM and SSM/I products. Mon. Wea. Rev., 129, 28612883, https://doi.org/10.1175/1520-0493(2001)129<2861:RTMMSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., S. Pattnaik, M. K. Biswas, E. Bensman, M. Kramer, N. Surgi, and T. S. V. V. Kumar, 2010: Hurricane forecasts with a mesoscale suite of models. Tellus, 62A, 633646, https://doi.org/10.1111/j.1600-0870.2010.00469.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., M. K. Biswas, B. P. Mackey, R. G. Ellingson, and P. H. Ruscher, 2011: Hurricane forecasts using a suite of large-scale models. Tellus, 63A, 727745, https://doi.org/10.1111/j.1600-0870.2011.00519.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neumann, C. J., 1972: An alternate to the Hurran tropical cyclone forecast system. NOAA Tech. Memo. NWS SR-62, 23 pp., https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/COM7210351.xhtml.

  • NHC, 2017: NHC track and intensity models. National Hurricane Center, http://www.nhc.noaa.gov/aboutmodels.shtml.

  • Williford, C. E., T. N. Krishnamurti, R. C. Torres, S. Cocke, Z. Christidis, and T. S. V. Kumar, 2003: Real-time multimodel superensemble forecasts of Atlantic tropical systems of 1999. Mon. Wea. Rev., 131, 18781894, https://doi.org/10.1175//2571.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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