• Avila, L. A., S. R. Stewart, R. Berg, and A. B. Hagen, 2020: National Hurricane Center tropical cyclone report: Hurricane Dorian (24 August–7 September 2019). NHC Tech. Rep. AL052019, 74 pp.

  • Beven, J. L., 1999: The boguscane—A serious problem with the NCEP medium range forecast model in the Tropics. Preprints, 23rd Conf. on Hurricanes and Tropical Meteorology, Dallas, TX, Amer. Meteor. Soc., 845–848.

  • Blake, E. S., 2019: National Hurricane Center tropical cyclone report: Tropical Storm Kirk (22–28 September 2018). NHC Tech. Rep. AL122018, 15 pp.

  • Briegel, L. M., and W. M. Frank, 1997: Large-scale influences on tropical cyclogenesis in the western North Pacific. Mon. Wea. Rev., 125, 13971413, https://doi.org/10.1175/1520-0493(1997)125<1397:LSIOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cangialosi, J. P., and N. A. Ramos, 2019: National Hurricane Center tropical cyclone report: Tropical Storm Karen (22–27 September 2019). NHC Tech. Rep. AL122019, 20 pp.

  • Chan, J. C., and R. H. Kwok, 1999: Tropical cyclone genesis in a global numerical weather prediction model. Mon. Wea. Rev., 127, 611624, https://doi.org/10.1175/1520-0493(1999)127<0611:TCGIAG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, K. T., and J. C. Chan, 2015: Global climatology of tropical cyclone size as inferred from QuikSCAT data. Int. J. Climatol., 35, 48434848, https://doi.org/10.1002/joc.4307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavas, D. R., and K. A. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 37, L18816, https://doi.org/10.1029/2010GL044558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J.-H., S.-J. Lin, L. Zhou, X. Chen, S. Rees, M. Bender, and M. Morin, 2019: Evaluation of tropical cyclone forecasts in the Next Generation Global Prediction System. Mon. Wea. Rev., 147, 34093428, https://doi.org/10.1175/MWR-D-18-0227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, K. K., and R. L. Elsberry, 2002: Tropical cyclone formations over the western North Pacific in the Navy Operational Global Atmospheric Prediction System forecasts. Wea. Forecasting, 17, 800820, https://doi.org/10.1175/1520-0434(2002)017<0800:TCFOTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corbosiero, K. L., and J. Molinari, 2002: The effects of vertical wind shear on the distribution of convection in tropical cyclones. Mon. Wea. Rev., 130, 21102123, https://doi.org/10.1175/1520-0493(2002)130<2110:TEOVWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cossuth, J. H., R. D. Knabb, D. P. Brown, and R. E. Hart, 2013: Tropical cyclone formation guidance using pregenesis Dvorak climatology. Part I: Operational forecasting and predictive potential. Wea. Forecasting, 28, 100118, https://doi.org/10.1175/WAF-D-12-00073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., W. Wang, J. Dudhia, and R. Torn, 2010: Does increased horizontal resolution improve hurricane wind forecasts? Wea. Forecasting, 25, 18261841, https://doi.org/10.1175/2010WAF2222423.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunion, J., 2017: NOAA JHT final report: Improvement to the Tropical Cyclone Genesis Index (TCGI). NOAA/NHC Tech. Rep., 16 pp.

  • Elsberry, R. L., H.-C. Tsai, and M. S. Jordan, 2014: Extended-range forecasts of Atlantic tropical cyclone events during 2012 using the ECMWF 32-day ensemble predictions. Wea. Forecasting, 29, 271288, https://doi.org/10.1175/WAF-D-13-00104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., R. F. Rogers, F. D. Marks, and D. S. Nolan, 2009: The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW model. Mon. Wea. Rev., 137, 37173743, https://doi.org/10.1175/2009MWR2946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fritz, C., and Z. Wang, 2013: A numerical study of the impacts of dry air on tropical cyclone formation: A development case and a nondevelopment case. J. Atmos. Sci., 70, 91111, https://doi.org/10.1175/JAS-D-12-018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S. G., F. Marks Jr., X. Zhang, J.-W. Bao, K.-S. Yeh, and R. Atlas, 2011: The experimental HWRF system: A study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Mon. Wea. Rev., 139, 17621784, https://doi.org/10.1175/2010MWR3535.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halperin, D. J., H. E. Fuelberg, R. E. Hart, J. H. Cossuth, P. Sura, and R. J. Pasch, 2013: An evaluation of tropical cyclone genesis forecasts from global numerical models. Wea. Forecasting, 28, 14231445, https://doi.org/10.1175/WAF-D-13-00008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halperin, D. J., H. E. Fuelberg, R. E. Hart, and J. H. Cossuth, 2016: Verification of tropical cyclone genesis forecasts from global numerical models: Comparisons between the North Atlantic and eastern North Pacific basins. Wea. Forecasting, 31, 947955, https://doi.org/10.1175/WAF-D-15-0157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halperin, D. J., R. E. Hart, H. E. Fuelberg, and J. H. Cossuth, 2017: The development and evaluation of a statistical–dynamical tropical cyclone genesis guidance tool. Wea. Forecasting, 32, 2746, https://doi.org/10.1175/WAF-D-16-0072.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarvinen, B., C. Neumann, and M. Davis, 1984: A tropical cyclone data tape for the North Atlantic basin, 1886–1983: Contents, limitations, and uses. NOAA Tech. Memo. NWS NHC-22, 24 pp., https://www.nhc.noaa.gov/pdf/NWS-NHC-1988-22.pdf.

  • Komaromi, W. A., and S. J. Majumdar, 2015: Ensemble-based error and predictability metrics associated with tropical cyclogenesis. Part II: Wave-relative framework. Mon. Wea. Rev., 143, 16651686, https://doi.org/10.1175/MWR-D-14-00286.1.

    • 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
  • Lee, C.-Y., S. J. Camargo, F. Vitart, A. H. Sobel, and M. K. Tippett, 2018: Subseasonal tropical cyclone genesis prediction and MJO in the S2S dataset. Wea. Forecasting, 33, 967988, https://doi.org/10.1175/WAF-D-17-0165.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McAdie, C., C. Landsea, C. Neumann, J. David, E. Blake, and G. Hammer, 2009: Tropical cyclones of the North Atlantic Ocean, 1851–2006. Historical Climatology Series 6-2, NCDC, 238 pp., https://www.nhc.noaa.gov/pdf/TC_Book_Atl_1851-2006_lowres.pdf.

  • McClung, T., 2012: Technical implementation notice 12-22, Amended. NOAA/NWS Tech. Rep., 4 pp., https://www.weather.gov/media/notification/tins/tin12-22gfs_hybridaab.pdf.

  • McClung, T., 2014: Technical implementation notice 14-46 Corrected. NOAA/NWS Tech. Rep., 8 pp., https://www.weather.gov/media/notification/tins/tin14-46gfs_cca.pdf.

  • McClung, T., 2016: Technical implementation notice 16-11, Amended. NOAA/NWS Tech. Rep., 6 pp., https://www.weather.gov/media/notification/tins/tin16-11gfs_gdasaaa.pdf.

  • McTaggart-Cowan, R., T. J. Galarneau Jr., L. F. Bosart, R. W. Moore, and O. Martius, 2013: A global climatology of baroclinically influenced tropical cyclogenesis. Mon. Wea. Rev., 141, 19631989, https://doi.org/10.1175/MWR-D-12-00186.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penny, A. B., J. P. Hacker, and P. A. Harr, 2016a: Analysis of tropical storm formation based on ensemble data assimilation and high-resolution numerical simulations of a nondeveloping disturbance. Mon. Wea. Rev., 144, 36313649, https://doi.org/10.1175/MWR-D-16-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penny, A. B., P. A. Harr, and J. D. Doyle, 2016b: Sensitivity to the representation of microphysical processes in numerical simulations during tropical storm formation. Mon. Wea. Rev., 144, 36113630, https://doi.org/10.1175/MWR-D-15-0259.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pratt, A. S., and J. L. Evans, 2009: Potential impacts of the Saharan air layer on numerical model forecasts of North Atlantic tropical cyclogenesis. Wea. Forecasting, 24, 420435, https://doi.org/10.1175/2008WAF2007090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappin, E. D., M. C. Morgan, and G. J. Tripoli, 2011: The impact of outflow environment on tropical cyclone intensification and structure. J. Atmos. Sci., 68, 177194, https://doi.org/10.1175/2009JAS2970.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601608, https://doi.org/10.1175/2008WAF2222159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryglicki, D. R., J. D. Doyle, Y. Jin, D. Hodyss, and J. H. Cossuth, 2018: The unexpected rapid intensification of tropical cyclones in moderate vertical wind shear. Part II: Vortex tilt. Mon. Wea. Rev., 146, 38013825, https://doi.org/10.1175/MWR-D-18-0021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and A. J. Schrader, 2000: The automated tropical cyclone forecasting system (version 3.2). Bull. Amer. Meteor. Soc., 81, 12311240, https://doi.org/10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, A. B., M. DeMaria, and J. A. Knaff, 2009: Objective estimation of the 24-h probability of tropical cyclone formation. Wea. Forecasting, 24, 456471, https://doi.org/10.1175/2008WAF2007109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsai, H.-C., and R. L. Elsberry, 2019: Combined three-stage 7-day weighted analog intensity prediction technique for western North Pacific tropical cyclones: Demonstration of optimum performance. Wea. Forecasting, 34, 19791998, https://doi.org/10.1175/WAF-D-19-0130.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsai, H.-C., K.-C. Lu, R. L. Elsberry, M.-M. Lu, and C.-H. Sui, 2011: Tropical cyclone-like vortices detection in the NCEP 16-day ensemble system over the western North Pacific in 2008: Application and forecast evaluation. Wea. Forecasting, 26, 7793, https://doi.org/10.1175/2010WAF2222415.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., W. Li, M. S. Peng, X. Jiang, R. McTaggart-Cowan, and C. A. Davis, 2018: Predictive skill and predictability of North Atlantic tropical cyclogenesis in different synoptic flow regimes. J. Atmos. Sci., 75, 361378, https://doi.org/10.1175/JAS-D-17-0094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • Yamaguchi, M., and N. Koide, 2017: Tropical cyclone genesis guidance using the early stage Dvorak analysis and global ensembles. Wea. Forecasting, 32, 21332141, https://doi.org/10.1175/WAF-D-17-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 62 62 48
Full Text Views 5 5 5
PDF Downloads 14 14 14

A Comparison of Tropical Cyclone Genesis Forecast Verification from Three Global Forecast System (GFS) Operational Configurations

View More View Less
  • 1 Department of Applied Aviation Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida
  • 2 National Hurricane Center, Miami, Florida, and CPAESS, University Corporation for Atmospheric Research, Boulder, Colorado
  • 3 Florida State University, Tallahassee, Florida
© Get Permissions
Restricted access

Abstract

Operational forecasting of tropical cyclone (TC) genesis has improved in recent years but still can be a challenge. Output from global numerical models continues to serve as a primary source of forecast guidance. Bulk verification statistics (e.g., critical success index) of TC genesis forecasts indicate that, overall, global models are increasingly able to predict TC genesis. However, as global model configurations are updated, TC genesis verification statistics will change. This study compares operational and retrospective forecasts from three configurations of NCEP’s Global Forecast System (GFS) to quantify the impact of model upgrades on TC genesis forecasts. First, bulk verification statistics from a homogeneous sample of model initialization cycles during the period 2013–14 are compared. Then, composites of select output fields are analyzed in an attempt to identify any key differences between hit and false alarm events. Bulk statistics indicate that TC genesis forecast performance decreased with the implementation of the 2015 version of the GFS, but then modestly recovered with the 2016 version of the model. In addition, the composite analysis suggests that false alarm forecasts in the 2015 version of the GFS may have been the result of inaccurately forecasting the location and/or strength of upper-level troughs poleward of the TC. There is also evidence of convective feedbacks occurring, such as ridging above the low-level circulation and upper-level convective outflow that were too strong, in this same set of false alarm forecasts. Overall, analyzing retrospective forecasts can assist forecasters in determining the strengths and weaknesses associated with a new configuration of a global model with respect to TC genesis.

Corresponding author: Daniel J. Halperin, daniel.halperin@erau.edu

Abstract

Operational forecasting of tropical cyclone (TC) genesis has improved in recent years but still can be a challenge. Output from global numerical models continues to serve as a primary source of forecast guidance. Bulk verification statistics (e.g., critical success index) of TC genesis forecasts indicate that, overall, global models are increasingly able to predict TC genesis. However, as global model configurations are updated, TC genesis verification statistics will change. This study compares operational and retrospective forecasts from three configurations of NCEP’s Global Forecast System (GFS) to quantify the impact of model upgrades on TC genesis forecasts. First, bulk verification statistics from a homogeneous sample of model initialization cycles during the period 2013–14 are compared. Then, composites of select output fields are analyzed in an attempt to identify any key differences between hit and false alarm events. Bulk statistics indicate that TC genesis forecast performance decreased with the implementation of the 2015 version of the GFS, but then modestly recovered with the 2016 version of the model. In addition, the composite analysis suggests that false alarm forecasts in the 2015 version of the GFS may have been the result of inaccurately forecasting the location and/or strength of upper-level troughs poleward of the TC. There is also evidence of convective feedbacks occurring, such as ridging above the low-level circulation and upper-level convective outflow that were too strong, in this same set of false alarm forecasts. Overall, analyzing retrospective forecasts can assist forecasters in determining the strengths and weaknesses associated with a new configuration of a global model with respect to TC genesis.

Corresponding author: Daniel J. Halperin, daniel.halperin@erau.edu
Save