• Bélair, S., , Roch M. , , Leduc A.-M. , , Vaillancourt P. A. , , Laroche S. , , and Mailhot J. , 2009: Medium-range quantitative precipitation forecasts from Canada's new 33-km deterministic global operational system. Wea. Forecasting, 24, 690708.

    • Search Google Scholar
    • Export Citation
  • 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., 845848.

  • Braun, S. A., and Coauthors, 2013: NASA's Genesis and Rapid Intensification Processes (GRIP) field experiment. Bull. Amer. Meteor. Soc., 94, 345–363.

    • Search Google Scholar
    • Export Citation
  • Briegel, L. M., , and Frank W. M. , 1997: Large-scale influences on tropical cyclogenesis in the western North Pacific. Mon. Wea. Rev., 125, 13971413.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , Emanuel K. A. , , and Sobel A. H. , 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J. Climate, 20, 48194834.

    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., , and Kwok R. H. F. , 1999: Tropical cyclone genesis in a global numerical weather prediction model. Mon. Wea. Rev., 127, 611624.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., , and Eliassen A. , 1964: On the growth of the hurricane depression. J. Atmos. Sci., 21, 6875.

  • Charron, M., and Coauthors, 2012: The stratospheric extension of the Canadian Global Deterministic Medium-Range Weather Forecasting System and its impact on tropospheric forecasts. Mon. Wea. Rev., 140, 19241944.

    • Search Google Scholar
    • Export Citation
  • Cheung, K. W., , and Elsberry R. L. , 2002: Tropical cyclone formations over the western North Pacific in the Navy Operational Global Atmospheric Prediction System Forecasts. Wea. Forecasting, 17, 800820.

    • Search Google Scholar
    • Export Citation
  • COMET, cited 2012: NOGAPS 4.0 introduction. UCAR COMET Program. [Available online at http://www.meted.ucar.edu/nwp/pcu2/nogaps/index.htm.]

  • Côté, J., , Desmarais J. G. , , Gravel S. , , Méthot A. , , Patoine A. , , Roch M. , , and Staniforth A. , 1998a: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part II: Results. Mon. Wea. Rev., 126, 13971418.

    • Search Google Scholar
    • Export Citation
  • Côté, J., , Gravel S. , , Méthot A. , , Patoine A. , , Roch M. , , and Staniforth A. , 1998b: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 13731395.

    • Search Google Scholar
    • Export Citation
  • Craig, G. C., , and Gray S. L. , 1996: CISK or WISHE as the mechanism for tropical cyclone intensification. J. Atmos. Sci., 53, 35283540.

    • Search Google Scholar
    • Export Citation
  • Cullen, M. J. P., 1993: The Unified Forecast/Climate Model. Meteor. Mag., 122, 8194.

  • Dunkerton, T. J., , Montgomery M. T. , , and Wang Z. , 2008: Tropical cyclogenesis in a tropical wave critical layer: Easterly waves. Atmos. Chem. Phys., 8, 11 14911 292.

    • Search Google Scholar
    • Export Citation
  • ECMWF, cited 2012: ECMWF annual reports. [Available online at http://www.ecmwf.int/publications/annual_report/.]

  • Elsberry, R. L., , Clune W. M. , , and Harr P. A. , 2009: Evaluation of global model early track and formation prediction in the western North Pacific. Asia-Pac. J. Atmos. Sci., 45, 357374.

    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., , Jordan M. S. , , and Vitart F. , 2010: Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model. Asia-Pac. J. Atmos. Sci., 46, 135153.

    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., , Jordan M. S. , , and Vitart F. , 2011: Evaluation of the ECMWF 32-day ensemble predictions during 2009 season of western North Pacific tropical cyclone events on intraseasonal timescales. Asia-Pac. J. Atmos. Sci., 47, 305318.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585605.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 11431155.

  • Emanuel, K. A., , and Nolan D. S. , 2004: Tropical cyclones and the global climate system. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami Beach, FL, Amer. Meteor. Soc., 10A.1. [Available online at https://ams.confex.com/ams/pdfpapers/75463.pdf.]

  • Enagonio, J., , and Montgomery M. T. , 2001: Tropical cyclogenesis via convectively forced vortex Rossby waves in a shallow water primitive equation model. J. Atmos. Sci., 58, 685706.

    • Search Google Scholar
    • Export Citation
  • Environmental Modeling Center, cited 2012: GFS/GDAS changes since 1991. [Available online at http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html.]

  • Frank, W. M., , and Young G. S. , 2007: The interannual variability of tropical cyclones. Mon. Wea. Rev., 135, 35873598.

  • Goerss, J. S., 2000: Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev., 128, 11871193.

  • Goerss, J. S., , Sampson C. R. , , and Gross J. M. , 2004: A history of western North Pacific tropical cyclone track forecast skill. Wea. Forecasting, 19, 633638.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700.

  • Jarvinen, B. R., , Neumann C. J. , , and Davis M. A. S. , 1984: A tropical cyclone data tape for the North Atlantic basin, 1886–1983: Contents, limitations, and uses. NWS NHC Tech. Memo. 22, 24 pp.

  • Kanamitsu, M., 1989: Description of the NMC global data assimilation and forecast system. Wea. Forecasting, 4, 335342.

  • Marchok, T. P., 2002: How the NCEP tropical cyclone tracker works. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteo. Soc., P1.13. [Available online at https://ams.confex.com/ams/25HURR/webprogram/Paper37628.html.]

    • Search Google Scholar
    • Export Citation
  • McAdie, C. J., , Landsea C. W. , , Neumann C. J. , , David J. E. , , Blake E. , , and Hammer G. R. , 2009: Tropical cyclones of the North Atlantic Ocean, 1851–2006. Historical Climatology Series 6-2, National Climatic Data Center–NHC, 238 pp.

  • McTaggart-Cowan, R., , Deane G. D. , , Bosart L. F. , , Davis C. A. , , and Galarneau T. J. , 2008: Climatology of tropical cyclogenesis in the North Atlantic (1948–2004). Mon. Wea. Rev., 136, 12841304.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., , and Enagonio J. , 1998: Tropical cyclogenesis via convectively forced vortex Rossby waves in a three-dimensional quasigeostrophic model. J. Atmos. Sci., 55, 31763207.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., , Nguyen V. S. , , Persing J. , , and Smith R. K. , 2009: Do tropical cyclones intensify by WISHE? Quart. J. Roy. Meteor. Soc., 135, 16971714.

    • Search Google Scholar
    • Export Citation
  • Montgomery, M. T., and Coauthors, 2012: The Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) experiment: Scientific basis, new analysis tools, and some first results. Bull. Amer. Meteor. Soc., 93, 153172.

    • Search Google Scholar
    • Export Citation
  • NHC, cited 2011: NHC track and intensity models. [Available online at http://www.nhc.noaa.gov/modelsummary.shtml.]

  • NRL, cited 2012: Marine Meteorology Division history. [Available online at http://www.nrlmry.navy.mil/MMD_History/text/frame.htm.]

  • Pasch, R. J., , Harr P. A. , , Avila L. A. , , Jiing J. G. , , and Elliot G. , 2006: An evaluation and comparison of predictions of tropical cyclogenesis by three global forecast models. Preprints, 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., 14B.5. [Available online at https://ams.confex.com/ams/pdfpapers/108725.pdf.]

  • Pasch, R. J., , Blake E. S. , , Jiing J. G. , , Mainelli M. M. , , and Roberts D. P. , 2008: Performance of the GFS in predicting tropical cyclone genesis during 2007. Preprints, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 11A.7. [Available online at https://ams.confex.com/ams/28Hurricanes/techprogram/paper_138218.htm.]

  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419.

  • Ritchie, E. A., , and Holland G. J. , 1997: Scale interactions during the formation of Typhoon Irving. Mon. Wea. Rev., 125, 13771396.

  • Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601608.

  • Rogers, R., and Coauthors, 2006: The Intensity Forecasting Experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 15231537.

    • Search Google Scholar
    • Export Citation
  • Rosmond, T. E., 1992: The design and testing of the Navy Operational Global Atmospheric Prediction System. Wea. Forecasting, 7, 262272.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., , and Schrader A. J. , 2000: The Automated Tropical Cyclone Forecasting System (version 3.2). Bull. Amer. Meteor. Soc., 81, 12311240.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., , Franklin J. L. , , Knaff J. A. , , and DeMaria M. , 2008: Experiments with a simple tropical cyclone intensity consensus. Wea. Forecasting, 23, 304312.

    • Search Google Scholar
    • Export Citation
  • Shieh, O. H., , and Colucci S. J. , 2010: Local minimum of tropical cyclogenesis in the eastern Caribbean. Bull. Amer. Meteor. Soc., 91, 185196.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., , Ritchie E. , , Holland G. J. , , Halverson J. , , and Stewart S. , 1997: Mesoscale interactions in tropical cyclone genesis. Mon. Wea. Rev., 125, 26432661.

    • Search Google Scholar
    • Export Citation
  • Tory, K. J., , and Frank W. M. , 2010: Tropical cyclone formation. Global Perspectives on Tropical Cyclones: From Science to Mitigation, J. C. L. Chan and J. D. Kepert, Eds., World Scientific, 55–91.

  • Tsai, H.-C., , Lu K.-C. , , Elsberry R. L. , , Lu M.-M. , , and Sui C.-H. , 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.

    • Search Google Scholar
    • Export Citation
  • Vislocky, R. L., , and Fritsch J. M. , 1995: Improved model output statistics forecasts through model consensus. Bull. Amer. Meteor. Soc., 76, 11571164.

    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., , Fiorino M. , , Landsea C. W. , , and McInnes K. L. , 2007: Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J. Climate, 20, 23072314.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

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An Evaluation of Tropical Cyclone Genesis Forecasts from Global Numerical Models

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  • 1 The Florida State University, Tallahassee, Florida
  • 2 National Hurricane Center, Miami, Florida
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Abstract

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.

Corresponding author address: Daniel J. Halperin, Dept. of Earth, Ocean, and Atmospheric Science, The Florida State University, Tallahassee, FL 32306-4520. E-mail: dhalperin@fsu.edu

Abstract

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.

Corresponding author address: Daniel J. Halperin, Dept. of Earth, Ocean, and Atmospheric Science, The Florida State University, Tallahassee, FL 32306-4520. E-mail: dhalperin@fsu.edu
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