Validation of HWRF-Based Probabilistic TC Wind and Precipitation Forecasts

Kevin Bachmann aUniversity at Albany, State University of New York, Albany, New York

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Ryan D. Torn aUniversity at Albany, State University of New York, Albany, New York

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Abstract

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use in 2017–19. Wind and precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34-kt (1 kt ≈ 0.51 m s−1) wind forecasts have comparable skill to the global models up to 60-h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 and 64 kt throughout 120-h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in. (120 h)−1. Postprocessing, like quantile mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.

© 2021 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: Kevin Bachmann, kbachmann@albany.edu

Abstract

Tropical cyclones are associated with a variety of significant social hazards, including wind, rain, and storm surge. Despite this, most of the model validation effort has been directed toward track and intensity forecasts. In contrast, few studies have investigated the skill of state-of-the-art, high-resolution ensemble prediction systems in predicting associated TC hazards, which is crucial since TC position and intensity do not always correlate with the TC-related hazards, and can result in impacts far from the actual TC center. Furthermore, dynamic models can provide flow-dependent uncertainty estimates, which in turn can provide more specific guidance to forecasters than statistical uncertainty estimates based on past errors. This study validates probabilistic forecasts of wind speed and precipitation hazards derived from the HWRF ensemble prediction system and compares its skill to forecasts by the stochastically based operational Monte Carlo Model (NHC), the IFS (ECMWF), and the GEFS (NOAA) in use in 2017–19. Wind and precipitation forecasts are validated against NHC best track wind radii information and the National Stage IV QPE Product. The HWRF 34-kt (1 kt ≈ 0.51 m s−1) wind forecasts have comparable skill to the global models up to 60-h lead time before HWRF skill decreases, possibly due to detrimental impacts of large track errors. In contrast, HWRF has comparable quality to its competitors for higher thresholds of 50 and 64 kt throughout 120-h lead time. In terms of precipitation hazards, HWRF performs similar or better than global models, but depicts higher, although not perfect, reliability, especially for events over 5 in. (120 h)−1. Postprocessing, like quantile mapping, improves forecast skill for all models significantly and can alleviate reliability issues of the global models.

© 2021 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: Kevin Bachmann, kbachmann@albany.edu
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  • Alaka, G. J., X. Zhang, S. G. Gopalakrishnan, S. B. Goldenberg, and F. D. Marks, 2017: Performance of basin-scale HWRF tropical cyclone track forecasts. Wea. Forecasting, 32, 12531271, https://doi.org/10.1175/WAF-D-16-0150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alaka, G. J., X. Zhang, S. G. Gopalakrishnan, Z. Zhang, F. D. Marks, and R. Atlas, 2019: Track uncertainty in high-resolution HWRF ensemble forecasts of Hurricane Joaquin. Wea. Forecasting, 34, 18891908, https://doi.org/10.1175/WAF-D-19-0028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. J. Geer, P. Lopez, and D. Salmond, 2010: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 18681885, https://doi.org/10.1002/qj.659.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Biswas, M. K., and Coauthors, 2018a: Hurricane Weather Research and Forecasting (HWRF) Model: 2017 scientific documentation NCAR Tech. Note NCAR/TN-544+STR, 111 pp., https://doi.org/10.5065/D6MK6BPR.

    • Crossref
    • Export Citation
  • Biswas, M. K., and Coauthors, 2018b: Hurricane Weather Research and Forecasting (HWRF) Model: 2018 scientific documentation. Developmental Testbed Center, 112 pp., https://dtcenter.org/sites/default/files/community-code/hwrf/docs/scientific_documents/HWRFv4.0a_ScientificDoc.pdf.

  • Blake, E. S., 2018: The 2017 Atlantic hurricane season: Catastrophic losses and costs. Weatherwise, 71, 2837, https://doi.org/10.1080/00431672.2018.1448147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bröcker, J., and L. A. Smith, 2007: Increasing the reliability of reliability diagrams. Wea. Forecasting, 22, 651661, https://doi.org/10.1175/WAF993.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., J.-R. Bidlot, N. Wedi, M. Fuentes, M. Hamrud, G. Holt, and F. Vitart, 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Quart. J. Roy. Meteor. Soc., 133, 681695, https://doi.org/10.1002/qj.75.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cangialosi, J. P., and J. L. Franklin, 2017: National Hurricane Center Forecast Verification Report: 2016 Hurricane Season. NOAA/NHC, 72 pp., https://www.nhc.noaa.gov/verification/pdfs/Verification_2016.pdf.

  • Cangialosi, J. P., E. Blake, M. Demaria, A. Penny, A. Latto, E. Rappaport, and V. Tallapragada, 2020: Recent progress in tropical cyclone intensity forecasting at the National Hurricane Center. Wea. Forecasting, 35, 19131922, https://doi.org/10.1175/WAF-D-20-0059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF Model. Mon. Wea. Rev., 136, 19902005, https://doi.org/10.1175/2007MWR2085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., W. Wang, J. Dudhia, and R. D. 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
  • DeMaria, M., J. A. Knaff, R. Knabb, C. Lauer, C. R. Sampson, and R. T. DeMaria, 2009: A new method for estimating tropical cyclone wind speed probabilities. Wea. Forecasting, 24, 15731591, https://doi.org/10.1175/2009WAF2222286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., and Coauthors, 2013: Improvements to the operational tropical cyclone wind speed probability model. Wea. Forecasting, 28, 586602, https://doi.org/10.1175/WAF-D-12-00116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, J., and Coauthors, 2020: The evaluation of real-time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) model performance for the 2019 Atlantic hurricane season. Atmosphere, 11, 617, https://doi.org/10.3390/atmos11060617.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernández-Alvarez, J. C., A. Pérez-Alarcon, A. J. Batista-Leyva, and O. Díaz-Rodríguez, 2020: Evaluation of precipitation forecast of system: Numerical tools for hurricane forecast. Adv. Meteor., 2020, 8815949, https://doi.org/10.1155/2020/8815949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fulton, R. A., J. P. Breidenbach, D. J. Seo, D. A. Miller, and T. O’Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377395, https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goerss, J. S., 2007: Prediction of consensus tropical cyclone track forecast error. Mon. Wea. Rev., 135, 19851993, https://doi.org/10.1175/MWR3390.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2012: Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States. Mon. Wea. Rev., 140, 22322252, https://doi.org/10.1175/MWR-D-11-00220.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., E. Engle, D. Myrick, M. Peroutka, C. Finan, and M. Scheuerer, 2017: The U.S. national blend of models for statistical postprocessing of probability of precipitation and deterministic precipitation amount. Mon. Wea. Rev., 145, 34413463, https://doi.org/10.1175/MWR-D-16-0331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazelton, A. T., M. A. Bender, M. Morin, L. Harris, and S.-J. Lin, 2018: 2017 Atlantic hurricane forecasts from a high-resolution version of the GFDL fvGFS model: Evaluation of track, intensity, and structure. Wea. Forecasting, 33, 13171337, https://doi.org/10.1175/WAF-D-18-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazelton, A. T., X. Zhang, S. Gopalakrishnan, W. Ramstrom, F. D. Marks, and J. A. Zhang, 2020: High-resolution ensemble HFV3 forecasts of Hurricane Michael (2018): Rapid intensification in shear. Mon. Wea. Rev., 148, 20092032, https://doi.org/10.1175/MWR-D-19-0275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, C. Y., C. A. Chen, S. H. Chen, and D. S. Nolan, 2016: On the upstream track deflection of tropical cyclones past a mountain range: Idealized experiments. J. Atmos. Sci., 73, 31573180, https://doi.org/10.1175/JAS-D-15-0218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judt, F., 2018: Insights into atmospheric predictability through global convection-permitting model simulations. J. Atmos. Sci., 75, 14771497, https://doi.org/10.1175/JAS-D-17-0343.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judt, F., and S. S. Chen, 2010: Convectively generated potential vorticity in rainbands and formation of the secondary eyewall in Hurricane Rita of 2005. J. Atmos. Sci., 67, 35813599, https://doi.org/10.1175/2010JAS3471.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., 143, 452470, https://doi.org/10.1175/MWR-D-13-00350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., C. R. Sampson, M. DeMaria, T. P. Marchok, J. M. Gross, and C. J. McAdie, 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22, 781791, https://doi.org/10.1175/WAF1026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ko, M. C., F. D. Marks, G. J. Alaka, and S. G. Gopalakrishnan, 2020: Evaluation of Hurricane Harvey (2017) rainfall in deterministic and probabilistic HWRF forecasts. Atmosphere, 11, 666, https://doi.org/10.3390/atmos11060666.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kowaleski, A. M., R. E. Morss, D. Ahijevych, and K. R. Fossell, 2020: Using a WRF-ADCIRC ensemble and track clustering to investigate storm surge hazards and inundation scenarios associated with Hurricane Irma. Wea. Forecasting, 35, 12891315, https://doi.org/10.1175/WAF-D-19-0169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045, https://doi.org/10.1175/1520-0493(1993)121<2030:AISOHM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 27912801, https://doi.org/10.1175/1520-0493(1995)123<2791:IITGHP>2.0.CO;2.

    • 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., 99, 22372243, https://doi.org/10.1175/BAMS-D-17-0136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leonardo, N. M., and B. A. Colle, 2017: Verification of multimodel ensemble forecasts of North Atlantic tropical cyclones. Wea. Forecasting, 32, 20832101, https://doi.org/10.1175/WAF-D-17-0058.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q., S. J. Lord, N. Surgi, Y. Zhu, R. Wobus, Z. Toth, and T. P. Marchok, 2006: Hurricane relocation in Global Ensemble Forecast System. 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., P5.13, https://ams.confex.com/ams/pdfpapers/108503.pdf.

  • Magnusson, L., J.-R. Bidlot, S. T. 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
  • Majumdar, S. J., and P. M. Finocchio, 2010: On the ability of global ensemble prediction systems to predict tropical cyclone track probabilities. Wea. Forecasting, 25, 659680, https://doi.org/10.1175/2009WAF2222327.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., and R. D. Torn, 2014: Probabilistic verification of global and mesoscale ensemble forecasts of tropical cyclogenesis. Wea. Forecasting, 29, 11811198, https://doi.org/10.1175/WAF-D-14-00028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchok, T., R. Rogers, and R. Tuleya, 2007: Validation schemes for tropical cyclone quantitative precipitation forecasts: Evaluation of operational models for U.S. landfalling cases. Wea. Forecasting, 22, 726746, https://doi.org/10.1175/WAF1024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, F. D., N. Kurkowski, M. DeMaria, and M. Brennan, 2019: Hurricane Forecast Improvement Program Five-Year Plan: 2019–2024: Proposed framework for addressing section 104 of the Weather Research Forecasting Innovation Act of 2017. NOAA Tech. Rep., 83 pp., https://hfip.org/sites/default/files/documents/hfip-strategic-plan-20190625.pdf.

  • Mogensen, K. S., L. Magnusson, and J.-R. Bidlot, 2017: Tropical cyclone sensitivity to ocean coupling in the ECMWF coupled model. J. Geophys. Res. Oceans, 122, 43924412, https://doi.org/10.1002/2017JC012753.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohapatra, G. N., V. Rakesh, P. K. Mohanty, and S. Himesh, 2018: Comparative evaluation of the skill of a global circulation model and a limited area model in simulating tropical cyclones in the north Indian Ocean. Meteor. Appl., 25, 523533, https://doi.org/10.1002/met.1718.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., and R. L. Winkler, 1977: Reliability of subjective probability forecasts of precipitation and temperature. Appl. Stat., 26, 41, https://doi.org/10.2307/2346866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., and R. L. Winkler, 1987: A general framework for forecast verification. Mon. Wea. Rev., 115, 13301338, https://doi.org/10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, B. R., O. P. Prat, D. J. Seo, and E. Habib, 2016: Assessment and implications of NCEP stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371394, https://doi.org/10.1175/WAF-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., J. A. Zhang, and E. W. Uhlhorn, 2014: On the limits of estimating the maximum wind speeds in hurricanes. Mon. Wea. Rev., 142, 28142837, https://doi.org/10.1175/MWR-D-13-00337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peatman, S. C., N. P. Klingaman, and K. I. Hodges, 2019: Tropical cyclone–related precipitation over the northwest tropical Pacific in Met Office global operational forecasts. Wea. Forecasting, 34, 923941, https://doi.org/10.1175/WAF-D-19-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77–78, 5364, https://doi.org/10.1016/S0167-6105(98)00131-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qin, N., and D.-L. Zhang, 2018: On the extraordinary intensification of Hurricane Patricia (2015). Part I: Numerical experiments. Wea. Forecasting, 33, 12051224, https://doi.org/10.1175/WAF-D-18-0045.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiu, X., Z.-M. Tan, and Q. Xiao, 2010: The roles of vortex Rossby waves in hurricane secondary eyewall formation. Mon. Wea. Rev., 138, 20922109, https://doi.org/10.1175/2010MWR3161.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., 2014: Fatalities in the United States from Atlantic tropical cyclones: New data and interpretation. Bull. Amer. Meteor. Soc., 95, 341346, https://doi.org/10.1175/BAMS-D-12-00074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and B. W. Blanchard, 2016: Fatalities in the United States indirectly associated with Atlantic tropical cyclones. Bull. Amer. Meteor. Soc., 97, 11391148, https://doi.org/10.1175/BAMS-D-15-00042.1.

    • 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
  • Schwartz, C. S., 2019: Medium-range convection-allowing ensemble forecasts with a variable-resolution global model. Mon. Wea. Rev., 147, 29973023, https://doi.org/10.1175/MWR-D-18-0452.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and R. A. Sobash, 2017: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Mon. Wea. Rev., 145, 33973418, https://doi.org/10.1175/MWR-D-16-0400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280, https://doi.org/10.1175/2009WAF2222267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Selz, T., 2019: Estimating the intrinsic limit of predictability using a stochastic convection scheme. J. Atmos. Sci., 76, 757765, https://doi.org/10.1175/JAS-D-17-0373.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D. J., and J. P. Breidenbach, 2002: Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J. Hydrometeor., 3, 93111, https://doi.org/10.1175/1525-7541(2002)003<0093:RTCOSN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swinbank, R., and Coauthors, 2016: The TIGGE project and its achievements. Bull. Amer. Meteor. Soc., 97, 4967, https://doi.org/10.1175/BAMS-D-13-00191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Titley, H. A., R. L. Bowyer, and H. L. Cloke, 2020: A global evaluation of multi-model ensemble tropical cyclone track probability forecasts. Quart. J. Roy. Meteor. Soc., 146, 531545, https://doi.org/10.1002/qj.3712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2016: Evaluation of atmosphere and ocean initial condition uncertainty and stochastic exchange coefficients on ensemble tropical cyclone intensity forecasts. Mon. Wea. Rev., 144, 34873506, https://doi.org/10.1175/MWR-D-16-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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., and M. Demaria, 2021: Validation of ensemble-based probabilistic tropical cyclone intensity change. Atmosphere, 12, 373, https://doi.org/10.3390/atmos12030373.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trahan, S., and L. Sparling, 2012: An analysis of NCEP tropical cyclone vitals and potential effects on forecasting models. Wea. Forecasting, 27, 744756, https://doi.org/10.1175/WAF-D-11-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP global forecast system: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, https://doi.org/10.1175/MWR-D-12-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463482, https://doi.org/10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winkler, R. L., and A. H. Murphy, 1968: “Good” probability assessors. J. Appl. Meteor., 7, 751758, https://doi.org/10.1175/1520-0450(1968)007<0751:PA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wirtz, A., W. Kron, P. Löw, and M. Steuer, 2014: The need for data: Natural disasters and the challenges of database management. Nat. Hazards, 70, 135157, https://doi.org/10.1007/s11069-012-0312-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, J., Z.-M. Tan, and K.-C. Chow, 2019: Structure and formation of convection of secondary rainbands in a simulated typhoon Jangmi (2008). Meteor. Atmos. Phys., 131, 713737, https://doi.org/10.1007/s00703-018-0599-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., J. Schleif, F. Kong, K. W. Thomas, Y. Wang, and K. Zhu, 2013: Track and intensity forecasting of hurricanes: Impact of convection-permitting resolution and global ensemble Kalman filter analysis on 2010 Atlantic season forecasts. Wea. Forecasting, 28, 13661384, https://doi.org/10.1175/WAF-D-12-00063.1.

    • 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
  • Yan, H., and W. A. Gallus, 2016: An evaluation of QPF from the WRF, NAM, and GFS models using multiple verification methods over a small domain. Wea. Forecasting, 31, 13631379, https://doi.org/10.1175/WAF-D-16-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, H., P. Chen, Q. Li, and B. Tang, 2013: Current capability of operational numerical models in predicting tropical cyclone intensity in the western North Pacific. Wea. Forecasting, 28, 353367, https://doi.org/10.1175/WAF-D-11-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J. A., D. S. Nolan, R. F. Rogers, and V. Tallapragada, 2015: Evaluating the impact of improvements in the boundary layer parameterization on hurricane intensity and structure forecasts in HWRF. Mon. Wea. Rev., 143, 31363155, https://doi.org/10.1175/MWR-D-14-00339.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Z., V. Tallapragada, C. Kieu, S. Trahan, and W. Wang, 2014a: HWRF based ensemble prediction system using perturbations from GEFS and stochastic convective trigger function. Trop. Cyclone Res. Rev., 3, 145161, https://doi.org/10.6057/2014TCRR03.02.

    • Search Google Scholar
    • Export Citation
  • Zhang, Z., V. Tallapragada, C. Kieu, S. Trahan, and W. Wang, 2014b: HWRF based ensemble prediction system using perturbations from GEFS and stochastic convective trigger function. ESCAP/WMO Typhoon Committee (TC), 145–161.

  • Zick, S. E., and C. J. Matyas, 2016: A shape metric methodology for studying the evolving geometries of synoptic-scale precipitation patterns in tropical cyclones. Ann. Assoc. Amer. Geogr., 106, 12171235, https://doi.org/10.1080/24694452.2016.1206460.

    • Search Google Scholar
    • Export Citation
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