Editorial Type:
Article
Article Type:
Research Article

Skill Assessment of NCEP Three-Way Coupled HWRF–HYCOM–WW3 Modeling System: Hurricane Laura Case Study

Hyun-Sook Kim aIMSG at NOAA/NCEP/EMC, College Park, Maryland
bNOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

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Jessica Meixner cNOAA/Environmental Modeling Center, College Park, Maryland

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Biju Thomas aIMSG at NOAA/NCEP/EMC, College Park, Maryland
cNOAA/Environmental Modeling Center, College Park, Maryland

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Brandon G. Reichl dNOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Bin Liu aIMSG at NOAA/NCEP/EMC, College Park, Maryland
cNOAA/Environmental Modeling Center, College Park, Maryland

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Avichal Mehra cNOAA/Environmental Modeling Center, College Park, Maryland

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Alan Wallcraft eCenter for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida

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Online Publication:
19 Jul 2022
Print Publication:
01 Aug 2022
DOI:
https://doi.org/10.1175/WAF-D-21-0191.1
Page(s):
1309–1331
Restricted access

Abstract

In this research, we develop a three-way coupled prediction system to advance the realization of air–sea interaction processes. This study considers the sea-state-dependent momentum flux and nonlinear interactions between waves, winds, and ocean currents using the U.S. National Centers for Environmental Prediction’s operational Hurricane Weather Research and Forecasting (HWRF)-Hybrid Coordinate Ocean Model (HYCOM) coupled modeling system. Wave feedback is performed through the air–sea interaction module (ASIM) added to WAVEWATCH III (WW3), which employs the wave boundary layer to parameterize unresolved high-frequency tail spectra by using the mean flux profile constructed from the conservation of total momentum and wave energy. The atmospheric momentum flux is updated using the sea-state-dependent Charnock coefficient, wave-induced stress, and ocean surface currents before being passed to HYCOM. Wave coupling in HYCOM includes Coriolis–Stokes forcing to simulate wave–current interactions and to enhance mixing to account for Langmuir turbulence. The fully coupled system is tested for Hurricane Laura (2020). This paper examines the forecast skills of the individual component models by comparing simulations with observations. Without skill degradation of HYCOM and WW3, the three-way coupling method improves the track and intensity forecast skills by 5% each over those of HWRF-HYCOM coupling, and 27% and 17% over those of uncoupling, respectively. Importantly, this fully coupled system outperforms rapid intensification by reducing the intensification magnitude and matching the occurrence and duration. Overall, the forecast performance evaluated in the study establishes a baseline for the next-generation hurricane prediction system.

Significance Statement

This study is the documentation of the numerical advancement of tropical cyclone (TC) forecasting and the demonstration of the improvement of the TC intensity forecast. A key asset is the importance of wave coupling and inclusion of the nonlinear interactions in the air–sea interaction zone, and is to advance the current U.S. NCEP operational coupled hurricane modeling system. By assessing simulations for Hurricane Laura (2020), we demonstrate skill improvement of the storm structure, and intensity forecasts, especially for rapid intensification (RI) by correcting the timing and the magnitude of RI simulated by uncoupling and two-way coupling.

© 2022 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: Hyun-Sook Kim, hyun.sook.kim@noaa.gov

Abstract

In this research, we develop a three-way coupled prediction system to advance the realization of air–sea interaction processes. This study considers the sea-state-dependent momentum flux and nonlinear interactions between waves, winds, and ocean currents using the U.S. National Centers for Environmental Prediction’s operational Hurricane Weather Research and Forecasting (HWRF)-Hybrid Coordinate Ocean Model (HYCOM) coupled modeling system. Wave feedback is performed through the air–sea interaction module (ASIM) added to WAVEWATCH III (WW3), which employs the wave boundary layer to parameterize unresolved high-frequency tail spectra by using the mean flux profile constructed from the conservation of total momentum and wave energy. The atmospheric momentum flux is updated using the sea-state-dependent Charnock coefficient, wave-induced stress, and ocean surface currents before being passed to HYCOM. Wave coupling in HYCOM includes Coriolis–Stokes forcing to simulate wave–current interactions and to enhance mixing to account for Langmuir turbulence. The fully coupled system is tested for Hurricane Laura (2020). This paper examines the forecast skills of the individual component models by comparing simulations with observations. Without skill degradation of HYCOM and WW3, the three-way coupling method improves the track and intensity forecast skills by 5% each over those of HWRF-HYCOM coupling, and 27% and 17% over those of uncoupling, respectively. Importantly, this fully coupled system outperforms rapid intensification by reducing the intensification magnitude and matching the occurrence and duration. Overall, the forecast performance evaluated in the study establishes a baseline for the next-generation hurricane prediction system.

Significance Statement

This study is the documentation of the numerical advancement of tropical cyclone (TC) forecasting and the demonstration of the improvement of the TC intensity forecast. A key asset is the importance of wave coupling and inclusion of the nonlinear interactions in the air–sea interaction zone, and is to advance the current U.S. NCEP operational coupled hurricane modeling system. By assessing simulations for Hurricane Laura (2020), we demonstrate skill improvement of the storm structure, and intensity forecasts, especially for rapid intensification (RI) by correcting the timing and the magnitude of RI simulated by uncoupling and two-way coupling.

© 2022 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: Hyun-Sook Kim, hyun.sook.kim@noaa.gov
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  • Ali, A., K. H. Christensen, Ø. Breivik, M. Malila, R. P. Raj, L. Bertino, E. P. Chassignet, and M. Bakhoday-Paskyabi, 2019: A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans. Ocean Modell., 137, 76–97, https://doi.org/10.1016/j.ocemod.2019.02.005.

    • Search Google Scholar
    • Export Citation

  • Ardhuin, F., and Coauthors, 2010: Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation. J. Phys. Oceanogr., 40, 1917–1941, https://doi.org/10.1175/2010JPO4324.1.

    • Search Google Scholar
    • Export Citation

  • Banzon, V., T. M. Smith, T. M. Chin, C. Liu, and W. Hankins, 2016: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data, 8, 165–176, https://doi.org/10.5194/essd-8-165-2016.

    • Search Google Scholar
    • Export Citation

  • Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The operational GFDL coupled hurricane–ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135, 3965–3989, https://doi.org/10.1175/2007MWR2032.1.

    • Search Google Scholar
    • Export Citation

  • Benetazzo, A., S. Carniel, M. Sclavo, and A. Bergamasco, 2013: Wave–current interaction: Effect on the wave field in a semi-enclosed basin. Ocean Modell., 70, 152–165, https://doi.org/10.1016/j.ocemod.2012.12.009.

    • Search Google Scholar
    • Export Citation

  • Biswas, M. K., and Coauthors, 2018: 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.

  • Black, P. G., and Coauthors, 2007: Air–sea exchange in hurricanes: Synthesis of observations from the Coupled Boundary Layer Air–Sea Transfer Experiment. Bull. Amer. Meteor. Soc., 88, 357–374, https://doi.org/10.1175/BAMS-88-3-357.

    • Search Google Scholar
    • Export Citation

  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Modell., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9.

    • Search Google Scholar
    • Export Citation

  • Bleck, R., and D. B. Boudra, 1981: Initial testing of a numerical ocean circulation model using a hybrid (quasi-isopycnic) vertical coordinate. J. Phys. Oceanogr., 11, 755–770, https://doi.org/10.1175/1520-0485(1981)011<0755:ITOANO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brand, S., 1971: The effects on a tropical cyclone of cooler surface waters due to upwelling and mixing produced by a prior tropical cyclone. J. Appl. Meteor., 10, 865–874, https://doi.org/10.1175/1520-0450(1971)010<0865:TEOATC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bye, J. A. T., and J.-O. Wolff, 2008: Charnock dynamics: A model for the velocity structure in the wave boundary layer of the air–sea interface. Ocean Dyn., 58, 31–42, https://doi.org/10.1007/s10236-007-0130-5.

    • Search Google Scholar
    • Export Citation

  • Chao, Y. Y., J. H. G. Alves, and H. L. Tolman, 2005: An operational system for predicting hurricane-generated wind waves in the North Atlantic Ocean. Wea. Forecasting, 20, 652–671, https://doi.org/10.1175/WAF851.1.

    • Search Google Scholar
    • Export Citation

  • Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135027.

  • Chassignet, E. P., L. T. Smith, G. R. Halliwell, and R. Bleck, 2003: North Atlantic simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the vertical coordinate choice, reference pressure, and thermobaricity. J. Phys. Oceanogr., 33, 2504–2526, https://doi.org/10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Chen, S. S., J. F. Price, W. Zhao, M. A. Donelan, and E. J. Walsh, 2007: The CBLAST-Hurricane program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc., 88, 311–318, https://doi.org/10.1175/BAMS-88-3-311.

    • Search Google Scholar
    • Export Citation

  • Chen, S. S., W. Zhao, M. A. Donelan, and H. L. Tolman, 2013: Directional wind–wave coupling in fully coupled atmosphere–wave–ocean models: Results from CBLAST-Hurricane. J. Atmos. Sci., 70, 3198–3215, https://doi.org/10.1175/JAS-D-12-0157.1.

    • Search Google Scholar
    • Export Citation

  • Donelan, M. A., B. K. Haus, N. Reul, W. J. Plant, M. Stiassnie, H. C. Graber, O. B. Brown, and E. S. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, https://doi.org/10.1029/2004GL019460.

    • Search Google Scholar
    • Export Citation

  • Doyle, J. D., 2002: Coupled atmosphere–ocean wave simulations under high wind conditions. Mon. Wea. Rev., 130, 3087–3099, https://doi.org/10.1175/1520-0493(2002)130<3087:CAOWSU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Doyle, J. D., and Coauthors, 2012: Real-time tropical cyclone prediction using COAMPS-TC. Advances in Geosciences: Volume 28: Atmospheric Science (AS) and Ocean Science (OS), K. Satake, C.-C. Wu, J. Gan, Eds., World Scientific, 15–28.

    • Search Google Scholar
    • Export Citation

  • Doyle, J. D., and Coauthors, 2014: Tropical cyclone prediction using COAMPS-TC. Oceanography, 27, 104–115, https://doi.org/10.5670/oceanog.2014.72.

    • Search Google Scholar
    • Export Citation

  • Edson, J. B., and Coauthors, 2007: The Coupled Boundary Layers and Air-Sea Transfer Experiment in low winds. Bull. Amer. Meteor. Soc., 88, 341–356, https://doi.org/10.1175/BAMS-88-3-341.

    • Search Google Scholar
    • Export Citation

  • Edson, J. B., and Coauthors, 2013: On the exchange of momentum over the open ocean. J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/JPO-D-12-0173.1.

    • Search Google Scholar
    • Export Citation

  • Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ferrier, B. S., Y. Jin, Y. Lin, T. Black, E. Rogers, and G. DiMego, 2002: Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta model. 19th Conf. on Weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., 10.1, https://ams.confex.com/ams/SLS_WAF_NWP/techprogram/paper_47241.htm.

  • Gopalakrishnan S. G., Q. Liu, T. Marchok, D. Sheinin, N. Surgi, R. Tuleya, R. Yablonsky, and X. Zhang, 2011: Hurricane Weather Research and Forecasting (HWRF) Model: 2011 scientific documentation. NOAA/NCAR/Development Testbed Center, 81 pp.

    • Search Google Scholar
    • Export Citation

  • Hagos, S., and Coauthors, 2020: Atmospheric convection and air–sea interactions over the tropical oceans: Scientific progress, challenges, and opportunities. Bull. Amer. Meteor. Soc., 101, E253–E258, https://doi.org/10.1175/BAMS-D-19-0261.1.

    • Search Google Scholar
    • Export Citation

  • Halliwell, G. R., 2004: Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM). Ocean Modell., 7, 285–322, https://doi.org/10.1016/j.ocemod.2003.10.002.

    • Search Google Scholar
    • Export Citation

  • Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1.

    • Search Google Scholar
    • Export Citation

  • Hara, T., and S. E. Belcher, 2002: Wind forcing in the equilibrium range of wind-wave spectra. J. Fluid Mech., 470, 223–245, https://doi.org/10.1017/S0022112002001945.

    • Search Google Scholar
    • Export Citation

  • Harcourt, R. R., and E. A. D’Asaro, 2008: Large-eddy simulation of Langmuir turbulence in pure wind seas. J. Phys. Oceanogr., 38, 1542–1562, https://doi.org/10.1175/2007JPO3842.1.

    • Search Google Scholar
    • Export Citation

  • Hegermiller, C. A., J. C. Warner, M. Olabarrieta, and C. R. Sherwood, 2019: Wave–current interaction between Hurricane Matthew wave fields and the Gulf Stream. J. Phys. Oceanogr., 49, 2883–2900, https://doi.org/10.1175/JPO-D-19-0124.1.

    • Search Google Scholar
    • Export Citation

  • Holthuijsen, L. H., M. D. Powell, and J. D. Pietrzak, 2012: Wind and waves in extreme hurricanes. J. Geophys. Res., 117, C09003, https://doi.org/10.1029/2012JC007983.

    • Search Google Scholar
    • Export Citation

  • Hsu, J. Y., R. C. Lien, E. A. D’Asaro, and T. B. Sanford, 2017: Estimates of surface wind stress and drag coefficients in Typhoon Megi. J. Phys. Oceanogr., 47, 545–565, https://doi.org/10.1175/JPO-D-16-0069.1.

    • Search Google Scholar
    • Export Citation

  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., R. Gall, and M. E. Pyle, 2010: Scientific documentation for the NMM solver. NCAR Tech. Note NCAR/TN-477+STR, 54 pp, https://doi.org/10.5065/D6MW2F3Z.

  • Kim, H.-S., C. Lozano, V. Tallapragada, D. Iredell, D. Sheinin, H. L. Tolman, V. M. Gerald, and J. Sims, 2014: Performance of ocean simulations in the coupled HWRF–HYCOM model. J. Atmos. Oceanic Technol., 31, 545–559, https://doi.org/10.1175/JTECH-D-13-00013.1.

    • Search Google Scholar
    • Export Citation

  • Kudryavtsev, V. N., and V. K. Makin, 2001: The impact of air-flow separation on the drag of the sea surface. Bound.-Layer Meteor., 98, 155–171, https://doi.org/10.1023/A:1018719917275.

    • Search Google Scholar
    • Export Citation

  • Kurihara, Y., R. E. Tuleya, and M. A. Bender, 1998: The GFDL hurricane prediction system and its performance in the 1995 hurricane season. Mon. Wea. Rev., 126, 1306–1322, https://doi.org/10.1175/1520-0493(1998)126<1306:TGHPSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Large, W. G., J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872.

    • Search Google Scholar
    • Export Citation

  • Leipper, D. F., and D. Volgenau, 1972: Hurricane heat potential of the Gulf of Mexico. J. Phys. Oceanogr., 2, 218–224, https://doi.org/10.1175/1520-0485(1972)002<0218:HHPOTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Liu, B., H. Liu, L. Xie, C. Guan, and D. Zhao, 2011: A coupled atmosphere–wave–ocean modeling system: Simulation of the intensity of an idealized tropical cyclone. Mon. Wea. Rev., 139, 132–152, https://doi.org/10.1175/2010MWR3396.1.

    • Search Google Scholar
    • Export Citation

  • Mainelli, M., M. DeMaria, L. K. Shay, and G. Goni, 2008: Application of oceanic heat content estimation to operational forecasting of recent Atlantic category-5 hurricanes. Wea. Forecasting, 23, 3–16, https://doi.org/10.1175/2007WAF2006111.1.

    • Search Google Scholar
    • Export Citation

  • Makin, V. K., and V. N. Kudryavtsev, 2002: Impact of dominant waves on sea drag. Bound.-Layer Meteor., 103, 83–99, https://doi.org/10.1023/A:1014591222717.

    • Search Google Scholar
    • Export Citation

  • McWilliams, J. C., and P. P. Sullivan, 2000: Vertical mixing by Langmuir circulations. Spill Sci. Technol. Bull., 6, 225–237, https://doi.org/10.1016/S1353-2561(01)00041-X.

    • Search Google Scholar
    • Export Citation

  • Mehra, A., V. Tallapragada, Z. Zhang, B. Liu, L. Zhu, W. Wang, and H.-S. Kim, 2018: Advancing the state of the art in operational tropical cyclone forecasting at NCEP. Trop. Cyclone Res. Rev., 7, 51–56, https://doi.org/10.6057/2018TCRR01.06.

    • Search Google Scholar
    • Export Citation

  • Moon, I.-J., T. Hara, I. Ginis, S. E. Belcher, and H. Tolman, 2004a: Effect of surface waves on air–sea momentum exchange. Part I: Effect of mature and growing seas. J. Atmos. Sci., 61, 2321–2333, https://doi.org/10.1175/1520-0469(2004)061<2321:EOSWOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Moon, I.-J., I. Ginis, and T. Hara, 2004b: Effect of surface waves on air–sea momentum exchange. Part II: Behavior of the drag coefficient under tropical cyclones. J. Atmos. Sci., 61, 2334–2348, https://doi.org/10.1175/1520-0469(2004)061<2334:EOSWOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Morrison, A., and Coauthors, 2016: Model comparison for transatlantic ocean glider flight: Student analysis of modern circumnavigation. OCEANS 2015—MTS/IEEE Washington, Washington, DC, Institute of Electrical and Electronics Engineers, 1–7, https://doi.org/10.23919/OCEANS.2015.7404505.

  • Porchetta, S., O. Temel, J. C. Warner, D. Muñoz‐Esparza, J. Monbaliu, J. van Beeck, and N. van Lipzig, 2020: Evaluation of a roughness length parametrization accounting for wind-wave alignment in a coupled atmosphere-wave model. Quart. J. Roy. Meteor. Soc., 147, 825–846, https://doi.org/10.1002/qj.3948.

    • Search Google Scholar
    • Export Citation

  • Price, J. F., 2009: Metrics of hurricane-ocean interaction: Vertically-integrated or vertically-averaged ocean temperature? Ocean Sci., 5, 351–368, https://doi.org/10.5194/os-5-351-2009.

    • Search Google Scholar
    • Export Citation

  • Reichl, B., T. Hara, and I. Ginis, 2014: Sea state dependence of the wind stress over the ocean under hurricane winds. J. Geophys. Res. Oceans, 119, 30–51, https://doi.org/10.1002/2013JC009289.

    • Search Google Scholar
    • Export Citation

  • Reichl, B., D. Wang, T. Hara, I. Ginis, and T. Kukulka, 2016: Langmuir turbulence parameterization in tropical cyclone conditions. J. Phys. Oceanogr., 46, 863–886, https://doi.org/10.1175/JPO-D-15-0106.1.

    • Search Google Scholar
    • Export Citation

  • Renault, L., M. J. Molemaker, J. C. McWilliams, A. F. Shchepetkin, F. Lemarié, D. Chelton, S. Illig, and A. Hall, 2016: Modulation of wind work by oceanic current interaction with the atmosphere. J. Phys. Oceanogr., 46, 1685–1704, https://doi.org/10.1175/JPO-D-15-0232.1.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.

    • Search Google Scholar
    • Export Citation

  • Rogers, W. E., and A. J. Wallcraft, 2013: Develop and test coupled physical parameterizations and tripolar wave model grid: NAVGEM/WaveWatch III/HYCOM. NRL Rep. 2013-09-30, 9 pp., https://apps.dtic.mil/sti/citations/ADA601275.

  • Ryan, A. G., and Coauthors, 2015: GODAE OceanView Class 4 forecast verification framework: Global ocean inter-comparison. J. Oper. Oceanogr., 8, s98–s111, https://doi.org/10.1080/1755876X.2015.1022330.

    • Search Google Scholar
    • Export Citation

  • Sandery, P. A., G. B. Brassington, A. Craig, and T. Pugh, 2010: Impacts of ocean–atmosphere coupling on tropical cyclone intensity change and ocean prediction in the Australian region. Mon. Wea. Rev., 138, 2074–2091, https://doi.org/10.1175/2010MWR3101.1.

    • Search Google Scholar
    • Export Citation

  • Shay, L. K., and E. W. Uhlhorn, 2008: Loop current response to Hurricanes Isidore and Lili. Mon. Wea. Rev., 136, 3248–3274, https://doi.org/10.1175/2007MWR2169.1.

    • Search Google Scholar
    • Export Citation

  • Shay, L. K., P. G. Black, A. J. Mariano, J. D. Hawkins, and R. L. Elsberry, 1992: Upper ocean response to Hurricane Gilbert. J. Geophys. Res., 97, 20 227–20 248, https://doi.org/10.1029/92JC01586.

    • Search Google Scholar
    • Export Citation

  • Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 1366–1383, https://doi.org/10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Small, R. D., and Coauthors, 2008: Air–sea interaction over ocean fronts and eddies. Dyn. Atmos. Oceans, 45, 274–319, https://doi.org/10.1016/j.dynatmoce.2008.01.001.

    • Search Google Scholar
    • Export Citation

  • Smyth, W. D., E. D. Skyllingstad, G. B. Crawford, and H. Wijesekera, 2002: Nonlocal fluxes and Stokes drift effects in the K-profile parameterization. Ocean Dyn., 52, 104–115, https://doi.org/10.1007/s10236-002-0012-9.

    • Search Google Scholar
    • Export Citation

  • Song, X., 2021: The importance of including sea surface current when estimating air–sea turbulent heat fluxes and wind stress in the Gulf Stream region. J. Atmos. Oceanic Technol., 38, 119–138, https://doi.org/10.1175/JTECH-D-20-0094.1.

    • Search Google Scholar
    • Export Citation

  • Sweet, W., R. Fett, J. Kerling, and P. La Violette, 1981: Air–sea interaction effects in the lower troposphere across the north wall of the Gulf Stream. Mon. Wea. Rev., 109, 1042–1052, https://doi.org/10.1175/1520-0493(1981)109<1042:ASIEIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Takaya, Y., J.-R. Bidlot, A. C. M. Beljaars, and P. A. E. M. Janssen, 2010: Refinements to a prognostic scheme of skin sea surface temperature. J. Geophys. Res., 115, C06009, https://doi:10.1029/2009JC005985.

    • Search Google Scholar
    • Export Citation

  • Tallapragada, V., 2016: Overview of the NOAA/NCEP Operational Hurricane Weather Research and Forecast (HWRF) modelling system. Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction, U. Mohanty and S. G. Gopalakrishnan, Eds., Springer, 51–106.

    • Search Google Scholar
    • Export Citation

  • Tallapragada, V., and Coauthors, 2016: Hurricane Weather Research and Forecasting (HWRF) Model: 2015 scientific documentation. NCAR Tech. Note NCAR/TN-522+STR, 119 pp, https://doi.org/10.5065/D6ZP44B5.

  • Tolman, H. L., 2014: User manual and system documentation of WAVEWATCH III version 4.18. NOAA/NWS/NCEP/MMAB Tech. Note 316, NOAA, 194 pp.

  • Wallcraft, A. J., E. J. Metzger, and S. N. Carroll, 2009: Software design description for the hybrid coordinate ocean model (HYCOM), version 2.2. NRL/MR/7320-09-9166, NRL, 149 pp.

  • Wang, W., B. Liu, L. Zhu, Z. Zhang, A. Mehra, and V. Tallapragada, 2021: A new horizontal mixing-length formulation for numerical simulations of tropical cyclones. Wea. Forecasting, 36, 679–695, https://doi.org/10.1175/WAF-D-20-0134.1.

    • Search Google Scholar
    • Export Citation

  • Xu, X., J. J. Voermans, Q. Liu, I. J. Moon, C. Guan, and A. V. Babanin, 2021: Impacts of the wave-dependent sea spray parameterizations on air–sea–wave coupled modeling under an idealized tropical cyclone. J. Mar. Sci. Eng., 9, 1390, https://doi.org/10.3390/jmse9121390.

    • Search Google Scholar
    • Export Citation

  • Zambon, J. B., R. He, and J. C. Warner, 2014: Investigation of Hurricane Ivan using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) Model. Ocean Dyn., 64, 1535–1554, https://doi.org/10.1007/s10236-014-0777-7.

    • Search Google Scholar
    • Export Citation

  • Zambon, J. B., R. He, J. C. Warner, and C. A. Hegermiller, 2021: Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation. Wea. Forecasting, 36, 1713–1734, https://doi.org/10.1175/WAF-D-20-0171.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., and F. D. Marks, 2015: Effects of horizontal diffusion on tropical cyclone intensity change and structure in idealized three-dimensional numerical simulations. Mon. Wea. Rev., 143, 3981–3995, https://doi.org/10.1175/MWR-D-14-00341.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., R. F. Rogers, D. S. Nolan, and F. D. Marks, 2011: On the characteristic height scales of the hurricane boundary layer. Mon. Wea. Rev., 139, 2523–2535, https://doi.org/10.1175/MWR-D-10-05017.1.

    • 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, 3136–3155, https://doi.org/10.1175/MWR-D-14-00339.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, J. A., F. D. Marks, J. A. Sippel, R. F. Rogers, X. Zhang, S. G. Gopalakrishnan, Z. Zhang, and V. Tallapragada, 2018: Evaluating the impact of improvement in the horizontal diffusion parameterization on hurricane prediction in the operational Hurricane Weather Research and Forecast (HWRF) Model. Wea. Forecasting, 33, 317–329, https://doi.org/10.1175/WAF-D-17-0097.1.

    • Search Google Scholar
    • Export Citation
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