• Aryal, Y. N., G. Villarini, W. Zhang, and G. A. Vecchi, 2018: Long term changes in flooding and heavy rainfall associated with North Atlantic tropical cyclones: Roles of the North Atlantic oscillation and El Niño-Southern oscillation. J. Hydrol., 559, 698710, https://doi.org/10.1016/j.jhydrol.2018.02.072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, S. A., and L. Wu, 2007: A numerical study of Hurricane Erin (2001). Part II: Shear and the organization of eyewall vertical motion. Mon. Wea. Rev., 135, 11791194, https://doi.org/10.1175/MWR3336.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavas, D. R., N. Lin, and K. Emanuel, 2015: A model for the complete radial structure of the tropical cyclone wind field. Part I: Comparison with observed structure. J. Atmos. Sci., 72, 36473662, https://doi.org/10.1175/JAS-D-15-0014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, K. K. W., L.-R. Huang, and C.-S. Lee, 2008: Characteristics of rainfall during tropical cyclone periods in Taiwan. Nat. Hazards Earth Syst. Sci., 8, 14631474, https://doi.org/10.5194/nhess-8-1463-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2017: Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc. Natl. Acad. Sci. USA, 114, 12 68112 684, https://doi.org/10.1073/pnas.1716222114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2021: Response of global tropical cyclone activity to increasing CO2: Results from downscaling CMIP6 models. J. Climate, 34, 5770, https://doi.org/10.1175/JCLI-D-20-0367.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347367, https://doi.org/10.1175/BAMS-89-3-347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feldmann, M., K. Emanuel, L. Zhu, and U. Lohmann, 2019: Estimation of Atlantic tropical cyclone rainfall frequency in the United States. J. Appl. Meteor. Climatol., 58, 18531866, https://doi.org/10.1175/JAMC-D-19-0011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gori, A., N. Lin, and D. Xi, 2020a: Tropical cyclone compound flood hazard assessment: From investigating drivers to quantifying extreme water levels. Earth’s Future, 8, e2020EF001660, https://doi.org/10.1029/2020EF001660.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gori, A., N. Lin, and J. Smith, 2020b: Assessing compound flooding from landfalling tropical cyclones on the North Carolina coast. Water Resour. Res., 56, e2019WR026788, https://doi.org/10.1029/2019WR026788.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gori, A., N. Lin, D. Xi, and K. Emanuel, 2022: Tropical cyclone climatology change greatly exacerbates US extreme rainfall–surge hazard. Nat. Climate Change, 12, 171178, https://doi.org/10.1038/s41558-021-01272-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, T. M., and J. P. Kossin, 2019: Hurricane stalling along the North American coast and implications for rainfall. NPJ Climate Atmos. Sci., 2, 17, https://doi.org/10.1038/s41612-019-0074-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2010: Clouds in tropical cyclones. Mon. Wea. Rev., 138, 293344, https://doi.org/10.1175/2009MWR2989.1.

  • Irish, J. L., D. T. Resio, and M. A. Cialone, 2009: A surge response function approach to coastal hazard assessment. Part 2: quantification of spatial attributes of response functions. Nat. Hazards, 51, 183205, https://doi.org/10.1007/s11069-009-9381-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jing, R., and N. Lin, 2020: An environment-dependent probabilistic tropical cyclone model. J. Adv. Model. Earth Syst., 12, e2019MS001975, https://doi.org/10.1029/2019MS001975.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101, E303E322, https://doi.org/10.1175/BAMS-D-18-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., 2018: A global slowdown of tropical-cyclone translation speed. Nature, 558, 104107, https://doi.org/10.1038/s41586-018-0158-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kroese, D. P., and R. Y. Rubinstein, 2016: Simulation and the Monte Carlo Method. John Wiley & Sons, 94 pp.

  • Lee, C.-Y., M. K. Tippett, A. H. Sobel, and S. J. Camargo, 2018: An environmentally forced tropical cyclone hazard model. J. Adv. Model. Earth Syst., 10, 223241, https://doi.org/10.1002/2017MS001186.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, N., K. Emanuel, M. Oppenheimer, and E. Vanmarcke, 2012: Physically based assessment of hurricane surge threat under climate change. Nat. Climate Change, 2, 462467, https://doi.org/10.1038/nclimate1389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.

    • Search Google Scholar
    • Export Citation
  • Liu, M., G. A. Vecchi, J. A. Smith, and H. Murakami, 2018: Projection of landfalling–tropical cyclone rainfall in the eastern United States under anthropogenic warming. J. Climate, 31, 72697286, https://doi.org/10.1175/JCLI-D-17-0747.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, P., N. Lin, K. Emanuel, D. Chavas, and J. Smith, 2018: Assessing hurricane rainfall mechanisms using a physics-based model: Hurricanes Isabel (2003) and Irene (2011). J. Atmos. Sci., 75, 23372358, https://doi.org/10.1175/JAS-D-17-0264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luitel, B., G. Villarini, and G. A. Vecchi, 2018: Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones. J. Hydrol., 556, 10261037, https://doi.org/10.1016/j.jhydrol.2016.09.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsooli, R., N. Lin, K. Emanuel, and K. Feng, 2019: Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns. Nat. Commun., 10, 3785, https://doi.org/10.1038/s41467-019-11755-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Resio, D., J. Irish, and M. Cialone, 2009: A surge response function approach to coastal hazard assessment – Part 1: Basic concepts. Nat. Hazards, 51, 163182, https://doi.org/10.1007/s11069-009-9379-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodgers, E. B., and R. F. Adler, 1981: Tropical cyclone rainfall characteristics as determined from a satellite passive microwave radiometer. Mon. Wea. Rev., 109, 506521, https://doi.org/10.1175/1520-0493(1981)109<0506:TCRCAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shapiro, L. J., 1983: The asymmetric boundary layer under a translating hurricane. J. Atmos. Sci., 40, 19841998, https://doi.org/10.1175/1520-0469(1983)040<1984:TABLFU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Studholme, J., A. V. Fedorov, S. K. Gulev, K. Emanuel, and K. Hodges, 2021: Poleward expansion of tropical cyclone latitudes in warming climates. Nat. Geosci., 15, 1428, https://doi.org/10.1038/s41561-021-00859-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tamarin-Brodsky, T., and Y. Kaspi, 2017: Enhanced poleward propagation of storms under climate change. Nat. Geosci., 10, 908913, https://doi.org/10.1038/s41561-017-0001-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tishbirani, R., 1996: Regression shrinkage and selection via the LASSO. J. Roy. Stat. Soc., 58B, 267288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.

    • Search Google Scholar
    • Export Citation
  • Toro, G. R., D. T. Resio, D. Divoky, A. W. Niedoroda, and C. Reed, 2010: Efficient joint-probability methods for hurricane surge frequency analysis. Ocean Eng., 37, 125134, https://doi.org/10.1016/j.oceaneng.2009.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuleya, R. E., M. DeMaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for US landfalling tropical storms. Wea. Forecasting, 22, 5670, https://doi.org/10.1175/WAF972.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., J. A. Smith, M. L. Baeck, T. Marchok, and G. A. Vecchi, 2011: Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004). J. Geophys. Res., 116, D23116, https://doi.org/10.1029/2011JD016175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., W. Zhang, P. Miller, D. Johnson, L. Grimley, and H. Roberts, 2021: Probabilistic rainfall generator for tropical cyclones affecting Louisiana. Int. J. Climatol., 42, 17891802, https://doi.org/10.1002/joc.7335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., and R. Toumi, 2021: Recent migration of tropical cyclones toward coasts. Science, 371, 514517, https://doi.org/10.1126/science.abb9038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., F. D. Marks, and R. J. Feinberg, 1984: Stationary and moving convective bands in hurricanes. J. Atmos. Sci., 41, 31893211, https://doi.org/10.1175/1520-0469(1984)041<3189:SAMCBI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wright, D. B., T. R. Knutson, and J. A. Smith, 2015: Regional climate model projections of rainfall from US landfalling tropical cyclones. Climate Dyn., 45, 33653379, https://doi.org/10.1007/s00382-015-2544-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xi, D., and N. Lin, 2021: Sequential landfall of tropical cyclones in the United States: From historical records to climate projections. Geophys. Res. Lett., 48, e2021GL094826, https://doi.org/10.1029/2021GL094826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xi, D., N. Lin, and J. Smith, 2020: Evaluation of a physics-based tropical cyclone rainfall model for risk assessment. J. Hydrometeor., 21, 21972218, https://doi.org/10.1175/JHM-D-20-0035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, M. J., S. A. Braun, and D. S. Chen, 2011: Water budget of Typhoon Nari (2001). Mon. Wea. Rev., 139, 38093828, https://doi.org/10.1175/MWR-D-10-05090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett., 32, L18701, https://doi.org/10.1029/2005GL023684.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, L., S. M. Quiring, and K. A. Emanuel, 2013: Estimating tropical cyclone precipitation risk in Texas. Geophys. Res. Lett., 40, 62256230, https://doi.org/10.1002/2013GL058284.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Understanding Uncertainties in Tropical Cyclone Rainfall Hazard Modeling Using Synthetic Storms

Dazhi XiaDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Ning LinaDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Abstract

Tropical cyclone (TC) rainfall hazard assessment is subject to the bias in TC climatology estimation from climate simulations or synthetic downscaling. In this study, we investigate the uncertainty in TC rainfall hazard assessment induced by this bias using both rain gauge and radar observations and synthetic-storm-model-coupled TC rainfall simulations. We identify the storm’s maximum intensity, impact duration, and minimal distance to the site to be the three most important storm parameters for TC rainfall hazard, and the relationship between the important storm parameters and TC rainfall can be well captured by a physics-based TC rainfall model. The uncertainty in the synthetic rainfall hazard induced by the bias in TC climatology can be largely explained by the bias in the important storm parameters simulated by the synthetic storm model. Correcting the distribution of the most biased parameter may significantly improve rainfall hazard estimation. Bias correction based on the joint distribution of the important parameters may render more accurate rainfall hazard estimations; however, the general technical difficulties in resampling from high-dimensional joint probability distributions prevent more accurate estimations in some cases. The results of the study also support future investigation of the impact of climate change on TC rainfall hazards through the lens of future changes in the identified important storm parameters.

© 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: Dazhi Xi, dxi@princeton.edu

Abstract

Tropical cyclone (TC) rainfall hazard assessment is subject to the bias in TC climatology estimation from climate simulations or synthetic downscaling. In this study, we investigate the uncertainty in TC rainfall hazard assessment induced by this bias using both rain gauge and radar observations and synthetic-storm-model-coupled TC rainfall simulations. We identify the storm’s maximum intensity, impact duration, and minimal distance to the site to be the three most important storm parameters for TC rainfall hazard, and the relationship between the important storm parameters and TC rainfall can be well captured by a physics-based TC rainfall model. The uncertainty in the synthetic rainfall hazard induced by the bias in TC climatology can be largely explained by the bias in the important storm parameters simulated by the synthetic storm model. Correcting the distribution of the most biased parameter may significantly improve rainfall hazard estimation. Bias correction based on the joint distribution of the important parameters may render more accurate rainfall hazard estimations; however, the general technical difficulties in resampling from high-dimensional joint probability distributions prevent more accurate estimations in some cases. The results of the study also support future investigation of the impact of climate change on TC rainfall hazards through the lens of future changes in the identified important storm parameters.

© 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: Dazhi Xi, dxi@princeton.edu
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