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Early Season Hurricane Risk Assessment: Climate-Conditioned HITS Simulation of North Atlantic Tropical Storm Tracks

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  • 1 Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York
  • | 2 Department of Earth and Environmental Engineering, Columbia University, New York, New York
  • | 3 Jupiter Intelligence, San Mateo, California
  • | 4 North Shore High School, Glen Head, New York
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Abstract

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

© 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: Jennifer Nakamura, jennie@ldeo.columbia.com

Abstract

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, Cluster-Based Climate-Conditioned Hurricane Intensity and Track Simulator (C3-HITS), extends a previous version of HITS. HITS is a nonparametric, spatial semi-Markov, stochastic model that generates TC tracks by conditionally simulating segments of randomly varying lengths from the TC tracks contained in NOAA’s Best Track Data, version 2, dataset. The distance to neighboring tracks, track direction, TC wind speed, and age are used as conditioning variables. C3-HITS adds conditioning on two early season, large-scale climate covariates to condition the track simulation: the Niño-3.4 index, representing the eastern equatorial Pacific Ocean sea surface temperature (SST) departure from climatology, and main development region, representing tropical North Atlantic SST departure from climatology in the North Atlantic TC main development region. A track clustering procedure is used to identify track families, and a Poisson regression model is used to model the probabilistic number of storms formed in each cluster, conditional on the two climate covariates. The HITS algorithm is then applied to evolve these tracks forward in time. The output of this two-step, climate-conditioned simulator is compared with an unconditional HITS application to illustrate its prognostic efficacy in simulating tracks during the subsequent season. As in the HITS model, each track retains information on velocity and other attributes that can be used for predictive coastal risk modeling for the upcoming TC season.

© 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: Jennifer Nakamura, jennie@ldeo.columbia.com
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