Early Season Hurricane Risk Assessment: Climate Conditioned HITS Simulation of N. Atlantic Tropical Storm Tracks

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

We present a hurricane risk assessment model that simulates N. Atlantic tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, C3-HITS (for Cluster-based Climate Conditioned Hurricane Intensity and Track Simulator), extends a previous version of HITS (Nakamura et al., 2015). 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 data set. 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 NINO3.4 index, representing the eastern equatorial Pacific sea surface temperature (SST) departure from climatology, and MDR, representing tropical N. Atlantic SST departure from climatology in the N. Atlantic TC Main Development Region. A track clustering procedure (Nakamura et al., 2009) 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.

Corresponding author: Jennifer Nakamura, jennie@ldeo.columbia.com

Abstract

We present a hurricane risk assessment model that simulates N. Atlantic tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state. The model, C3-HITS (for Cluster-based Climate Conditioned Hurricane Intensity and Track Simulator), extends a previous version of HITS (Nakamura et al., 2015). 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 data set. 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 NINO3.4 index, representing the eastern equatorial Pacific sea surface temperature (SST) departure from climatology, and MDR, representing tropical N. Atlantic SST departure from climatology in the N. Atlantic TC Main Development Region. A track clustering procedure (Nakamura et al., 2009) 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.

Corresponding author: Jennifer Nakamura, jennie@ldeo.columbia.com
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