Hourly Atlantic hurricane data

In the absence of higher temporally resolved information on historical hurricanes, we develop a set of hourly-interpolated track values that can be used for hurricane climate studies. A spline interpolation is used to obtain locations and wind speeds at 1-hr intervals from the 6-hr values for all tropical cyclones in NOAA's dataset. The data set is augmented with a set of storm intensification and heading estimates based on a Savitzky-Golay filter. The data are available here.

Limitation of risk models in a changing climate

Hurricane risk models used by the insurance industry rely on a catalog of storms that represent the historical data in some way or another. While useful for estimating aggregate portfolio losses from a hypothetical worse case scenario, these catalogs are not easily suited for anticipating losses based on a changing climate. At the core of the catalog is a set of synthetic storms and a way to assign a probability to each. But each synthetic storm in the catalog is a composite of size, track, and intensity so it is difficult to estimate risk at a particular point location. Additionally, it is important to consider how climate influences hurricane risk, but it is not obvious how to condition the multidimensional storm event on climate. Perhaps most importantly, the assigned return rates are empirically driven in that the rates are not connected parametrically to a theoretical distribution. This leads to lower confidence in the estimated rates.

We propose an alternative approach for anticipating losses that produces predict expected wind speed distributions at any location. The parametric distributions which give tighter confidence intervals can be naturally conditioned on preseason climate variables. More importantly this approach could allow the reinsurance industry to examine which coastal regions are most sensitive to the changing climate. For example, with increasing Atlantic sea-surface temperatures is it realistic to expect that the risk of hurricane damage will increase everywhere? The answer to this question has implications for insurance rates and societal vulnerability.