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Jeremy Penzer

Publications and source records attributed to Jeremy Penzer.

8 recordsLinked to original sources
arXiv2007

Predicting landfalling hurricane numbers from basin hurricane numbers: statistical analysis and predictions

One possible method for predicting landfalling hurricane numbers is to first predict the number of hurricanes in the basin and then convert that prediction to a prediction of landfalling hurricane numbers using an estimated proportion. Should this work better than just predicting landfalling hurricane numbers directly? We perform a basic statistical analysis of this question in the context of a simple abstract model, and convert some previous predictions of basin numbers into landfalling numbers.

physics.ao-ph↗
arXiv2007

Predicting landfalling hurricane numbers from sea surface temperature: theoretical comparisons of direct and indirect approaches

We consider two ways that one might convert a prediction of sea surface temperature (SST) into a prediction of landfalling hurricane numbers. First, one might regress historical numbers of landfalling hurricanes onto historical SSTs, and use the fitted regression relation to predict future landfalling hurricane numbers given predicted SSTs. We call this the direct approach. Second, one might regress \emph{basin} hurricane numbers onto historical SSTs, estimate the proportion of basin hurricanes that make landfall, and use the fitted regression relation and estimated proportion to predict future landfalling hurricane numbers. We call this the \emph{indirect} approach. Which of these two methods is likely to work better? We answer this question for two simple models. The first model is reasonably realistic, but we have to resort to using simulations to answer the question in the context of this model. The second model is less realistic, but allows us to derive a general analytical result.

physics.ao-ph↗
arXiv2007

Five year prediction of Sea Surface Temperature in the Tropical Atlantic: a comparison of simple statistical methods

We are developing schemes that predict future hurricane numbers by first predicting future sea surface temperatures (SSTs), and then apply the observed statistical relationship between SST and hurricane numbers. As part of this overall goal, in this study we compare the historical performance of three simple statistical methods for making five-year SST forecasts. We also present SST forecasts for 2006-2010 using these methods and compare them to forecasts made from two structural time series models.

physics.ao-ph↗
arXiv2006

Year ahead prediction of US landfalling hurricane numbers: the optimal combination of multiple levels of activity since 1900

In earlier work we considered methods for predicting future levels of hurricane activity based on the assumption that historical mean activity was at one constant level from 1900 to 1994, and has been at another constant level since then. We now make this model a little more subtle, and account for the possibility of four different levels of mean hurricane activity since 1900.

physics.ao-ph↗
arXiv2006

An objective change-point analysis of historical Atlantic hurricane numbers

We perform an objective change-point analysis on 106 years of historical hurricane number data. The algorithm we use looks at all possible combinations of change-points and compares them in terms of the variances of the differences between real and modelled numbers. Overfitting is avoided by using cross-validation. We identify four change-points, and show that the presence of temporal structure in the hurricane number time series is highly statistically significant.

physics.ao-ph↗
arXiv2005

Year ahead prediction of US landfalling hurricane numbers: the optimal combination of long and short baselines

Annual levels of US landfalling hurricane activity averaged over the last 11 years (1995-2005) are higher than those averaged over the previous 95 years (1900-1994). How, then, should we best predict hurricane activity rates for next year? Based on the assumption that the higher rates will continue we use an optimal combination of averages over the long and short time-periods to produce a prediction that minimises MSE.

physics.ao-ph↗