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.
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.
We continue with our program to derive simple practical methods that can be used to predict the number of US landfalling hurricanes a year in advance. We repeat an earlier study, but for a slightly different definition landfalling hurricanes, and for intense hurricanes only. We find that the averaging lengths needed for optimal predictions of numbers of intense hurricanes are longer than those needed for optimal predictions of numbers of hurricanes of all strengths.
In previous work we have analysed the Atlantic basin hurricane number time-series to identify decadal time-scale change points. We now repeat the analysis but for US landfalling hurricanes. The results are very different.
In previous work, we have shown how to combine long and short historical baselines to make predictions of future hurricane numbers. We now ask: how should such combinations change if we are interested in predicting the future number of intense hurricanes?
We are building a hurricane number prediction scheme that relies, in part, on statistical modelling of the empirical relationship between Atlantic sea surface temperatures and landfalling hurricane numbers. We test out a number of simple statistical models for that relationship, using data from 1900 to 2005 and data from 1950 to 2005, and for both all hurricane numbers and intense hurricane numbers. The results are very different from the corresponding analysis for basin hurricane numbers.
We present a simple method for the year-ahead prediction of the number of hurricanes making landfall in the US. The method is based on averages of historical annual hurricane numbers, and we perform a backtesting study to find the length of averaging window that would have given the best predictions in the past.
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.
We are building a hurricane number prediction scheme based on first predicting main development region sea surface temperature (SST), then predicting the number of hurricanes in the Atlantic basin given the SST prediction, and finally predicting the number of US landfalling hurricanes based on the prediction of the number of basin hurricanes. We have described a number of SST prediction methods in previous work. We now investigate the empirical relationship between SST and basin hurricane numbers, and put this together with the SST predictions to make predictions of both basin and landfalling …
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.
There is significant correlation between main development region sea surface temperature and the number of hurricanes that form in the Atlantic basin. The correlation between the same sea surface temperatures and the number of \emph{landfalling} hurricanes is much lower, however. Why is this? Do we need to consider complex physical hypotheses, or is there a simple statistical explanation?
The time series of the number of hurricanes per year in the Atlantic basin shows a clear change of level between 1994 and 1995. The time series of the number of hurricanes that make landfall in the US, however, does not show the same obvious change of level. Prima-facie this seems rather surprising, given that the landfalling hurricanes are a subset of the basin hurricanes. We investigate whether it really should be considered surprising or whether there is a simple statistical explanation for the disappearance of this change-point at landfall.
Recent US major landfalling hurricanes Katrina and Rita and last year's four U.S. landfalling major hurricanes have spawned an abundance of questions concerning the role that global warming might be playing in these events. This idea has been given added credence by the September 2005 Science paper of Webster, Holland, Curry and Chang (Vol. 304, pp. 1844-1846) showing that the global number of Category 4-5 hurricanes have increased in the last 15 years (1990-2004) in comparison with the prior 15-year period of 1975-1989. They report 171 Category 4-5 hurricanes in the earlier 15-year period vs.…
One way to predict hurricane numbers would be to predict sea surface temperature, and then predict hurricane numbers as a function of the predicted sea surface temperature. For certain parametric models for sea surface temperature and the relationship between sea surface temperature and hurricane numbers, closed-form solutions exist for the mean and the variance of the number of predicted hurricanes, and for the standard error on the mean. We derive a number of such expressions.
One possible method for the year-ahead prediction of hurricane numbers would be to make a year-ahead prediction of sea surface temperature (SST), and then to apply relationships that link SST to hurricane numbers. As a first step towards setting up such a system this article compares three simple statistical methods for the year-ahead prediction of the relevant SSTs.
Global warming due to the increasing concentration of greenhouse gases has become a great concern and has been linked to increased hurricane activity associated with higher sea surface temperatures with conflicting views. Our observational results based on long term trends of sea surface temperatures reveal that the anomaly reached a record 0.8 C in the Gulf of Mexico in August 2005 as compared to previous years and may have been responsible for the intensification of the devastating Hurricane Katrina into a category 5 hurricane that hit the Southern coast of United States severely impacting t…
A physical model is proposed for the estimation of the foam impact on the variation of the effective drag coefficient, C_d, with reference to the wind speed U10 in stormy and hurricane conditions. In the present model C_d is approximated by partitioning the sea surface into foam-covered and foam-free areas. Based on the available optical and radiometric measurements of the fractional foam coverage and the characteristic roughness of the sea-surface in the saturation limit of the foam coverage, the model yields the resulting dependence of C_d vs U10. This dependence is in fair agreement with th…
A physical model is proposed for the prediction of the non-monotonic variation of the drag coefficient, C_d, with wind speed. The model approximates the effective C_d by the area-weighted averaging of the distinct drag coefficients associated with the foam-free and foam totally-covered portions of the sea surface, and identifies the roughness of the sea surface totally covered by foam with the foam bubble size. Based on the available optical and radiometric measurements of the foam fractional coverage and the foam bubble size, the present model yields the effective C_d which is in fair agreeme…
Recently proposed model of foam impact on the air sea drag coefficient Cd has been employed for the estimation of the efficient foam-bubble radius Rb variation with wind speed U10 in hurricane conditions. The model relates Cd (U10) with the efficient roughness length Zeff (U10) represented as a sum of aerodynamic roughness lengths of the foam free and foam covered sea surfaces Zw (U10 ), and Zf (U10) weighted with the foam coverage coefficient. This relation is treated for known phenomenological distributions Cd (U10), Zw (U10) at strong wind speeds as an inverse problem for the efficient roug…
The proposition that the tropical cyclogenesis increases with the size of the warm pool, the area enclosed by the 26C SST isotherm, is tested by comparing the seasonal variation of the warm pool area with the seasonality of the number of tropical cyclones. A non-linear relationship of high statistical significance is found between the area and the number of cyclones, which may explain both why there is no linear trend in the number of cyclones over time and the recent upturn in the number of Atlantic hurricanes.
The present study concerns the numerical modeling of sea-wave instability under the effect of logarithmic-wind profile in hurricane conditions. The central point of the study is the calculation of the wave growth rate, which is proportional to the fractional input energy from the weakly-sheared (logarithmic) wind to the wave exponentially varying with time. It is shown for hurricane conditions that the Miles-type stability model based on the Charnock's formula with the standard constant coefficient underestimates the growth rate ~5 to 50 times as compared with the model employing the roughness…
The present study concerns the numerical modeling of sea-wave instability under the effect of logarithmic-wind profile in hurricane conditions. The central point of the study is the calculation of the wave growth rate, which is proportional to the fractional input energy from the weakly-sheared (logarithmic) wind to the wave exponentially varying with time. It is shown for hurricane conditions that the Miles-type stability model based on the Charnock's formula with the standard constant coefficient underestimates the growth rate ~5 to 50 times as compared with the model employing the roughness…
There is a clear positive correlation between boreal summer tropical Atlantic sea-surface temperature and annual hurricane numbers. This motivates the idea of trying to predict the sea-surface temperature in order to be able to predict future hurricane activity. In previous work we have used simple statistical methods to make 5 year predictions of tropical Atlantic sea surface temperatures for this purpose. We now compare these statistical SST predictions with SST predictions made by an ensemble mean of IPCC climate models.
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.
Evidence is provided that the global distribution of tropical hurricanes is principally determined by a universal function H of a single variable z that in turn is expressible in terms of the local sea surface temperature and latitude. The data-driven model presented here carries stark implications for the large increased numbers of hurricanes which it predicts for a warmer world. Moreover, the rise in recent decades in the numbers of hurricanes in the Atlantic, but not the Pacific basin, is shown to have a simple explanation in terms of the specific form of H(z), which yields larger percentag…
A formal averaging procedure over the air-sea interface is developed for both momentum and enthalpy surface-transfer coefficients, C_D and C_K, in hurricane conditions. This leads to splitting of both the transfer coefficients across the total area of the sea surface, C_(D,K), into the sums of their partial values over the foam-free, C_(D,Kw), and foam-covered, C_(D,Kf), fractions weighted with foam- and water-coverage coefficients αf and 1-αf . The transfer coefficients, C_(D,K) and C_(D,Kw), are estimated by measurements in open-sea and in laboratory conditions, respectively, while the trans…
Air-sea drag governs the momentum transfer between the atmosphere and the ocean, and remains largely unknown in hurricane winds. We revisit the momentum budget and eddy-covariance methods to estimate the surface drag coefficient in the laboratory. Our drag estimates agree with field measurements in low-to-moderate winds, and previous laboratory measurements in hurricane-force winds. The drag coefficient saturates at $2.6 \times 10^{-3}$ and $U_{10} \approx 25\ m\ s^{-1}$, in agreement with previous laboratory results by Takagaki et al. (2012). During our analysis, we discovered an error in the…
Atlantic Multidecadal Variability (AMV) describes variations of North Atlantic sea surface temperature with a typical cycle of between 60 and 70 years. AMV strongly impacts local climate over North America and Europe, therefore prediction of AMV, especially the extreme values, is of great societal utility for understanding and responding to regional climate change. This work tests multiple machine learning models to improve the state of AMV prediction from maps of sea surface temperature, salinity, and sea level pressure in the North Atlantic region. We use data from the Community Earth System…
