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There is widespread concern about the recent increase in North Atlantic hurricane activity. Results here suggest that fledgling storms tracking east to west at low latitudes are more likely to reach hurricane intensity than those traveling on a more northerly trajectory. The annual occurrence of these straight-moving hurricanes (east to west at low latitudes) is statistically linked to the El Niño-Southern Oscillation (ENSO) and to the North Atlantic Oscillation (NAO) using a Poisson regression. Because the occurrence of U.S. hurricanes south of about 35°N is positively correlated with the abundance of straight-moving hurricanes, an accurate prediction of ENSO together with observations of the NAO could be used to forecast seasonal hurricane probabilities along the southeast U.S. coast. It is stressed that in order to understand the range of mechanisms associated with hurricane activity, it is important to consider factors that influence tracks. In this regard, the NAO is a leading candidate.
There is widespread concern about the recent increase in North Atlantic hurricane activity. Results here suggest that fledgling storms tracking east to west at low latitudes are more likely to reach hurricane intensity than those traveling on a more northerly trajectory. The annual occurrence of these straight-moving hurricanes (east to west at low latitudes) is statistically linked to the El Niño-Southern Oscillation (ENSO) and to the North Atlantic Oscillation (NAO) using a Poisson regression. Because the occurrence of U.S. hurricanes south of about 35°N is positively correlated with the abundance of straight-moving hurricanes, an accurate prediction of ENSO together with observations of the NAO could be used to forecast seasonal hurricane probabilities along the southeast U.S. coast. It is stressed that in order to understand the range of mechanisms associated with hurricane activity, it is important to consider factors that influence tracks. In this regard, the NAO is a leading candidate.
Abstract
In a 2008 paper, using satellite-derived wind speed estimates from tropical cyclones over the 25-yr period 1981–2006, we showed the strongest tropical cyclones getting stronger. We related the increasing intensity to rising ocean temperatures consistent with theory. Oceans have continued to warm since that paper was published, so the intensity of the strongest cyclones should have continued upward as well. Here I show that this is the case, with increases in the upper-quantile intensities of global tropical cyclones amounting to between 3.5% and 4.5% in the period 2007–19 relative to the earlier base period (1981–2006). All basins individually show upward intensity trends for at least one upper quantile considered, with the North Atlantic and western North Pacific basins showing the steepest and most consistent trends across the quantiles.
Abstract
In a 2008 paper, using satellite-derived wind speed estimates from tropical cyclones over the 25-yr period 1981–2006, we showed the strongest tropical cyclones getting stronger. We related the increasing intensity to rising ocean temperatures consistent with theory. Oceans have continued to warm since that paper was published, so the intensity of the strongest cyclones should have continued upward as well. Here I show that this is the case, with increases in the upper-quantile intensities of global tropical cyclones amounting to between 3.5% and 4.5% in the period 2007–19 relative to the earlier base period (1981–2006). All basins individually show upward intensity trends for at least one upper quantile considered, with the North Atlantic and western North Pacific basins showing the steepest and most consistent trends across the quantiles.
Abstract
Hurricane activity over the North Atlantic basin during 1995 and 1996 is compared to the combined hurricane activity over the previous four years (1991–94). The earlier period produced a total of 15 hurricanes compared to a total of 20 hurricanes over the latter period. Despite this similarity in numbers, the hurricanes of 1995 and 1996 were generally of the tropical-only variety, which marks a substantial departure from activity during the early 1990s. The return of tropical-only hurricanes to the Atlantic basin is likely the result of several global and local factors, including cool SST conditions in the equatorial central and eastern Pacific and warm SSTs in the tropical Atlantic. The hurricane activity of 1995 and 1996 is more reminiscent of activity of some seasons during the early and mid-1950s.
Abstract
Hurricane activity over the North Atlantic basin during 1995 and 1996 is compared to the combined hurricane activity over the previous four years (1991–94). The earlier period produced a total of 15 hurricanes compared to a total of 20 hurricanes over the latter period. Despite this similarity in numbers, the hurricanes of 1995 and 1996 were generally of the tropical-only variety, which marks a substantial departure from activity during the early 1990s. The return of tropical-only hurricanes to the Atlantic basin is likely the result of several global and local factors, including cool SST conditions in the equatorial central and eastern Pacific and warm SSTs in the tropical Atlantic. The hurricane activity of 1995 and 1996 is more reminiscent of activity of some seasons during the early and mid-1950s.
Abstract
The authors illustrate a statistical model for predicting tornado activity in the central Great Plains by 1 March. The model predicts the number of tornado reports during April–June using February sea surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean Sea (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the nonstationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree Celsius increase in SST over the WCA and a 15% decrease in the number of reports per degree Celsius increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large-scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1–F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data.
Abstract
The authors illustrate a statistical model for predicting tornado activity in the central Great Plains by 1 March. The model predicts the number of tornado reports during April–June using February sea surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean Sea (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the nonstationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree Celsius increase in SST over the WCA and a 15% decrease in the number of reports per degree Celsius increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large-scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1–F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data.
Abstract
The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.
Abstract
The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.
Abstract
Empirical studies have led to improvements in evaluating and quantifying the tornado threat. However, more work is needed to put the research onto a solid statistical foundation. Here the authors begin to build this foundation by introducing and then demonstrating a statistical model to estimate damage rating (enhanced Fujita scale) probabilities. A goal is to alert researchers to available statistical technology for improving severe weather warnings. The model is cumulative logistic regression and the parameters are determined using Bayesian inference. The model is demonstrated by estimating damage rating probabilities from values of known environmental factors on days with many tornadoes in the United States. Controlling for distance to nearest town/city, which serves as a proxy variable for damage target density, the model quantifies the chance that a particular tornado will be assigned any damage rating given specific environmental conditions. Under otherwise average conditions, the model estimates a 65% chance that a tornado occurring in a city or town will be rated EF0 when bulk shear (1000–500-hPa layer) is weak (10 m s−1). This probability drops to 38% when the bulk shear is strong (40 m s−1). The model quantifies the corresponding increases in the chance of the same tornado receiving higher damage ratings. Quantifying changes to the probability distribution on the ordered damage rating categories is a natural application of cumulative logistic regression.
Abstract
Empirical studies have led to improvements in evaluating and quantifying the tornado threat. However, more work is needed to put the research onto a solid statistical foundation. Here the authors begin to build this foundation by introducing and then demonstrating a statistical model to estimate damage rating (enhanced Fujita scale) probabilities. A goal is to alert researchers to available statistical technology for improving severe weather warnings. The model is cumulative logistic regression and the parameters are determined using Bayesian inference. The model is demonstrated by estimating damage rating probabilities from values of known environmental factors on days with many tornadoes in the United States. Controlling for distance to nearest town/city, which serves as a proxy variable for damage target density, the model quantifies the chance that a particular tornado will be assigned any damage rating given specific environmental conditions. Under otherwise average conditions, the model estimates a 65% chance that a tornado occurring in a city or town will be rated EF0 when bulk shear (1000–500-hPa layer) is weak (10 m s−1). This probability drops to 38% when the bulk shear is strong (40 m s−1). The model quantifies the corresponding increases in the chance of the same tornado receiving higher damage ratings. Quantifying changes to the probability distribution on the ordered damage rating categories is a natural application of cumulative logistic regression.
Abstract
Models that predict annual U.S. hurricane activity assume a Poisson distribution for the counts. Here the authors show that this assumption applied to Florida hurricanes leads to a forecast that underpredicts both the number of years without hurricanes and the number of years with three or more hurricanes. The underdispersion in forecast counts arises from a tendency for hurricanes to arrive in groups along this part of the coastline. The authors then develop an extension to their earlier statistical model that assumes that the rate of hurricane clusters follows a Poisson distribution with cluster size capped at two hurricanes. Hindcasts from the cluster model better fit the distribution of Florida hurricanes conditional on the climate covariates including the North Atlantic Oscillation and Southern Oscillation index. Results are similar to models that parameterize the extra-Poisson variation in the observed counts, including the negative binomial and the Poisson inverse Gaussian models. The authors argue, however, that the cluster model is physically consistent with the way Florida hurricanes tend to arrive in groups.
Abstract
Models that predict annual U.S. hurricane activity assume a Poisson distribution for the counts. Here the authors show that this assumption applied to Florida hurricanes leads to a forecast that underpredicts both the number of years without hurricanes and the number of years with three or more hurricanes. The underdispersion in forecast counts arises from a tendency for hurricanes to arrive in groups along this part of the coastline. The authors then develop an extension to their earlier statistical model that assumes that the rate of hurricane clusters follows a Poisson distribution with cluster size capped at two hurricanes. Hindcasts from the cluster model better fit the distribution of Florida hurricanes conditional on the climate covariates including the North Atlantic Oscillation and Southern Oscillation index. Results are similar to models that parameterize the extra-Poisson variation in the observed counts, including the negative binomial and the Poisson inverse Gaussian models. The authors argue, however, that the cluster model is physically consistent with the way Florida hurricanes tend to arrive in groups.
Abstract
A hierarchical Bayesian strategy for modeling annual U.S. hurricane counts from the period 1851–2000 is illustrated. The approach is based on a separation of the reliable twentieth-century records from the less precise nineteenth-century records and makes use of Poisson regression. The work extends a recent climatological analysis of U.S. hurricanes by including predictors (covariates) in the form of indices for the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Model integration is achieved through a Markov chain Monte Carlo algorithm. A Bayesian strategy that uses only hurricane counts from the twentieth century together with noninformative priors compares favorably to a traditional (frequentist) approach and confirms a statistical relationship between climate patterns and coastal hurricane activity. Coinciding La Niña and negative NAO conditions significantly increase the probability of a U.S. hurricane. Hurricane counts from the nineteenth century are bootstrapped to obtain informative priors on the model parameters. The earlier records, though less reliable, allow for a more precise description of U.S. hurricane activity. This translates to a greater certainty in the authors' belief about the effects of ENSO and NAO on coastal hurricane activity. Similar conclusions are drawn when annual U.S. hurricane counts are disaggregated into regional counts. Contingent on the availability of values for the covariates, the models can be used to make predictive inferences about the hurricane season.
Abstract
A hierarchical Bayesian strategy for modeling annual U.S. hurricane counts from the period 1851–2000 is illustrated. The approach is based on a separation of the reliable twentieth-century records from the less precise nineteenth-century records and makes use of Poisson regression. The work extends a recent climatological analysis of U.S. hurricanes by including predictors (covariates) in the form of indices for the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Model integration is achieved through a Markov chain Monte Carlo algorithm. A Bayesian strategy that uses only hurricane counts from the twentieth century together with noninformative priors compares favorably to a traditional (frequentist) approach and confirms a statistical relationship between climate patterns and coastal hurricane activity. Coinciding La Niña and negative NAO conditions significantly increase the probability of a U.S. hurricane. Hurricane counts from the nineteenth century are bootstrapped to obtain informative priors on the model parameters. The earlier records, though less reliable, allow for a more precise description of U.S. hurricane activity. This translates to a greater certainty in the authors' belief about the effects of ENSO and NAO on coastal hurricane activity. Similar conclusions are drawn when annual U.S. hurricane counts are disaggregated into regional counts. Contingent on the availability of values for the covariates, the models can be used to make predictive inferences about the hurricane season.
Abstract
The authors build on their efforts to understand and predict coastal hurricane activity by developing statistical seasonal forecast models that can be used operationally. The modeling strategy uses May–June averaged values representing the North Atlantic Oscillation (NAO), the Southern Oscillation index (SOI), and the Atlantic multidecadal oscillation to predict the probabilities of observing U.S. hurricanes in the months ahead (July–November). The models are developed using a Bayesian approach and make use of data that extend back to 1851 with the earlier hurricane counts (prior to 1899) treated as less certain relative to the later counts. Out-of-sample hindcast skill is assessed using the mean-squared prediction error within a hold-one-out cross-validation exercise. Skill levels are compared to climatology. Predictions show skill above climatology, especially using the NAO + SOI and the NAO-only models. When the springtime NAO values are below normal, there is a heightened risk of U.S. hurricane activity relative to climatology. The preliminary NAO value for 2005 is −0.565 standard deviations so the NAO-only model predicts a 13% increase over climatology of observing three or more U.S. hurricanes.
Abstract
The authors build on their efforts to understand and predict coastal hurricane activity by developing statistical seasonal forecast models that can be used operationally. The modeling strategy uses May–June averaged values representing the North Atlantic Oscillation (NAO), the Southern Oscillation index (SOI), and the Atlantic multidecadal oscillation to predict the probabilities of observing U.S. hurricanes in the months ahead (July–November). The models are developed using a Bayesian approach and make use of data that extend back to 1851 with the earlier hurricane counts (prior to 1899) treated as less certain relative to the later counts. Out-of-sample hindcast skill is assessed using the mean-squared prediction error within a hold-one-out cross-validation exercise. Skill levels are compared to climatology. Predictions show skill above climatology, especially using the NAO + SOI and the NAO-only models. When the springtime NAO values are below normal, there is a heightened risk of U.S. hurricane activity relative to climatology. The preliminary NAO value for 2005 is −0.565 standard deviations so the NAO-only model predicts a 13% increase over climatology of observing three or more U.S. hurricanes.
