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Abstract
Predictive climate distributions of U.S. landfalling hurricanes are estimated from observational records over the period 1851–2000. The approach is Bayesian, combining the reliable records of hurricane activity during the twentieth century with the less precise accounts of activity during the nineteenth century to produce a best estimate of the posterior distribution on the annual rates. The methodology provides a predictive distribution of future activity that serves as a climatological benchmark. Results are presented for the entire coast as well as for the Gulf Coast, Florida, and the East Coast. Statistics on the observed annual counts of U.S. hurricanes, both for the entire coast and by region, are similar within each of the three consecutive 50-yr periods beginning in 1851. However, evidence indicates that the records during the nineteenth century are less precise. Bayesian theory provides a rational approach for defining hurricane climate that uses all available information and that makes no assumption about whether the 150-yr record of hurricanes has been adequately or uniformly monitored. The analysis shows that the number of major hurricanes expected to reach the U.S. coast over the next 30 yr is 18 and the number of hurricanes expected to hit Florida is 20.
Abstract
Predictive climate distributions of U.S. landfalling hurricanes are estimated from observational records over the period 1851–2000. The approach is Bayesian, combining the reliable records of hurricane activity during the twentieth century with the less precise accounts of activity during the nineteenth century to produce a best estimate of the posterior distribution on the annual rates. The methodology provides a predictive distribution of future activity that serves as a climatological benchmark. Results are presented for the entire coast as well as for the Gulf Coast, Florida, and the East Coast. Statistics on the observed annual counts of U.S. hurricanes, both for the entire coast and by region, are similar within each of the three consecutive 50-yr periods beginning in 1851. However, evidence indicates that the records during the nineteenth century are less precise. Bayesian theory provides a rational approach for defining hurricane climate that uses all available information and that makes no assumption about whether the 150-yr record of hurricanes has been adequately or uniformly monitored. The analysis shows that the number of major hurricanes expected to reach the U.S. coast over the next 30 yr is 18 and the number of hurricanes expected to hit Florida is 20.
Abstract
The rarity of severe coastal hurricanes implies that empirical estimates of extreme wind speed return levels will be unreliable. Here climatology models derived from extreme value theory are estimated using data from the best-track [Hurricane Database (HURDAT)] record. The occurrence of a hurricane above a specified threshold intensity level is assumed to follow a Poisson distribution, and the distribution of the maximum wind is assumed to follow a generalized Pareto distribution. The likelihood function is the product of the generalized Pareto probabilities for each wind speed estimate. A geographic region encompassing the entire U.S. coast vulnerable to Atlantic hurricanes is of primary interest, but the Gulf Coast, Florida, and the East Coast regions are also considered. Model parameters are first estimated using a maximum likelihood (ML) procedure. Results estimate the 100-yr return level for the entire coast at 157 kt (±10 kt), but at 117 kt (±4 kt) for the East Coast region (1 kt = 0.514 m s−1). Highest wind speed return levels are noted along the Gulf Coast from Texas to Alabama. The study also examines how the extreme wind return levels change depending on climate conditions including El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature. The mean 5-yr return level during La Niña (El Niño) conditions is 125 (116) kt, but is 140 (164) kt for the 100-yr return level. This indicates that La Niña years are the most active for the occurrence of strong hurricanes, but that extreme hurricanes are more likely during El Niño years. Although El Niño inhibits hurricane formation in part through wind shear, the accompanying cooler lower stratosphere appears to increase the potential intensity of hurricanes that do form. To take advantage of older, less reliable data, the models are reformulated using Bayesian methods. Gibbs sampling is used to integrate the prior over the likelihood to obtain the posterior distributions for the model parameters conditional on global temperature. Higher temperatures are conditionally associated with more strong hurricanes and higher return levels for the strongest hurricane winds. Results compare favorably with an ML approach as well as with recent modeling and observational studies. The maximum possible near-coastal wind speed is estimated to be 208 kt (183 kt) using the Bayesian (ML) approach.
Abstract
The rarity of severe coastal hurricanes implies that empirical estimates of extreme wind speed return levels will be unreliable. Here climatology models derived from extreme value theory are estimated using data from the best-track [Hurricane Database (HURDAT)] record. The occurrence of a hurricane above a specified threshold intensity level is assumed to follow a Poisson distribution, and the distribution of the maximum wind is assumed to follow a generalized Pareto distribution. The likelihood function is the product of the generalized Pareto probabilities for each wind speed estimate. A geographic region encompassing the entire U.S. coast vulnerable to Atlantic hurricanes is of primary interest, but the Gulf Coast, Florida, and the East Coast regions are also considered. Model parameters are first estimated using a maximum likelihood (ML) procedure. Results estimate the 100-yr return level for the entire coast at 157 kt (±10 kt), but at 117 kt (±4 kt) for the East Coast region (1 kt = 0.514 m s−1). Highest wind speed return levels are noted along the Gulf Coast from Texas to Alabama. The study also examines how the extreme wind return levels change depending on climate conditions including El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, and global temperature. The mean 5-yr return level during La Niña (El Niño) conditions is 125 (116) kt, but is 140 (164) kt for the 100-yr return level. This indicates that La Niña years are the most active for the occurrence of strong hurricanes, but that extreme hurricanes are more likely during El Niño years. Although El Niño inhibits hurricane formation in part through wind shear, the accompanying cooler lower stratosphere appears to increase the potential intensity of hurricanes that do form. To take advantage of older, less reliable data, the models are reformulated using Bayesian methods. Gibbs sampling is used to integrate the prior over the likelihood to obtain the posterior distributions for the model parameters conditional on global temperature. Higher temperatures are conditionally associated with more strong hurricanes and higher return levels for the strongest hurricane winds. Results compare favorably with an ML approach as well as with recent modeling and observational studies. The maximum possible near-coastal wind speed is estimated to be 208 kt (183 kt) using the Bayesian (ML) approach.
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
Advances in hurricane climate science allow forecasts of seasonal landfall activity to be made. The authors begin with a review of the forecast methods available in the literature. They then reformulate the methods using a Bayesian probabilistic approach. This allows a direct comparison to be made while focusing on a single hindcast of the 2004 season over Florida. The models, including climatology, are estimated using Gibbs sampling. Diagnostic checks verify convergence and efficient mixing of the samples from each of the models. A below average sea level pressure gradient over the eastern North Atlantic Ocean during May and June in combination with an above average tropospheric-averaged wind index associated, in part, with a strengthening of the Bermuda high pressure during July resulted in an above average probability of at least one Florida hurricane. The relatively high hindcast probabilities for 2004 were in marked contrast to the most recent 50-yr empirical probabilities for Florida, but fell short in anticipating the unprecedented level of activity that ensued. Similar results are obtained from hindcasts of total U.S. hurricane activity for 2004.
Abstract
Advances in hurricane climate science allow forecasts of seasonal landfall activity to be made. The authors begin with a review of the forecast methods available in the literature. They then reformulate the methods using a Bayesian probabilistic approach. This allows a direct comparison to be made while focusing on a single hindcast of the 2004 season over Florida. The models, including climatology, are estimated using Gibbs sampling. Diagnostic checks verify convergence and efficient mixing of the samples from each of the models. A below average sea level pressure gradient over the eastern North Atlantic Ocean during May and June in combination with an above average tropospheric-averaged wind index associated, in part, with a strengthening of the Bermuda high pressure during July resulted in an above average probability of at least one Florida hurricane. The relatively high hindcast probabilities for 2004 were in marked contrast to the most recent 50-yr empirical probabilities for Florida, but fell short in anticipating the unprecedented level of activity that ensued. Similar results are obtained from hindcasts of total U.S. hurricane activity for 2004.
Abstract
The authors develop and apply a model that uses hurricane-experience data in counties along the U.S. hurricane coast to give annual exceedence probabilities to maximum tropical cyclone wind events. The model uses a maximum likelihood estimator to determine a linear regression for the scale and shape parameters of the Weibull distribution for maximum wind speed. Model simulations provide quantiles for the probabilities at prescribed hurricane intensities. When the model is run in the raw climatological mode, median probabilities compare favorably with probabilities from the National Hurricane Center’s risk analysis program “HURISK” model. When the model is run in the conditional climatological mode, covariate information in the form of regression equations for the distributional parameters allows probabilities to be estimated that are conditioned on climate factors. Changes to annual hurricane probabilities with respect to a combined effect of a La Niña event and a negative phase of the North Atlantic oscillation mapped from Texas to North Carolina indicate an increased likelihood of hurricanes along much of the coastline. Largest increases are noted along the central Gulf coast.
Abstract
The authors develop and apply a model that uses hurricane-experience data in counties along the U.S. hurricane coast to give annual exceedence probabilities to maximum tropical cyclone wind events. The model uses a maximum likelihood estimator to determine a linear regression for the scale and shape parameters of the Weibull distribution for maximum wind speed. Model simulations provide quantiles for the probabilities at prescribed hurricane intensities. When the model is run in the raw climatological mode, median probabilities compare favorably with probabilities from the National Hurricane Center’s risk analysis program “HURISK” model. When the model is run in the conditional climatological mode, covariate information in the form of regression equations for the distributional parameters allows probabilities to be estimated that are conditioned on climate factors. Changes to annual hurricane probabilities with respect to a combined effect of a La Niña event and a negative phase of the North Atlantic oscillation mapped from Texas to North Carolina indicate an increased likelihood of hurricanes along much of the coastline. Largest increases are noted along the central Gulf coast.
Abstract
Property losses from tornadoes in Florida are estimated by combining a 1-km spatial grid of structural values from the Department of Revenue’s 2014 cadastral database with historical tornado events since 1950. There are 91 180 grid cells in the state with at least some structural value. Total and residential structural values total $942 billion and $619 billion, respectively. Over the period 1950 through 2015 there were 3233 individual tornado reports in the state with a peak frequency during June. The property value exposed to tornadoes is estimated using a geometric model for the path. Annual statewide total and residential structural property exposure to tornadoes is estimated at $171 million and $103 million, respectively. Property exposure to tornadoes peaks in February. A regression model quantifies the relationship between actual losses since 2007 and exposures. A doubling of the residential exposure increases actual recorded losses by 26% since 2007, and a doubling of nonresidential exposure increases losses by 21%, controlling for changes over time. Randomization of the historical tornado paths provides alternative exposure scenarios that are used to determine the probability of extreme loss years. Results from the Monte Carlo algorithm indicate a 1% chance that the annual loss will exceed $430 million and a 0.1% chance that it will exceed $1 billion. These findings, and the procedure to obtain them, should help property insurance and reinsurance companies gauge their risk of losses and prioritize their management actions.
Abstract
Property losses from tornadoes in Florida are estimated by combining a 1-km spatial grid of structural values from the Department of Revenue’s 2014 cadastral database with historical tornado events since 1950. There are 91 180 grid cells in the state with at least some structural value. Total and residential structural values total $942 billion and $619 billion, respectively. Over the period 1950 through 2015 there were 3233 individual tornado reports in the state with a peak frequency during June. The property value exposed to tornadoes is estimated using a geometric model for the path. Annual statewide total and residential structural property exposure to tornadoes is estimated at $171 million and $103 million, respectively. Property exposure to tornadoes peaks in February. A regression model quantifies the relationship between actual losses since 2007 and exposures. A doubling of the residential exposure increases actual recorded losses by 26% since 2007, and a doubling of nonresidential exposure increases losses by 21%, controlling for changes over time. Randomization of the historical tornado paths provides alternative exposure scenarios that are used to determine the probability of extreme loss years. Results from the Monte Carlo algorithm indicate a 1% chance that the annual loss will exceed $430 million and a 0.1% chance that it will exceed $1 billion. These findings, and the procedure to obtain them, should help property insurance and reinsurance companies gauge their risk of losses and prioritize their management actions.
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
Abstract
A flash flood occurred at Milwaukee, Wisconsin on 6 August 1986 as a result of >6 in. (15.2 cm) of rain, much of it falling over a 2-h period. Several possible contributing factors to the excessive rainfall are addressed, as well as a brief overview of the radar imagery and the local National Weather Service (NWS) forecasts issued during the event.
Conventional weather analyses and infrared satellite imagery are used to describe the synoptic-scale weather patterns and cloud features associated with the flash flood. The synoptic patterns are compared with a meteorological composite for heavy rain-producing weather systems associated with relatively warm-topped cloud signatures imbedded in comma-shaped cloud features, as described by Spayd (1982). This composite is referred to as a cyclonic circulation system (CCS). A comparison between the observed synoptic patterns and those predicted by the operational numerical model forecasts is also discussed. A climatological survey is performed to document the frequency of heavy rainfall events associated with weather systems similar to the CCS composite during seven warm seasons.
Results show that the synoptic weather patterns attending the Milwaukee flood were similar in many respects to the CCS composite. While the numerical models were deficient in accurately predicting rainfall amounts, they were more than adequate in forecasting some of the features of the CCS composite. The climatology shows that weather systems resembling the composite appear infrequently on a given day during the warm season. However, rainfall in excess of 5 in. (12.7 cm) occurred in a preferred location of nearly 60% of the cases in which these systems were identified.
This article lends support to the value of pattern recognition from satellite imagery, conventional weather analysis, and forecast model output to alert forecasters to the potential for heavy rainfall.
Abstract
A flash flood occurred at Milwaukee, Wisconsin on 6 August 1986 as a result of >6 in. (15.2 cm) of rain, much of it falling over a 2-h period. Several possible contributing factors to the excessive rainfall are addressed, as well as a brief overview of the radar imagery and the local National Weather Service (NWS) forecasts issued during the event.
Conventional weather analyses and infrared satellite imagery are used to describe the synoptic-scale weather patterns and cloud features associated with the flash flood. The synoptic patterns are compared with a meteorological composite for heavy rain-producing weather systems associated with relatively warm-topped cloud signatures imbedded in comma-shaped cloud features, as described by Spayd (1982). This composite is referred to as a cyclonic circulation system (CCS). A comparison between the observed synoptic patterns and those predicted by the operational numerical model forecasts is also discussed. A climatological survey is performed to document the frequency of heavy rainfall events associated with weather systems similar to the CCS composite during seven warm seasons.
Results show that the synoptic weather patterns attending the Milwaukee flood were similar in many respects to the CCS composite. While the numerical models were deficient in accurately predicting rainfall amounts, they were more than adequate in forecasting some of the features of the CCS composite. The climatology shows that weather systems resembling the composite appear infrequently on a given day during the warm season. However, rainfall in excess of 5 in. (12.7 cm) occurred in a preferred location of nearly 60% of the cases in which these systems were identified.
This article lends support to the value of pattern recognition from satellite imagery, conventional weather analysis, and forecast model output to alert forecasters to the potential for heavy rainfall.
Abstract
Hurricane return levels estimated using historical and geological information are quantitatively compared for Lake Shelby, Alabama. The minimum return level of overwash events recorded in sediment cores is estimated using a modern analog (Hurricane Ivan of 2004) to be 54 m s−1 (105 kt) for a return period of 318 yr based on 11 events over 3500 yr. The expected return level of rare hurricanes in the observed records (1851–2005) at this location and for this return period is estimated using a parametric statistical model and a maximum likelihood procedure to be 73 m s−1 (141 kt), with a lower bound on the 95% confidence interval of 64 m s−1 (124 kt). Results are not significantly different if data are taken from the shorter 1880–2005 period. Thus, the estimated sensitivity of Lake Shelby to overwash events is consistent with the historical record given the model. In fact, assuming the past is similar to the present, the sensitivity of the site to overwash events as estimated from the model is likely more accurately set at 64 m s−1.
Abstract
Hurricane return levels estimated using historical and geological information are quantitatively compared for Lake Shelby, Alabama. The minimum return level of overwash events recorded in sediment cores is estimated using a modern analog (Hurricane Ivan of 2004) to be 54 m s−1 (105 kt) for a return period of 318 yr based on 11 events over 3500 yr. The expected return level of rare hurricanes in the observed records (1851–2005) at this location and for this return period is estimated using a parametric statistical model and a maximum likelihood procedure to be 73 m s−1 (141 kt), with a lower bound on the 95% confidence interval of 64 m s−1 (124 kt). Results are not significantly different if data are taken from the shorter 1880–2005 period. Thus, the estimated sensitivity of Lake Shelby to overwash events is consistent with the historical record given the model. In fact, assuming the past is similar to the present, the sensitivity of the site to overwash events as estimated from the model is likely more accurately set at 64 m s−1.
Abstract
A statistical procedure for estimating the risk of strong winds from hurricanes is demonstrated and applied to several major cities in Florida. The procedure, called the hurricane risk calculator, provides an estimate of wind risk over different length periods and can be applied to any location experiencing this hazard. Results show that the city of Miami can expect to see hurricane winds blowing at 50 m s−1 [45.5–54.5 m s−1 is the 90% confidence interval (CI)] or stronger, on average, once every 12 yr. In comparison, the city of Pensacola can expect to see hurricane winds of 50 m s−1 (46.9–53.1 m s−1, 90% CI) or stronger once every 24 yr. A quantile regression is applied to hurricane wind speeds in the vicinity of Florida. Results show that the strongest hurricanes are getting stronger as a consequence of higher offshore intensification rates.
Abstract
A statistical procedure for estimating the risk of strong winds from hurricanes is demonstrated and applied to several major cities in Florida. The procedure, called the hurricane risk calculator, provides an estimate of wind risk over different length periods and can be applied to any location experiencing this hazard. Results show that the city of Miami can expect to see hurricane winds blowing at 50 m s−1 [45.5–54.5 m s−1 is the 90% confidence interval (CI)] or stronger, on average, once every 12 yr. In comparison, the city of Pensacola can expect to see hurricane winds of 50 m s−1 (46.9–53.1 m s−1, 90% CI) or stronger once every 24 yr. A quantile regression is applied to hurricane wind speeds in the vicinity of Florida. Results show that the strongest hurricanes are getting stronger as a consequence of higher offshore intensification rates.
