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- Author or Editor: James B. Elsner x
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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
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
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.
Abstract
A recent study showed the importance of tornado energy as a factor in a model for tornado deaths and injuries (casualties). The model was additive under the assumption of uniform threat. Here, we test two explicit hypotheses designed to examine this additive assumption. The first hypothesis concerns energy dissipation’s effect conditional on population density and the second concerns population’s effect conditional on energy. Both hypotheses are tested using a regression model that contains the product of population density and energy dissipation. Results show that the elasticity of casualties with respect to energy dissipation increases with population density. That is, the percentage increase in casualties with increasing energy dissipation increases with population density. Similarly, the elasticity of casualties with respect to population density increases with energy dissipation. That is, the percentage increase in casualties with increasing population density increases with energy dissipation. Allowing energy and population elasticities to be conditional rather than constant provides a more complete description of how tornado casualties are influenced by these two important factors.
Abstract
A recent study showed the importance of tornado energy as a factor in a model for tornado deaths and injuries (casualties). The model was additive under the assumption of uniform threat. Here, we test two explicit hypotheses designed to examine this additive assumption. The first hypothesis concerns energy dissipation’s effect conditional on population density and the second concerns population’s effect conditional on energy. Both hypotheses are tested using a regression model that contains the product of population density and energy dissipation. Results show that the elasticity of casualties with respect to energy dissipation increases with population density. That is, the percentage increase in casualties with increasing energy dissipation increases with population density. Similarly, the elasticity of casualties with respect to population density increases with energy dissipation. That is, the percentage increase in casualties with increasing population density increases with energy dissipation. Allowing energy and population elasticities to be conditional rather than constant provides a more complete description of how tornado casualties are influenced by these two important factors.
Abstract
Nighttime minimum temperatures at the Tallahassee Regional Airport (TLH) are colder in comparison with surrounding locations and other parts of the city, especially during the cool season (TLH minimum temperature anomaly). These cold events are examined using the one-dimensional Oregon State University atmospheric boundary layer (ABL) model including a two-layer model of soil hydrology. The model is used for 12-h forecasts of the ABL parameters, such as surface fluxes, surface inversion height, and minimum temperature when clear, calm synoptic conditions existed over the region at night. The minimum temperature forecasts are performed at TLH and a nearby location. Cooling in the surface inversion layer is examined in terms of turbulence and clear-air radiative effects, and it is confirmed that the lower temperatures at TLH are related to the clear-air radiative cooling even in the lower part of the inversion layer but not to cold-air drainage. Stability, ABL height, and surface inversion height are examined with respect to a potential temperature curvature. Turbulent exchanges in the surface boundary layer are also taken into account. The model is able to simulate the nocturnal evolution of air temperatures well. Besides the soil moisture, the value of the roughness length momentum has a substantial effect on temperature forecasts in the model. The best overall agreement for the minimum temperature prediction over TLH is obtained using equal values for the roughness lengths of heat and momentum. Finally, use of the ABL model with its surface energy balance and crude radiative parameterization package under negligible synoptic-scale forcing can be valuable to a forecaster in predicting the daily maximum temperature drop.
Abstract
Nighttime minimum temperatures at the Tallahassee Regional Airport (TLH) are colder in comparison with surrounding locations and other parts of the city, especially during the cool season (TLH minimum temperature anomaly). These cold events are examined using the one-dimensional Oregon State University atmospheric boundary layer (ABL) model including a two-layer model of soil hydrology. The model is used for 12-h forecasts of the ABL parameters, such as surface fluxes, surface inversion height, and minimum temperature when clear, calm synoptic conditions existed over the region at night. The minimum temperature forecasts are performed at TLH and a nearby location. Cooling in the surface inversion layer is examined in terms of turbulence and clear-air radiative effects, and it is confirmed that the lower temperatures at TLH are related to the clear-air radiative cooling even in the lower part of the inversion layer but not to cold-air drainage. Stability, ABL height, and surface inversion height are examined with respect to a potential temperature curvature. Turbulent exchanges in the surface boundary layer are also taken into account. The model is able to simulate the nocturnal evolution of air temperatures well. Besides the soil moisture, the value of the roughness length momentum has a substantial effect on temperature forecasts in the model. The best overall agreement for the minimum temperature prediction over TLH is obtained using equal values for the roughness lengths of heat and momentum. Finally, use of the ABL model with its surface energy balance and crude radiative parameterization package under negligible synoptic-scale forcing can be valuable to a forecaster in predicting the daily maximum temperature drop.
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
The strongest hurricanes over the North Atlantic Ocean are getting stronger, with the increase related to rising ocean temperature. Here, the authors develop a procedure for estimating future wind losses from hurricanes and apply it to Eglin Air Force Base along the northern coast of Florida. The method combines models of the statistical distributions for extreme wind speed and average sea surface temperature over the Gulf of Mexico with dynamical models for tropical cyclone wind fields and damage losses. Results show that the 1-in-100-yr hurricane from the twentieth century picked at random to occur in the year 2100 would result in wind damage that is 36% [(13%, 76%) = 90% confidence interval] greater solely as a consequence of the projected warmer waters in the Gulf of Mexico. The method can be applied elsewhere along the coast with modeling assumptions modified for regional conditions.
Abstract
The strongest hurricanes over the North Atlantic Ocean are getting stronger, with the increase related to rising ocean temperature. Here, the authors develop a procedure for estimating future wind losses from hurricanes and apply it to Eglin Air Force Base along the northern coast of Florida. The method combines models of the statistical distributions for extreme wind speed and average sea surface temperature over the Gulf of Mexico with dynamical models for tropical cyclone wind fields and damage losses. Results show that the 1-in-100-yr hurricane from the twentieth century picked at random to occur in the year 2100 would result in wind damage that is 36% [(13%, 76%) = 90% confidence interval] greater solely as a consequence of the projected warmer waters in the Gulf of Mexico. The method can be applied elsewhere along the coast with modeling assumptions modified for regional conditions.
Abstract
The statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climatological description of tornado occurrence rates across the tornado-prone region of the Great Plains.
Abstract
The statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climatological description of tornado occurrence rates across the tornado-prone region of the Great Plains.
