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Abstract
Hurricanes cause drastic social problems as well as generate huge economic losses. A reliable forecast of the level of hurricane activity covering the next several seasons has the potential to mitigate against such losses through improvements in preparedness and insurance mechanisms. Here a statistical algorithm is developed to predict North Atlantic hurricane activity out to 5 yr. The algorithm has two components: a time series model to forecast average hurricane-season Atlantic sea surface temperature (SST), and a regression model to forecast the hurricane rate given the predicted SST value. The algorithm uses Monte Carlo sampling to generate distributions for the predicted SST and model coefficients. For a given forecast year, a predicted hurricane count is conditional on a sampled predicted value of Atlantic SST. Thus forecasts are samples of hurricane counts for each future year. Model skill is evaluated over the period 1997–2005 and compared against climatology, persistence, and other multiseasonal forecasts issued during this time period. Results indicate that the algorithm will likely improve on earlier efforts and perhaps carry enough skill to be useful in the long-term management of hurricane risk.
Abstract
Hurricanes cause drastic social problems as well as generate huge economic losses. A reliable forecast of the level of hurricane activity covering the next several seasons has the potential to mitigate against such losses through improvements in preparedness and insurance mechanisms. Here a statistical algorithm is developed to predict North Atlantic hurricane activity out to 5 yr. The algorithm has two components: a time series model to forecast average hurricane-season Atlantic sea surface temperature (SST), and a regression model to forecast the hurricane rate given the predicted SST value. The algorithm uses Monte Carlo sampling to generate distributions for the predicted SST and model coefficients. For a given forecast year, a predicted hurricane count is conditional on a sampled predicted value of Atlantic SST. Thus forecasts are samples of hurricane counts for each future year. Model skill is evaluated over the period 1997–2005 and compared against climatology, persistence, and other multiseasonal forecasts issued during this time period. Results indicate that the algorithm will likely improve on earlier efforts and perhaps carry enough skill to be useful in the long-term management of hurricane risk.
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.
The power dissipation of Atlantic tropical cyclones has risen dramatically during the last decades and the increase is correlated with an increase in the underlying sea surface temperature (SST) at low (decadal) frequencies. Because of the large positive correlation between global mean surface air temperature (GT) and Atlantic SST it has been speculated that increases in the power dissipation might, in part, be related to human activity. Here we investigate the question of the relationship between GT and hurricane power dissipation directly using statistical analysis and show that after removing the effect of SST, the correlation between GT and hurricane power dissipation is negative. This suggests that the positive influence of global temperature on Atlantic hurricanes appears to be limited to an indirect connection with tropical Atlantic SST. We also show that the relationship between hurricane power dissipation and Atlantic SST is significant at the high-frequency time scales. El Niño–Southern Oscillation (ENSO) plays an important role in statistically explaining the variations in hurricane power at these higher frequencies.
The power dissipation of Atlantic tropical cyclones has risen dramatically during the last decades and the increase is correlated with an increase in the underlying sea surface temperature (SST) at low (decadal) frequencies. Because of the large positive correlation between global mean surface air temperature (GT) and Atlantic SST it has been speculated that increases in the power dissipation might, in part, be related to human activity. Here we investigate the question of the relationship between GT and hurricane power dissipation directly using statistical analysis and show that after removing the effect of SST, the correlation between GT and hurricane power dissipation is negative. This suggests that the positive influence of global temperature on Atlantic hurricanes appears to be limited to an indirect connection with tropical Atlantic SST. We also show that the relationship between hurricane power dissipation and Atlantic SST is significant at the high-frequency time scales. El Niño–Southern Oscillation (ENSO) plays an important role in statistically explaining the variations in hurricane power at these higher frequencies.
The authors demonstrate a statistical model for the time it takes a manuscript to be accepted for publication. The manuscript received and accepted dates from published manuscripts with the term “hurricane” in the title are obtained from the American Meteorological Society's online publication search feature. The time to acceptance as the difference in days between these two dates is modeled using a Bayesian approach. Assuming an article picked at random gets published, draws from the posterior distribution of the modeled time-to-acceptance parameter indicate about a 12% chance that it will spend more than 210 days (7 months) in review. The model can be adapted to fit similar data obtained using other search criteria.
The authors demonstrate a statistical model for the time it takes a manuscript to be accepted for publication. The manuscript received and accepted dates from published manuscripts with the term “hurricane” in the title are obtained from the American Meteorological Society's online publication search feature. The time to acceptance as the difference in days between these two dates is modeled using a Bayesian approach. Assuming an article picked at random gets published, draws from the posterior distribution of the modeled time-to-acceptance parameter indicate about a 12% chance that it will spend more than 210 days (7 months) in review. The model can be adapted to fit similar data obtained using other search criteria.
Abstract
Time series of annual hurricane counts are examined using a changepoint analysis. The approach simulates posterior distributions of the Poisson-rate parameter using Gibbs sampling. A posterior distribution is a distribution of a parameter conditional on the data. The analysis is first performed on the annual series of major North Atlantic hurricane counts from the twentieth century. Results show significant shifts in hurricane rates during the middle 1940s, the middle 1960s, and at 1995, consistent with earlier published results. The analysis is then applied to U.S. hurricane activity. Results show no abrupt changes in overall coastal hurricane rates during the twentieth century. In contrast, the record of Florida hurricanes indicates downward shifts during the early 1950s and the late 1960s. The shifts result from fewer hurricanes passing through the Bahamas and the western Caribbean Sea. No significant rate shifts are noted along either the Gulf or East Coasts. Climate influences on coastal hurricane activity are then examined. Results show a significant reduction in U.S. hurricane activity during strong El Niño events and during the positive phase of the North Atlantic Oscillation (NAO). ENSO effects are prominent over Florida while NAO effects are concentrated along the Gulf Coast.
Abstract
Time series of annual hurricane counts are examined using a changepoint analysis. The approach simulates posterior distributions of the Poisson-rate parameter using Gibbs sampling. A posterior distribution is a distribution of a parameter conditional on the data. The analysis is first performed on the annual series of major North Atlantic hurricane counts from the twentieth century. Results show significant shifts in hurricane rates during the middle 1940s, the middle 1960s, and at 1995, consistent with earlier published results. The analysis is then applied to U.S. hurricane activity. Results show no abrupt changes in overall coastal hurricane rates during the twentieth century. In contrast, the record of Florida hurricanes indicates downward shifts during the early 1950s and the late 1960s. The shifts result from fewer hurricanes passing through the Bahamas and the western Caribbean Sea. No significant rate shifts are noted along either the Gulf or East Coasts. Climate influences on coastal hurricane activity are then examined. Results show a significant reduction in U.S. hurricane activity during strong El Niño events and during the positive phase of the North Atlantic Oscillation (NAO). ENSO effects are prominent over Florida while NAO effects are concentrated along the Gulf Coast.
Abstract
The authors provide a statistical and physical basis for understanding regional variations in major hurricane activity along the U.S. coastline on long timescales. Current statistical models of hurricane activity are focused on the frequency of events over the entire North Atlantic basin. The exception is the lead author’s previous work, which models the occurrence of hurricanes over the Caribbean Sea, Gulf of Mexico, and the southeast U.S. coast separately. Here the authors use statistics to analyze data from historical and paleoclimatic records to expand this work. In particular, an inverse correlation in major hurricane activity across latitudes at various timescales is articulated. When activity is above normal at high latitudes it tends to be below normal at low latitudes and vice versa. Past research, paleoclimatic records, and historical data hint at the potential of using the North Atlantic oscillation (NAO) as an indicator of where storms will likely track over long timescales. An excited (relaxed) NAO is associated with higher (lower) latitude recurving (nonrecurving) storms. The Gulf (East) Coast is more susceptible to a major hurricane strike during a relaxed (excited) NAO.
Abstract
The authors provide a statistical and physical basis for understanding regional variations in major hurricane activity along the U.S. coastline on long timescales. Current statistical models of hurricane activity are focused on the frequency of events over the entire North Atlantic basin. The exception is the lead author’s previous work, which models the occurrence of hurricanes over the Caribbean Sea, Gulf of Mexico, and the southeast U.S. coast separately. Here the authors use statistics to analyze data from historical and paleoclimatic records to expand this work. In particular, an inverse correlation in major hurricane activity across latitudes at various timescales is articulated. When activity is above normal at high latitudes it tends to be below normal at low latitudes and vice versa. Past research, paleoclimatic records, and historical data hint at the potential of using the North Atlantic oscillation (NAO) as an indicator of where storms will likely track over long timescales. An excited (relaxed) NAO is associated with higher (lower) latitude recurving (nonrecurving) storms. The Gulf (East) Coast is more susceptible to a major hurricane strike during a relaxed (excited) NAO.
Abstract
The return-flow of low-level air from the Gulf of Mexico over the southeast United States during the cool season is studied using numerical models. The key models are a newly developed airmass transformation (AMT) model and a one-dimensional planetary boundary layer (PBL) model. Both are employed to examine the thermodynamic structure over and to the north of the Gulf. Model errors for predicting minimum, maximum, and dewpoint temperatures at the surface during both offshore and onshore phases of the return-flow cycle are analyzed. PBL model forecasts indicate soil moisture values obtained from the Eta Model improve accuracy. It is shown that forecasts of maximum temperature for coastal locations are sensitive to the soil moisture used in the PBL model. The AMT model performs well in determining boundary layer parameters since it includes horizontal advective processes. The AMT model is also able to predict the regional differences caused by different surface forcing while passing over land or sea. Results lead to a strategy for making predictions during cool-season return-flow events over and around the Gulf of Mexico.
Abstract
The return-flow of low-level air from the Gulf of Mexico over the southeast United States during the cool season is studied using numerical models. The key models are a newly developed airmass transformation (AMT) model and a one-dimensional planetary boundary layer (PBL) model. Both are employed to examine the thermodynamic structure over and to the north of the Gulf. Model errors for predicting minimum, maximum, and dewpoint temperatures at the surface during both offshore and onshore phases of the return-flow cycle are analyzed. PBL model forecasts indicate soil moisture values obtained from the Eta Model improve accuracy. It is shown that forecasts of maximum temperature for coastal locations are sensitive to the soil moisture used in the PBL model. The AMT model performs well in determining boundary layer parameters since it includes horizontal advective processes. The AMT model is also able to predict the regional differences caused by different surface forcing while passing over land or sea. Results lead to a strategy for making predictions during cool-season return-flow events over and around the Gulf of Mexico.
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 climate of 2015 was characterized by a strong El Niño, global warmth, and record-setting tropical cyclone (TC) intensity for western North Pacific typhoons. In this study, the highest TC intensity in 32 years (1984–2015) is shown to be a consequence of above normal TC activity—following natural internal variation—and greater efficiency of intensity. The efficiency of intensity (EINT) is termed the “blasting” effect and refers to typhoon intensification at the expense of occurrence. Statistical models show that the EINT is mostly due to the anomalous warmth in the environment indicated by global mean sea surface temperature. In comparison, the EINT due to El Niño is negligible. This implies that the record-setting intensity of 2015 might not have occurred without environmental warming and suggests that a year with even greater TC intensity is possible in the near future when above normal activity coincides with another record EINT due to continued multidecadal warming.
Abstract
The climate of 2015 was characterized by a strong El Niño, global warmth, and record-setting tropical cyclone (TC) intensity for western North Pacific typhoons. In this study, the highest TC intensity in 32 years (1984–2015) is shown to be a consequence of above normal TC activity—following natural internal variation—and greater efficiency of intensity. The efficiency of intensity (EINT) is termed the “blasting” effect and refers to typhoon intensification at the expense of occurrence. Statistical models show that the EINT is mostly due to the anomalous warmth in the environment indicated by global mean sea surface temperature. In comparison, the EINT due to El Niño is negligible. This implies that the record-setting intensity of 2015 might not have occurred without environmental warming and suggests that a year with even greater TC intensity is possible in the near future when above normal activity coincides with another record EINT due to continued multidecadal warming.
