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Abstract
The variability of intense (or major) hurricanes in the Atlantic basin is investigated on both intraseasonal and interannual time scales. Differences are highlighted in characteristics between intense hurricanes and the weaker minor hurricanes and tropical storms. Intense hurricanes show a much more peaked annual cycle than do weaker tropical cyclones. Ninety-five percent of all intense hurricane activity occurs during August to October. In addition, over 80% of all intense hurricanes originate from African easterly waves, a much higher proportion than is observed for weaker cyclones. Of all classes of Atlantic basin tropical cyclones, the intense hurricanes display the greatest year-to-year variability. The incidence of intense hurricanes also has decreased during the last two decades. A small portion of this decreased activity appears to be due to an overestimation of hurricane intensity during the period spanning the 1940s through the 1960s. After adjusting for this bias, however, a substantial downward trend in intense hurricane activity during recent years is still apparent. Given that intense hurricanes are responsible for more than 70% of all destruction caused by tropical cyclones in the United States, an understanding is needed of the physical mechanisms for these observed variations of intense hurricane activity.
Abstract
The variability of intense (or major) hurricanes in the Atlantic basin is investigated on both intraseasonal and interannual time scales. Differences are highlighted in characteristics between intense hurricanes and the weaker minor hurricanes and tropical storms. Intense hurricanes show a much more peaked annual cycle than do weaker tropical cyclones. Ninety-five percent of all intense hurricane activity occurs during August to October. In addition, over 80% of all intense hurricanes originate from African easterly waves, a much higher proportion than is observed for weaker cyclones. Of all classes of Atlantic basin tropical cyclones, the intense hurricanes display the greatest year-to-year variability. The incidence of intense hurricanes also has decreased during the last two decades. A small portion of this decreased activity appears to be due to an overestimation of hurricane intensity during the period spanning the 1940s through the 1960s. After adjusting for this bias, however, a substantial downward trend in intense hurricane activity during recent years is still apparent. Given that intense hurricanes are responsible for more than 70% of all destruction caused by tropical cyclones in the United States, an understanding is needed of the physical mechanisms for these observed variations of intense hurricane activity.
Abstract
“Best tracks” are National Hurricane Center (NHC) poststorm analyses of the intensity, central pressure, position, and size of Atlantic and eastern North Pacific basin tropical and subtropical cyclones. This paper estimates the uncertainty (average error) for Atlantic basin best track parameters through a survey of the NHC Hurricane Specialists who maintain and update the Atlantic hurricane database. A comparison is then made with a survey conducted over a decade ago to qualitatively assess changes in the uncertainties. Finally, the implications of the uncertainty estimates for NHC analysis and forecast products as well as for the prediction goals of the Hurricane Forecast Improvement Program are discussed.
Abstract
“Best tracks” are National Hurricane Center (NHC) poststorm analyses of the intensity, central pressure, position, and size of Atlantic and eastern North Pacific basin tropical and subtropical cyclones. This paper estimates the uncertainty (average error) for Atlantic basin best track parameters through a survey of the NHC Hurricane Specialists who maintain and update the Atlantic hurricane database. A comparison is then made with a survey conducted over a decade ago to qualitatively assess changes in the uncertainties. Finally, the implications of the uncertainty estimates for NHC analysis and forecast products as well as for the prediction goals of the Hurricane Forecast Improvement Program are discussed.
This paper describes a predictive relationship between West African rainfall and U.S. hurricane-spawned destruction, which is based on information for the 42-yr period 1949–90. It is shown that above-average rainfall during the previous year along the Gulf of Guinea, in combination with above-average rainfall in the western Sahel during June and July, is linked to hurricane-spawned destruction along the U.S. East Coast occurring after 1 August, which is 10–20 times greater than in years when pre-1 August precipitation for these West African regions is below average. Similar hurricane-spawned damage along the U.S. Gulf Coast shows only a negligible relationship with African rainfall. Hurricane-caused deaths for both U.S. coastal regions also show a similar association with West African rainfall.
This paper describes a predictive relationship between West African rainfall and U.S. hurricane-spawned destruction, which is based on information for the 42-yr period 1949–90. It is shown that above-average rainfall during the previous year along the Gulf of Guinea, in combination with above-average rainfall in the western Sahel during June and July, is linked to hurricane-spawned destruction along the U.S. East Coast occurring after 1 August, which is 10–20 times greater than in years when pre-1 August precipitation for these West African regions is below average. Similar hurricane-spawned damage along the U.S. Gulf Coast shows only a negligible relationship with African rainfall. Hurricane-caused deaths for both U.S. coastal regions also show a similar association with West African rainfall.
The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.
The very strong 1997–98 El Niño was the first major event in which numerous forecasting groups participated in its real-time prediction. A previously developed simple statistical tool—the El Niño–Southern Oscillation Climatology and Persistence (ENSO–CLIPER) model—is utilized as a baseline for determination of skill in forecasting this event. Twelve statistical and dynamical models were available in real time for evaluation. Some of the models were able to outperform ENSO–CLIPER in predicting either the onset or the decay of the 1997–98 El Niño, but none were successful at both for a medium-range two season (6–8 months) lead time. There were no models, including ENSO–CLIPER, able to anticipate even one-half of the actual amplitude of the El Niño's peak at medium-range (6–11 months) lead. In addition, none of the models showed skill (i.e., lower root-mean-square error than ENSO–CLIPER) at the zero season (0–2 months) through the two season (6–8 months) lead times. No dynamical model and only two of the statistical models [the canonical correlation analysis (CCA) and the constructed analog (ANALOG)] outperformed ENSO–CLIPER by more than 5% of the root-mean-square error at the three season (9–11 months) and four season (12–14 months) lead time. El Niño impacts were correctly anticipated by national meteorological centers one half-year in advance, because of the tendency for El Niño events to persist into and peak during the boreal winter. Despite this, the zero to two season (0–8 month) forecasts of the El Niño event itself were no better than ENSO–CLIPER and were, in that sense, not skillful—a conclusion that remains unclear to the general meteorological and oceanographic communities.
Abstract
The tropical cyclone is the largest single-day-impact meteorological event in the United States and worldwide through its effects from storm surge, extreme winds, freshwater flooding, and embedded tornadoes. Fortunately, over the last three decades there have been incredible advances in forecast accuracy, especially for the track of the tropical cyclone’s center. Errors have been cut by two-thirds in just 25 years due to global modeling advances, data assimilation improvements, dramatic increases in observations primarily derived from satellite platforms, and use of ensemble forecast techniques. These four factors have allowed for highly accurate synoptic-scale atmospheric initial conditions and forecasts of the steering flow out through several days into the future. However, such improvements cannot continue indefinitely. It is well known in the atmospheric sciences that there exists an inherent “limit of predictability” because of errors at the smallest scales (microscale—meters and seconds) that eventually cascade up to the largest scales (synoptic scale—thousands of kilometers and several days). While there have been estimates of the limits of predictability for tropical cyclone track prediction in the past, our current capabilities have exceeded those somewhat pessimistic earlier outlooks. This essay discusses the current state of the art for tropical cyclone track prediction and reassesses whether reaching the “limit of predictability” is imminent. The ramifications of this eventual conclusion—whether in the short-term or still decades away—could be critical for all users of tropical cyclone track forecast information, including the emergency management community/governments, the media, the private sector, and the general public.
Abstract
The tropical cyclone is the largest single-day-impact meteorological event in the United States and worldwide through its effects from storm surge, extreme winds, freshwater flooding, and embedded tornadoes. Fortunately, over the last three decades there have been incredible advances in forecast accuracy, especially for the track of the tropical cyclone’s center. Errors have been cut by two-thirds in just 25 years due to global modeling advances, data assimilation improvements, dramatic increases in observations primarily derived from satellite platforms, and use of ensemble forecast techniques. These four factors have allowed for highly accurate synoptic-scale atmospheric initial conditions and forecasts of the steering flow out through several days into the future. However, such improvements cannot continue indefinitely. It is well known in the atmospheric sciences that there exists an inherent “limit of predictability” because of errors at the smallest scales (microscale—meters and seconds) that eventually cascade up to the largest scales (synoptic scale—thousands of kilometers and several days). While there have been estimates of the limits of predictability for tropical cyclone track prediction in the past, our current capabilities have exceeded those somewhat pessimistic earlier outlooks. This essay discusses the current state of the art for tropical cyclone track prediction and reassesses whether reaching the “limit of predictability” is imminent. The ramifications of this eventual conclusion—whether in the short-term or still decades away—could be critical for all users of tropical cyclone track forecast information, including the emergency management community/governments, the media, the private sector, and the general public.
Abstract
Seasonal variability of Atlantic basin tropical cyclones is examined with respect to the monsoon rainfall over West Africa. Variations of intense hurricanes are of the most interest, as they are responsible for over three-quarters of United States tropical cyclone spawned destruction, though they account for only one-fifth of all landfalling cyclones. Intense hurricanes have also shown a strong downward trend during the last few decades. It is these storms that show the largest concurrent association with Africa's western Sahelian June-September rainfall for the years 1949–90.
Though the Sahel is currently experiencing a multidecadal drought, the relationship between Atlantic tropical cyclones and western Sahelian rainfall is not dependent on the similar downward trends in both datasets. A detrended analysis confirms that a strong association still exists, though reduced somewhat in variance explained. Additionally, independent data from the years 1899 to 1948 substantiate the existence of the tropical cyclone-western Sabelian rainfall association.
The fact that the Sahel periodically experiences multidecadal wet and dry regimes suggests that the current Sahelian drought, which began in the late 1960s, could be a temporary condition that may end in the new future. When this occurs, the Atlantic hurricane basin—especially the Caribbean islands and the United States East Coast—will likely see a large increase in intense hurricane activity associated with abundant Sahelian rainfall similar to the period of the late 1940s through the 1960s.
Abstract
Seasonal variability of Atlantic basin tropical cyclones is examined with respect to the monsoon rainfall over West Africa. Variations of intense hurricanes are of the most interest, as they are responsible for over three-quarters of United States tropical cyclone spawned destruction, though they account for only one-fifth of all landfalling cyclones. Intense hurricanes have also shown a strong downward trend during the last few decades. It is these storms that show the largest concurrent association with Africa's western Sahelian June-September rainfall for the years 1949–90.
Though the Sahel is currently experiencing a multidecadal drought, the relationship between Atlantic tropical cyclones and western Sahelian rainfall is not dependent on the similar downward trends in both datasets. A detrended analysis confirms that a strong association still exists, though reduced somewhat in variance explained. Additionally, independent data from the years 1899 to 1948 substantiate the existence of the tropical cyclone-western Sabelian rainfall association.
The fact that the Sahel periodically experiences multidecadal wet and dry regimes suggests that the current Sahelian drought, which began in the late 1960s, could be a temporary condition that may end in the new future. When this occurs, the Atlantic hurricane basin—especially the Caribbean islands and the United States East Coast—will likely see a large increase in intense hurricane activity associated with abundant Sahelian rainfall similar to the period of the late 1940s through the 1960s.
Abstract
Ten years ago, Webster et al. documented a large and significant increase in both the number as well as the percentage of category 4 and 5 hurricanes for all global basins from 1970 to 2004, and this manuscript examines whether those trends have continued when including 10 additional years of data. In contrast to that study, as shown here, the global frequency of category 4 and 5 hurricanes has shown a small, insignificant downward trend while the percentage of category 4 and 5 hurricanes has shown a small, insignificant upward trend between 1990 and 2014. Accumulated cyclone energy globally has experienced a large and significant downward trend during the same period. The primary reason for the increase in category 4 and 5 hurricanes noted in observational datasets from 1970 to 2004 by Webster et al. is concluded to be due to observational improvements at the various global tropical cyclone warning centers, primarily in the first two decades of that study.
Abstract
Ten years ago, Webster et al. documented a large and significant increase in both the number as well as the percentage of category 4 and 5 hurricanes for all global basins from 1970 to 2004, and this manuscript examines whether those trends have continued when including 10 additional years of data. In contrast to that study, as shown here, the global frequency of category 4 and 5 hurricanes has shown a small, insignificant downward trend while the percentage of category 4 and 5 hurricanes has shown a small, insignificant upward trend between 1990 and 2014. Accumulated cyclone energy globally has experienced a large and significant downward trend during the same period. The primary reason for the increase in category 4 and 5 hurricanes noted in observational datasets from 1970 to 2004 by Webster et al. is concluded to be due to observational improvements at the various global tropical cyclone warning centers, primarily in the first two decades of that study.
Abstract
An investigation is conducted to determine how improvements in observing capabilities and technology may have affected scientists’ ability to detect and monitor Saffir–Simpson Hurricane Wind Scale Category 5 hurricanes in the Atlantic Ocean basin during the mid-twentieth century. Previous studies state that there has been an increase in the number of intense hurricanes and attribute this increase to anthropogenic global warming. Other studies claim that the apparent increased hurricane activity is an artifact of better observational capabilities and improved technology for detecting these intense hurricanes. The present study focuses on the 10 most recent Category 5 hurricanes recorded in the Atlantic, from Hurricane Andrew (1992) through Hurricane Felix (2007). These 10 hurricanes are placed into the context of the technology available in the period of 1944–53, the first decade of aircraft reconnaissance. A methodology is created to determine how many of these 10 recent Category 5 hurricanes likely would have been recorded as Category 5 if they had occurred during this period using only the observations that likely would have been available with existing technology and observational networks. Late-1940s and early-1950s best-track intensities are determined for the entire lifetime of these 10 recent Category 5 hurricanes. It is found that likely only 2 of these 10—both Category 5 landfalling hurricanes—would have been recorded as Category 5 hurricanes if they had occurred during the late-1940s period. The results suggest that intensity estimates for extreme tropical cyclones prior to the satellite era are unreliable for trend and variability analysis.
Abstract
An investigation is conducted to determine how improvements in observing capabilities and technology may have affected scientists’ ability to detect and monitor Saffir–Simpson Hurricane Wind Scale Category 5 hurricanes in the Atlantic Ocean basin during the mid-twentieth century. Previous studies state that there has been an increase in the number of intense hurricanes and attribute this increase to anthropogenic global warming. Other studies claim that the apparent increased hurricane activity is an artifact of better observational capabilities and improved technology for detecting these intense hurricanes. The present study focuses on the 10 most recent Category 5 hurricanes recorded in the Atlantic, from Hurricane Andrew (1992) through Hurricane Felix (2007). These 10 hurricanes are placed into the context of the technology available in the period of 1944–53, the first decade of aircraft reconnaissance. A methodology is created to determine how many of these 10 recent Category 5 hurricanes likely would have been recorded as Category 5 if they had occurred during this period using only the observations that likely would have been available with existing technology and observational networks. Late-1940s and early-1950s best-track intensities are determined for the entire lifetime of these 10 recent Category 5 hurricanes. It is found that likely only 2 of these 10—both Category 5 landfalling hurricanes—would have been recorded as Category 5 hurricanes if they had occurred during the late-1940s period. The results suggest that intensity estimates for extreme tropical cyclones prior to the satellite era are unreliable for trend and variability analysis.
Abstract
The Dvorak technique is used operationally by meteorological agencies throughout the world for estimating tropical cyclone intensity and position. The technique consists of constraints that put a maximum threshold for which the final T-number, relating directly to intensity, can change during a certain time interval (6, 12, 18, and 24 h). There are cases when these constraints could be broken, especially during rapid intensification. This research tests whether the constraints used for intensity change are warranted or need to be changed. A database of cases with the largest intensity changes for 2000–17 Atlantic tropical cyclones was compiled. A reconnaissance or scatterometer “fix” is required within 3 h of both the beginning and ending of the period for each case to inform the best track and to be included for analysis. Dvorak classifications from the Tropical Analysis and Forecast Branch are noted for each case, which includes the initial and final T-numbers, current intensity numbers, and data T-numbers. Statistical parameters, including correlations, intensity errors, absolute intensity errors, root-mean-square errors, and significance tests are calculated and analyzed for each period. Results suggest that the T-number constraints for the 18- and 24-h periods could be increased to a 2.5 and a 3.0, respectively. However, results also suggest that the constraints for the 6- and 12-h time intervals should remain the same.
Abstract
The Dvorak technique is used operationally by meteorological agencies throughout the world for estimating tropical cyclone intensity and position. The technique consists of constraints that put a maximum threshold for which the final T-number, relating directly to intensity, can change during a certain time interval (6, 12, 18, and 24 h). There are cases when these constraints could be broken, especially during rapid intensification. This research tests whether the constraints used for intensity change are warranted or need to be changed. A database of cases with the largest intensity changes for 2000–17 Atlantic tropical cyclones was compiled. A reconnaissance or scatterometer “fix” is required within 3 h of both the beginning and ending of the period for each case to inform the best track and to be included for analysis. Dvorak classifications from the Tropical Analysis and Forecast Branch are noted for each case, which includes the initial and final T-numbers, current intensity numbers, and data T-numbers. Statistical parameters, including correlations, intensity errors, absolute intensity errors, root-mean-square errors, and significance tests are calculated and analyzed for each period. Results suggest that the T-number constraints for the 18- and 24-h periods could be increased to a 2.5 and a 3.0, respectively. However, results also suggest that the constraints for the 6- and 12-h time intervals should remain the same.
Abstract
A statistical prediction method, which is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions, and climatology, is developed for the El Niño–Southern Oscillation (ENSO) phenomena. The selection of predictors is by design intended to avoid any pretense of predictive ability based on “model physics” and the like, but rather is to specify the optimal “no-skill” forecast as a baseline comparison for more sophisticated forecast methods. Multiple least squares regression using the method of leaps and bounds is employed to test a total of 14 possible predictors for the selection of the best predictors, based upon 1950–94 developmental data. A range of zero to four predictors were chosen in developing 12 separate regression models, developed separately for each initial calendar month. The predictands to be forecast include the Southern Oscillation (pressure) index (SOI) and the Niño 1+2, Niño 3, Niño 4, and Niño 3.4 SST indices for the equatorial eastern and central Pacific at lead times ranging from zero seasons (0–2 months) through seven seasons (21–23 months). Though hindcast ability is strongly seasonally dependent, substantial improvement is achieved over simple persistence wherein largest gains occur for two–seven-season (6–23 months) lead times. For example, expected maximum forecast ability for the Niño 3.4 SST region, depending on the initial date, reaches 92%, 85%, 64%, 41%, 36%, 24%, 24%, and 28% of variance for leads of zero to seven seasons. Comparable maxima of persistence only forecasts explain 92%, 77%, 50%, 17%, 6%, 14%, 21%, and 17%, respectively. More sophisticated statistical and dynamic forecasting models are encouraged to utilize this ENSO-CLIPER model in place of persistence when assessing whether they have achieved forecasting skill; to this end, real-time results for this model are made available via a Web site.
Abstract
A statistical prediction method, which is based entirely on the optimal combination of persistence, month-to-month trend of initial conditions, and climatology, is developed for the El Niño–Southern Oscillation (ENSO) phenomena. The selection of predictors is by design intended to avoid any pretense of predictive ability based on “model physics” and the like, but rather is to specify the optimal “no-skill” forecast as a baseline comparison for more sophisticated forecast methods. Multiple least squares regression using the method of leaps and bounds is employed to test a total of 14 possible predictors for the selection of the best predictors, based upon 1950–94 developmental data. A range of zero to four predictors were chosen in developing 12 separate regression models, developed separately for each initial calendar month. The predictands to be forecast include the Southern Oscillation (pressure) index (SOI) and the Niño 1+2, Niño 3, Niño 4, and Niño 3.4 SST indices for the equatorial eastern and central Pacific at lead times ranging from zero seasons (0–2 months) through seven seasons (21–23 months). Though hindcast ability is strongly seasonally dependent, substantial improvement is achieved over simple persistence wherein largest gains occur for two–seven-season (6–23 months) lead times. For example, expected maximum forecast ability for the Niño 3.4 SST region, depending on the initial date, reaches 92%, 85%, 64%, 41%, 36%, 24%, 24%, and 28% of variance for leads of zero to seven seasons. Comparable maxima of persistence only forecasts explain 92%, 77%, 50%, 17%, 6%, 14%, 21%, and 17%, respectively. More sophisticated statistical and dynamic forecasting models are encouraged to utilize this ENSO-CLIPER model in place of persistence when assessing whether they have achieved forecasting skill; to this end, real-time results for this model are made available via a Web site.
