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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.
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
This study investigates tropical cyclones of the past two decades (1990–2010) and the connection, if any, between their size and their ability to subsequently undergo rapid intensification (RI). Three different parameters are chosen to define the size of a tropical cyclone: radius of maximum wind (RMW), the average 34-knot (kt; 1 kt = 0.51 m s−1) radius (AR34), and the radius of the outermost closed isobar (ROCI). The data for this study, coming from the North Atlantic hurricane database second generation (HURDAT2), as well as the extended best-track dataset, are organized into 24-h intervals of either RI or slow intensification/constant intensity periods (non-RI periods). Each interval includes the intensity (maximum sustained surface wind speed), RMW, AR34, and ROCI at the beginning of the period and the change of intensity during the subsequent 24-h period. Results indicate that the ability to undergo RI shows significant sensitivity to initial size. Comparisons between RI and non-RI cyclones confirm that tropical cyclones that undergo RI are more likely to be smaller initially than those that do not. Analyses show that the RMW and AR34 have the strongest negative correlation with the change of intensity. Scatterplots imply there is a general maximum size threshold for RMW and AR34, above which RI is extremely rare. In contrast, the overall size of the tropical cyclones, as measured by ROCI, appears to have little to no relationship with subsequent intensification. The results of this work suggest that intensity forecasts and RI predictions in particular may be aided by the use of the initial size as measured by RMW and AR34.
Abstract
This study investigates tropical cyclones of the past two decades (1990–2010) and the connection, if any, between their size and their ability to subsequently undergo rapid intensification (RI). Three different parameters are chosen to define the size of a tropical cyclone: radius of maximum wind (RMW), the average 34-knot (kt; 1 kt = 0.51 m s−1) radius (AR34), and the radius of the outermost closed isobar (ROCI). The data for this study, coming from the North Atlantic hurricane database second generation (HURDAT2), as well as the extended best-track dataset, are organized into 24-h intervals of either RI or slow intensification/constant intensity periods (non-RI periods). Each interval includes the intensity (maximum sustained surface wind speed), RMW, AR34, and ROCI at the beginning of the period and the change of intensity during the subsequent 24-h period. Results indicate that the ability to undergo RI shows significant sensitivity to initial size. Comparisons between RI and non-RI cyclones confirm that tropical cyclones that undergo RI are more likely to be smaller initially than those that do not. Analyses show that the RMW and AR34 have the strongest negative correlation with the change of intensity. Scatterplots imply there is a general maximum size threshold for RMW and AR34, above which RI is extremely rare. In contrast, the overall size of the tropical cyclones, as measured by ROCI, appears to have little to no relationship with subsequent intensification. The results of this work suggest that intensity forecasts and RI predictions in particular may be aided by the use of the initial size as measured by RMW and AR34.
Abstract
Western Sahelian rainfall during the primary rainy season of June through September is shown to he significantly associated with concurrent intense U.S. landfalling hurricanes during the last 92 years. The meet intense hurricanes (i.e., Saffir–Simpson Scale Category 3, 4, or 5) have an especially strong relationship with Sahelian rainfall, whereas weaker hurricanes show little or no association. The hurricane-Sahelian rainfall association is most evident along the U.S. East Coast but is negligible in the U.S. Gulf Coast region.
Abstract
Western Sahelian rainfall during the primary rainy season of June through September is shown to he significantly associated with concurrent intense U.S. landfalling hurricanes during the last 92 years. The meet intense hurricanes (i.e., Saffir–Simpson Scale Category 3, 4, or 5) have an especially strong relationship with Sahelian rainfall, whereas weaker hurricanes show little or no association. The hurricane-Sahelian rainfall association is most evident along the U.S. East Coast but is negligible in the U.S. Gulf Coast region.
Abstract
A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.
Abstract
A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.
Abstract
The results of a simulation study of multiple regression prediction models for meteorological forecasting are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations (LAD) and least (sum of) squared deviations (LSD) regression models are examined on five populations constructed from meteorological data. Artificial skill is shown to be a product of small sample size, LSD regression, and nonrepresentative data. Validation of sample results is examined, and LAD regression is found to be superior to LSD regression when sample size is small and nonrepresentative data are present.
Abstract
The results of a simulation study of multiple regression prediction models for meteorological forecasting are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations (LAD) and least (sum of) squared deviations (LSD) regression models are examined on five populations constructed from meteorological data. Artificial skill is shown to be a product of small sample size, LSD regression, and nonrepresentative data. Validation of sample results is examined, and LAD regression is found to be superior to LSD regression when sample size is small and nonrepresentative data are present.
Abstract
An estimator of shrinkage based on information contained in a single sample is presented and the results of a simulation study are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations and least (sum of) squared deviations regression models are examined on the estimator. A single-sample estimator of shrinkage based on drop-one cross-validation is shown to be highly accurate under a wide variety of research conditions.
Abstract
An estimator of shrinkage based on information contained in a single sample is presented and the results of a simulation study are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations and least (sum of) squared deviations regression models are examined on the estimator. A single-sample estimator of shrinkage based on drop-one cross-validation is shown to be highly accurate under a wide variety of research conditions.
Abstract
More than 90% of all seasonal Atlantic tropical cyclone activity typically occurs after 1 August. A strong predictive potential exists that allows seasonal forecasts of Atlantic basin tropical cyclone activity to be issued by 1 August, prior to the start of the active portion of the hurricane season. Predictors include June-July meteorological information of the stratospheric quasi-biennial oscillation (QBO), West African rainfall, the El Niño-Southern Oscillation (ENSO) as well as sea level pressure anomalies (SLPA), and the upper-tropospheric zonal-wind anomalies (ZWA) in the Caribbean basin.
Use of a combination of these global and regional predictors provides a basis for making cross-validated (jackknifed) 1 August hindcasts of subsequent Atlantic seasonal tropical cyclone activity that show substantial skill over climatology. This relationship is demonstrated in 41 years of hindcasts of the 1950-90 seasons. It is possible to independently explain more than 60% of the year-to-year variability associated with intense (category 3–4–5) hurricane activity. This is significant because over 70% of all United States tropical cyclone damage comes from intense hurricanes, and over 98% of intense hurricane activity occurs after 1 August.
Empirical evidence suggests that least sum of absolute deviations (LAD) regression yields substantially more improved cross-validated results than an analogous procedure based on ordinary least sum of squared deviations (OLS) regression. This improvement surprisingly occurs even with the squared Pearson product-moment correlation coefficient for which one might anticipate OLS regression to yield better cross-validated results than LAD regression.
Abstract
More than 90% of all seasonal Atlantic tropical cyclone activity typically occurs after 1 August. A strong predictive potential exists that allows seasonal forecasts of Atlantic basin tropical cyclone activity to be issued by 1 August, prior to the start of the active portion of the hurricane season. Predictors include June-July meteorological information of the stratospheric quasi-biennial oscillation (QBO), West African rainfall, the El Niño-Southern Oscillation (ENSO) as well as sea level pressure anomalies (SLPA), and the upper-tropospheric zonal-wind anomalies (ZWA) in the Caribbean basin.
Use of a combination of these global and regional predictors provides a basis for making cross-validated (jackknifed) 1 August hindcasts of subsequent Atlantic seasonal tropical cyclone activity that show substantial skill over climatology. This relationship is demonstrated in 41 years of hindcasts of the 1950-90 seasons. It is possible to independently explain more than 60% of the year-to-year variability associated with intense (category 3–4–5) hurricane activity. This is significant because over 70% of all United States tropical cyclone damage comes from intense hurricanes, and over 98% of intense hurricane activity occurs after 1 August.
Empirical evidence suggests that least sum of absolute deviations (LAD) regression yields substantially more improved cross-validated results than an analogous procedure based on ordinary least sum of squared deviations (OLS) regression. This improvement surprisingly occurs even with the squared Pearson product-moment correlation coefficient for which one might anticipate OLS regression to yield better cross-validated results than LAD regression.
Abstract
This is the third in a series of papers describing the potential for the seasonal forecasting of Atlantic basin tropical cyclone activity. Earlier papers by the authors describe seasonal prediction from 1 December of the previous year and from 1 August of the current year; this work demonstrates the degree of predictability by 1 June, the “official” beginning of the hurricane season. Through three groupings consisting of 13 separate predictors, hindcasts are made that explain 51%–72% of the variability as measured by cross-validated agreement coefficients for eight measures of seasonal tropical cyclone activity. The three groupings of predictors include 1) an extrapolation of quasi-biennial oscillation of 50- and 30-mb zonal winds and the vertical shear between the 50- and 30-mb zonal winds (three predictors); 2) West African rainfall, sea level pressure, and temperature data (four predictors); and 3) Caribbean basin and El Niño–Southern Oscillation information including Caribbean 200-mb zonal winds and sea level pressures, equatorial eastern Pacific sea surface temperatures and Southern Oscillation index values, and their changes in time (six predictors). The cross validation is carried out using least sum of absolute deviations regression that provides an efficient procedure for the maximum agreement measure criterion. Corrected intense hurricane data for the 1950s and 1960s have been incorporated into the forecasts. Comparisons of these 1 June forecast results with forecast results from 1 December of the year previous and 1 August of the current year are also given.
Abstract
This is the third in a series of papers describing the potential for the seasonal forecasting of Atlantic basin tropical cyclone activity. Earlier papers by the authors describe seasonal prediction from 1 December of the previous year and from 1 August of the current year; this work demonstrates the degree of predictability by 1 June, the “official” beginning of the hurricane season. Through three groupings consisting of 13 separate predictors, hindcasts are made that explain 51%–72% of the variability as measured by cross-validated agreement coefficients for eight measures of seasonal tropical cyclone activity. The three groupings of predictors include 1) an extrapolation of quasi-biennial oscillation of 50- and 30-mb zonal winds and the vertical shear between the 50- and 30-mb zonal winds (three predictors); 2) West African rainfall, sea level pressure, and temperature data (four predictors); and 3) Caribbean basin and El Niño–Southern Oscillation information including Caribbean 200-mb zonal winds and sea level pressures, equatorial eastern Pacific sea surface temperatures and Southern Oscillation index values, and their changes in time (six predictors). The cross validation is carried out using least sum of absolute deviations regression that provides an efficient procedure for the maximum agreement measure criterion. Corrected intense hurricane data for the 1950s and 1960s have been incorporated into the forecasts. Comparisons of these 1 June forecast results with forecast results from 1 December of the year previous and 1 August of the current year are also given.
