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William M. Gray, Christopher W. Landsea, Paul W. Mielke Jr., and Kenneth J. Berry

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.

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Paul W. Mielke Jr., Kenneth J. Berry, Christopher W. Landsea, and William M. Gray

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.

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Paul W. Mielke Jr., Kenneth J. Berry, Christopher W. Landsea, and William M. Gray

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.

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William M. Gray, Christopher W. Landsea, Paul W. Mielke Jr., and Kenneth J. Berry

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.

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William M. Gray, Christopher W. Landsea, Paul W. Mielke Jr., and Kenneth J. Berry

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.

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