The "truth" campaign was created to change youth attitudes about tobacco and to reduce teen tobacco use throughout Florida by using youth-driven advertising, public relations, and advocacy. Results of the campaign include a 92 percent brand awareness rate among teens, a 15 percent rise in teens who agree with key attitudinal statements about smoking, a 19.4 percent decline in smoking among middle school students, and a 8.0 percent decline among high school students. States committed to results-oriented youth anti-tobacco campaigns should look to Florida's "truth" campaign as a model that effectively places youth at the helm of anti-tobacco efforts.

Threshold estimation in the Peaks Over Threshold (POT) method and the impact of the estimation method on the calculation of high return period quantiles and their uncertainty (or confidence intervals) are issues that are still unresolved. In the past, methods based on goodness of fit tests and EDF‐statistics have yielded satisfactory results, but their use has not yet been systematized. This paper proposes a methodology for automatic threshold estimation, based on the Anderson‐Darling EDF‐statistic and goodness of fit test. When combined with bootstrapping techniques, this methodology can be used to quantify both the uncertainty of threshold estimation and its impact on the uncertainty of high return period quantiles. This methodology was applied to several simulated series and to four precipitation/river flow data series. The results obtained confirmed its robustness. For the measured series, the estimated thresholds corresponded to those obtained by nonautomatic methods. Moreover, even though the uncertainty of the threshold estimation was high, this did not have a significant effect on the width of the confidence intervals of high return period quantiles.

[1] This paper explores the use of a mixture model for determining the marginal distribution of hydrological variables, consisting of a truncated central distribution that is representative of the central or main-mass regime, which for the cases studied is a lognormal distribution, and of two generalized Pareto distributions for the maximum and minimum regimes, representing the upper and lower tails, respectively. The thresholds defining the limits between these regimes and the central regime are parameters of the model and are calculated together with the remaining parameters by maximum likelihood. After testing the model with a simulation study we concluded that the upper threshold of the model can be used when applying the peak over threshold method. This will yield an automatic and objective identification of the threshold presenting an alternative to existing methods. The model was also applied to four hydrological data series: two mean daily flow series, the Thames at Kingston (United Kingdom), and the Guadalfeo River at Orgiva (Spain); and two daily precipitation series, Fort Collins (CO, USA), and Orgiva (Spain). It was observed that the model improved the fit of the data series with respect to the fit obtained with the lognormal (LN) and, in particular, provided a good fit for the upper tail. Moreover, we concluded that the proposed model is able to accommodate the entire range of values of some significant hydrological variables.Citation: Solari, S., and M. A. Losada (2012), A unified statistical model for hydrological variables including the selection of threshold for the peak over threshold method, Water Resour. Res., 48, W10541,

[1] The most popular methods of simulating time series for wave heights and other meteorological and oceanic variables are based on the use of autoregressive models and the transformation of variables to make them normal and stationary. Generally, when these models are used, attention is centered on their capacity to represent the autocorrelation of the series. In this article, a simulation model is proposed that is based on the following: (i) a non-stationary parametric mixture model for the marginal distribution of the variable, that combines a log-normal distribution for main-mass regime and generalized Pareto distributions for upper and lower tail regimes, and (ii) the use of copulas to model the time dependency of the variable. The model has been evaluated by comparing the original series and the simulated series in terms of the autocorrelation function, the mean, the annual maxima and peaks-over-threshold regimes, and the persistences regime. It has also been compared to an ARMA model and found to yield more satisfactory results.Citation: Solari, S., and M. A. Losada (2011), Non-stationary wave height climate modeling and simulation, J. Geophys. Res., 116, C09032,

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Extreme Value Analysis is usually based on the assumption that the data is independent and homogeneous. Historically the hypothesis of independence has received more attention than the hypothesis of homogeneity. The two most common ways of ensuring independence is to use annual maxima or peaks over threshold approaches. In wave and wind extreme analysis, the usual approaches to achieve homogeneous series have been to work to differentiate according to type of process generating the extreme value (e.g. differentiate between hurricanes and cyclones) and conduct directional analyzes. In this work an alternative approach is proposed, based on the use of cluster analysis methodologies to identify weather circulation patterns that results in extreme wave conditions. The proposed methodology is successfully applied to a case study in the Uruguayan South Atlantic coast. From the obtained results it seems that the proposed methodology is able to differentiate the data in homogenous subsets, not only in terms of the target variable (significant wave height) but also in terms of relevant covariables, like wave direction or sea level, and that the extreme value distribution of the whole data, obtained from the distributions fitted to each subset, is fairly insensitive to the number of weather patterns used in the analysis.

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