extRemes (version 2.0-9)

Ozone4H: Ground-Level Ozone Order Statistics.

Description

Ground-level ozone order statistics from 1997 at 513 monitoring stations in the eastern United States.

Usage

data(Ozone4H)

Format

A data frame with 513 observations on the following 5 variables.

station

a numeric vector identifying the station (or line) number.

r1

a numeric vector giving the maximum ozone reading (ppb) for 1997.

r2

a numeric vector giving the second-highest ozone reading (ppb) for 1997.

r3

a numeric vector giving the third-highest ozone reading (ppb) for 1997.

r4

a numeric vector giving the fourth-highest ozone reading (ppb) for 1997.

Details

Ground level ozone readings in parts per billion (ppb) are recorded hourly at ozone monitoring stations throughout the country during the "ozone season" (roughly April to October). These data are taken from a dataset giving daily maximum 8-hour average ozone for 5 ozone seasons (including 1997). The new U.S. Environmental Protection Agency (EPA) National Ambient Air Quality Standard (NAAQS) for ground-level ozone is based on a three-year average of fourth-highest daily 8-hour maximum ozone readings.

For more analysis on the original data regarding the U.S. EPA NAAQS for ground-level ozone, see Fuentes (2003), Gilleland and Nychka (2005) and Gilleland et al. (2006). For an example of using these data with extRemes, see the online tutorial (http://www.isse.ucar.edu/extremevalues/evtk.html). These data are in the form required by the rlarg.fit function of Stuart Coles available in the R package ismev; see Coles (2001) for more on the r-th largest order statistic model and the function rlarg.fit.

References

Coles, S. (2001) An Introduction to Statistical Modeling of Extreme Values. London, U.K.: Springer-Verlag, 208pp.

Fuentes, M. (2003) Statistical assessment of geographic areas of compliance with air quality. Journal of Geophysical Research, 108, (D24).

Gilleland, E. and Nychka, D. (2005) Statistical Models for Monitoring and Regulating Ground-level Ozone. Environmetrics, 16, 535--546.

Gilleland, E., Nychka, D., and Schneider, U. (2006) Spatial models for the distribution of extremes. In Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods, Edited by J.S. Clark & A. Gelfand. Oxford University Press. 170--183, ISBN 0-19-8569671.

Examples

# NOT RUN {
data(Ozone4H)
str(Ozone4H)
plot(Ozone4H)
# }