The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

data can ever be generated. In fact, conventional resampling algorithms, such as the standard
form of bootstrap, fail for the maximum value of a sample or when the underlying distribution is
heavy-tailed. Such limitations of the resampling approach are demonstrated in this talk by
example.
<strong>The</strong> alternative approach to stochastic weather generation is parametric. This approach
involves a formal stochastic model for time series of daily weather variables (e.g., a Markov
chain model for precipitation occurrence). Because the parametric distributions (e.g., gamma)
typically used cannot produce a heavy tail, precipitation extremes can be underestimated by a
substantial amount.
Furrer and Katz (<strong>2007</strong>) proposed a generalized linear modeling (GLM) technique for
parametric stochastic weather generation. <strong>The</strong> GLM technique and its extensions are used in
this talk to explore how the simulation of high precipitation amounts can be improved. In
particular, the substitution of a stretched exponential (or Weibull) distribution, instead of the
gamma, for the upper tail of daily precipitation amounts can be readily incorporated into the
GLM approach. Extreme value theory has established that such a distribution can produce an
apparent heavy tail. Requiring an extension of the GLM framework is the technique of fitting a
mixture of two Weibull distributions to daily precipitation amount. <strong>The</strong> results suggest that the
simulation of extreme precipitation events can be substantially improved, with a stochastic
model that remains fairly parsimonious.
Reference
Furrer, E.M., and R.W. Katz, <strong>2007</strong>: Generalized linear modeling approach to stochastic
weather generators. Climate Research (in press).
Atmospheric dust dispersion from disking operation: near-field simulation
Speaker: Junming wang
Junming wang
New Mexico State University, Plant & Environmental Sciences, USA
jwang@nmsu.edu
April L. Hiscox
University of Connecticut, USA
David R. Miller
University of Connecticut, USA
Ted Sammis
New Mexico State University, Plant & Environmental Sciences, USA
Wenli Yang, Britt A. Holmén
University of California at Davis, USA
This paper presents a dynamic random-walk model which simulates the field-scale
PM10 (particle m) dust dispersion from an agriculture operationμmatter of diameter ≤10
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