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

Parallel Sessions:Simulation tools for weather generation and risk assessment
High resolution simulation of weather sequences for binary events with Generative Classifiers
Speaker: Rafael Ancell
Rafael Ancell
Spanish National Meteorological Institute (INM)
rct@inm.es
Jose M. Gutierrez
University of Cantabria
Different generative classifiers have been used to develop a probabilistic model for the
simulation of high resolution spatially distributed weather sequences for binary events. <strong>The</strong>
main difference between discriminative and generative classifiers is that discriminative
classifiers directly model the posterior probability, and the generative ones first model the joint
probability distribution to get the posterior, taking into account the relationships among the
different attributes given some kind of available evidence. Generative Classifiers provide us a
simple method for generating stochastic weather in a spatially consistent manner. Instead of
simulating values independently for each variable (as in standard weather generator methods)
we simulate spatial realizations taking into account the constraints imposed by the
dependencies among the atributes. This work illustrates the application of this methodology in
the case of discrete variables (precipitation), though a similar scheme is also applicable to the
continuous case.
<strong>The</strong> study has been carried out over a small area with 42 locations in Northern Spain, and
both extreme and normal precipitaion events have been considered. <strong>The</strong> validation of the
results is based on the RSA (ROC skill area). Three generative classifier models are evaluated:
naive Bayes, augmented naive Bayes, and general probabilistic models (Bayesian networks),
in different nowcasting, forecasting and climate problems. Finally we show that, in the
forecasting situation, the spatial dependencies do not provide any additional information to the
simple naive Bayes when an estimation of the atmospheric state (numerical weather prediction)
is given. However, these dependencies become very important in the nowcasting and climate
paradigms, i.e. when some kind of evidence, other than the atmospheric state, is considered.
Using multi-site stochastic downscaling to understand regional precipitation variability and
trends across southern Australia
Speaker: Stephen P. Charles
Stephen P. Charles
CSIRO Land and Water
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