It could be worse, it could be raining: reliable automatic
  meteorological forecasting
  Meteorological forecasting provides reliable prediction about the future
weather within a given interval of time. Meteorological forecasting can be
viewed as a form of hybrid diagnostic reasoning and can be mapped onto an
integrated conceptual framework. The automation of the forecasting process
would be helpful in a number of contexts, in particular: when the amount of
data is too wide to be dealt with manually; to support forecasters education;
when forecasting about underpopulated geographic areas is not interesting for
everyday life (and then is out from human forecasters' tasks) but is central
for tourism sponsorship. We present logic MeteoLOG, a framework that models the
main steps of the reasoner the forecaster adopts to provide a bulletin.
MeteoLOG rests on several traditions, mainly on fuzzy, temporal and
probabilistic logics. On this basis, we also introduce the algorithm
Tournament, that transforms a set of MeteoLOG rules into a defeasible theory,
that can be implemented into an automatic reasoner. We finally propose an
example that models a real world forecasting scenario.
