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Meteorological forecasting provides reliable predictions about the weather within a given interval of time. The automation of the forecasting process would be helpful in a number of contexts. For instance, when forecasting about underpopulated or small geographic areas is out of the human forecasters' tasks but is central, e.g., for tourism. In this paper, we start to tame this challenging tasks: we develop a defeasible reasoner for meteorological forecasting, which we evaluate on of a realworld example with applications to tourism and holiday planning.