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arXiv · 1901.09867

It could be worse, it could be raining: reliable automatic meteorological forecasting

Matteo Cristani·Francesco Domenichini·Claudio Tomazzoli·Luca Viganò·Margherita Zorzi
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Abstract

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.

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Matteo Cristani, Francesco Domenichini, Claudio Tomazzoli, Luca Viganò, Margherita Zorzi. 2019-01-28. It could be worse, it could be raining: reliable automatic meteorological forecasting. https://arxiv.org/abs/1901.09867

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