It could be worse, it could be raining: reliable automatic meteorological forecasting
This work addresses the need for reliable automated weather prediction to handle large data volumes, support education, and forecast for underpopulated areas, though it appears incremental as it builds on existing logical traditions.
The paper tackles the problem of automating meteorological forecasting by introducing MeteoLOG, a logical framework that models forecaster reasoning, and the Tournament algorithm to convert rules into an automatic reasoner, with an example demonstrating a real-world scenario.
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.