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

## TL;DR

This paper introduces MeteoLOG, a logic-based framework combining fuzzy, temporal, and probabilistic logics to automate meteorological forecasting, especially useful for handling large data sets and underpopulated areas.

## Contribution

It presents MeteoLOG, a novel logic framework for automated weather prediction, and the Tournament algorithm for translating rules into defeasible theories.

## Key findings

- MeteoLOG effectively models forecasting reasoning processes.
- The Tournament algorithm enables automatic rule-based reasoning.
- Application example demonstrates real-world forecasting scenario.

## 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.