Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data

TL;DR

This paper introduces GALiWeather, an innovative system that automatically generates accurate, linguistically rich short-term weather forecasts for Galicia using real meteorological data and natural language generation techniques.

Contribution

The paper presents a novel combination of perception-based data description and natural language generation for automated weather forecast creation.

Findings

Forecasts validated by expert meteorologist with high accuracy

System successfully encodes data into natural language descriptions

Ready for deployment as a public weather forecast service

Abstract

We present in this paper an application which automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and strategies for linguistic description of data together with a natural language generation (NLG) system. The application, named GALiWeather, extracts relevant information from weather forecast input data and encodes it into intermediate descriptions using linguistic variables and temporal references. These descriptions are later translated into natural language texts by the natural language generation system. The obtained forecast results have been thoroughly validated by an expert meteorologist from MeteoGalicia using a quality assessment methodology which covers two key dimensions of a text: the accuracy of its content and the correctness of its form. Following this validation GALiWeather will be released as a real service offering custom forecasts for a wide public.