RadRAR: A relational association rule mining approach for nowcasting based on predicting radar products’ values

https://doi.org/10.1016/j.procs.2020.08.032Get rights and content
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Abstract

The paper approaches the topic of nowcasting, one of the hottest topics in meteorology which deals with the problem of short-term forecasting of severe weather phenomena. Various types of meteorological data, including radar measurements, satellite data and weather stations’ observations are currently used for forecasting severe weather events. Radar data is one of the important sources used by meteorologists for nowcasting and for providing alerts for severe weather events. We are proposing a new one-class classifier, named RadRAR (Radar products’ values prediction using Relational Association Rules) for convective storms nowcasting based on radar data. More specifically, RadRAR is trained on radar data collected from normal weather conditions and learns to predict whether the radar echo values will be higher than 35dBZ, i.e. likely to indicate the occurrence of a storm. RadRAR is intended to be a proof a concept that relational association rule mining applied on radar data is helpful in discriminating between severe and normal weather conditions. Real radar data provided by the Romanian National Meteorological Administration is used for evaluating the effectiveness of RadRAR. A Critical Success Index of 61% was obtained, outperforming similar approaches from the literature and highlighting a good performance of RadRAR.

Keywords

Weather nowcasting
Data mining
Classification
Relational association rules 2000 MSC: 68T05
68P15

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