Meteorological Insights
Scalable Weather Pattern Mining in Tallinn and Tartu
Mahtab Shahin (Tallinn University of Technology)
Tara Ghasempouri (Tallinn University of Technology)
Saeed Rahimpour (Tallinn University of Technology)
Juan Aznar Poveda (University of Innsbruck)
Nasim Janatian (TU Delft - Civil Engineering & Geosciences)
Thomas Fahringer (University of Innsbruck)
S. A. Shah (Birmingham City University)
Dirk Draheim (Tallinn University of Technology)
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Abstract
Accurate weather and climate prediction are essential for early warning systems that improve response strategies to climate-related events. This study explores the use of association rule mining (ARM) techniques to analyze large-scale meteorological datasets. We focus on the weather patterns of Tallinn and Tartu, investigating variables such as wind speed, temperature, precipitation, and humidity, and their influence on weather intensity. A distributed ARM approach is employed using the Apollo framework, which utilizes serverless functions to enhance scalability and performance. Results show Apollo outperforms traditional systems like Apache Spark by approximately 15% in terms of processing speed, while extracting a greater number of meaningful rules. Time-series analysis was also applied to investigate temporal weather trends. Our findings highlight the potential of this approach for enhancing weather prediction systems and offer a foundation for future research in this area.