Real-time hazard approximation of long-lasting convective storms using emergency data

Pekka J. Rossi*, Vesa Hasu, Kalle Halmevaara, Antti Mäkelä, Jarmo Koistinen, Heikki Pohjola

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    7 Citations (Scopus)


    Convective storms cause several types of damage, including economic and ecological losses, every year. This paper focuses on an automatic hazard-level determination of convective storms based on a largely unused information source: real-time emergency report data. In addition to the location of the report, the emergency response centers classify cases into different emergency types and deliver a free-form verbal description of the incident for online use. This study uses archived weather-related emergency reports to determine hazard levels for convective storms detected by the weather radar. To develop an algorithm for estimating the hazard level of convective storms, a weather radar-databased convective storm-tracking algorithm was applied with a method that links reported emergency events to individually tracked convective storms. Based on the relationship between each convective storm track and an emergency report, the algorithm determines the hazard level of the storms automatically. Moreover, the developed algorithm takes into account the population density at the location of the report because, in densely populated areas, the flow of emergency reports is more intense. The proposed algorithm with case studies shows the potential use of realtime emergency call data in operational severe weather nowcasting and warning tools. This study demonstrates that supplementing storms with emergency information is advantageous, especially with long-lasting storms such as supercell storms or mesoscale convective systems.

    Original languageEnglish
    Pages (from-to)538-555
    Number of pages18
    JournalJournal of Atmospheric and Oceanic Technology
    Issue number3
    Publication statusPublished - Mar 2013
    MoE publication typeA1 Journal article-refereed


    • Algorithms
    • Emergency preparedness
    • Nowcasting
    • Radars/Radar observations
    • Severe storms

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