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Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

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  • Peter L. Watson

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Marika Koukoula

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Emmanouil Anagnostou

    (Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future.

Suggested Citation

  • Peter L. Watson & Marika Koukoula & Emmanouil Anagnostou, 2021. "Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System," Forecasting, MDPI, vol. 3(3), pages 1-20, August.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:34-560:d:608764
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    References listed on IDEAS

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    Cited by:

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    3. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

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