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Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration

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  • Casciaro, Gabriele
  • Ferrari, Francesco
  • Lagomarsino-Oneto, Daniele
  • Lira-Loarca, Andrea
  • Mazzino, Andrea

Abstract

All numerical weather prediction models used for the wind industry need to produce their forecasts starting from the main synoptic hours 00, 06, 12, and 18 UTC, once the analysis becomes available. The 6-h latency time between two consecutive model runs calls for strategies to fill the gap by providing new accurate predictions having, at least, hourly frequency. This is done to accommodate the request of frequent, accurate and fresh information from traders and system regulators to continuously adapt their work strategies. Here, we propose a strategy where quasi-real time observed wind speed and weather model predictions are combined by means of a novel Ensemble Model Output Statistics (EMOS) strategy. The success of our strategy is measured by comparisons against observed wind speed from SYNOP stations over Italy in the years 2018 and 2019.

Suggested Citation

  • Casciaro, Gabriele & Ferrari, Francesco & Lagomarsino-Oneto, Daniele & Lira-Loarca, Andrea & Mazzino, Andrea, 2022. "Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007976
    DOI: 10.1016/j.energy.2022.123894
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    References listed on IDEAS

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    Cited by:

    1. Mattia Cavaiola & Federico Cassola & Davide Sacchetti & Francesco Ferrari & Andrea Mazzino, 2024. "Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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