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

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

Energy, 2022, vol. 251, issue C

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.

Keywords: Wind forecasting; Probabilistic forecasting; Dynamic forecast calibration; Ensemble model output statistics; Wind forecast based on real-time conditions; Numerical weather prediction models (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:251:y:2022:i:c:s0360544222007976

DOI: 10.1016/j.energy.2022.123894

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