Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration. (15th July 2022)

• Record Type: Journal Article
• Authors: Casciaro, Gabriele; Ferrari, Francesco; Lagomarsino-Oneto, Daniele; Lira-Loarca, Andrea; Mazzino, Andrea
• Is Part Of: Energy. Volume 251(2022)
• Publication Date: 2022-07-15
• Subjects: Wind forecasting -- Probabilistic forecasting -- Dynamic forecast calibration -- Ensemble model output statistics -- Wind forecast based on real-time conditions -- Numerical weather prediction models; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123894
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: First attempt to couple in an efficient and economic way real-time data and ensemble predictions. First assessment of the added value of real-time observations in a wind calibration. Real-time data can be easily and economically ingested in an EMOS-based calibration. Ingestion of real-time data produces noticeable benefits vs. static calibrations. Real-time data provide added value to the whole wind predictive probability density.