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All numerical weather prediction models used for the wind industry need toproduce their forecasts starting from the main synoptic hours 00, 06, 12, and18 UTC, once the analysis becomes available. The six-hour latency time betweentwo consecutive model runs calls for strategies to fill the gap by providingnew accurate predictions having, at least, hourly frequency. This is done toaccommodate the request of frequent, accurate and fresh information fromtraders and system regulators to continuously adapt their work strategies.Here, we propose a strategy where quasi-real time observed wind speed andweather model predictions are combined by means of a novel Ensemble ModelOutput Statistics (EMOS) strategy. The success of our strategy is measured bycomparisons against observed wind speed from SYNOP stations over Italy in theyears 2018 and 2019.

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