Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration
This work addresses the need for more frequent and accurate wind speed forecasts for traders and system regulators in the wind industry, representing an incremental improvement in dynamic calibration methods.
The paper tackled the problem of six-hour latency in wind speed forecasts by combining quasi-real-time observations with model predictions using a new Ensemble Model Output Statistics strategy, achieving improved forecast skill as measured against SYNOP station data from 2018-2019.
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 six-hour 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.