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

TL;DR

This paper introduces a new dynamic calibration method that combines observed wind speeds with forecast models using a novel EMOS approach to improve short-term wind speed predictions at hourly intervals.

Contribution

The paper presents a novel EMOS-based dynamic calibration technique that enhances the accuracy of short-term wind speed forecasts by integrating real-time observations and model predictions.

Findings

Improved forecast accuracy over traditional methods.

Effective filling of six-hour forecast gaps with hourly predictions.

Validated using Italian SYNOP station data from 2018-2019.

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 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.