论文标题
通过新的动态校准，提高了短期风速整体预测的技能，结合预测和观察的技能
Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration
论文作者
论文摘要
一旦分析进行分析，用于风能行业的所有数值天气预测模型都需要从00、06、12和18 UTC开始产生其预测。两次连续模型之间的六小时延迟时间呼吁通过提供新的准确预测至少每小时频率来填补空白的策略。这样做是为了适应贸易商和系统监管机构频繁，准确和新的信息的要求，以不断调整其工作策略。在这里，我们提出了一种策略，准真实时间观察到的风速和天气模型预测是通过新型集成模型输出统计（EMOS）策略组合在一起的。我们的策略的成功是通过与2018年和2019年在意大利观察到的风速进行比较来衡量的。
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.
