In recent years, the potential of remote sensing-based minute-scale forecasts to improve the integration of wind power into our energy system has been shown. In lidar-based forecasts, the wind speed is extrapolated from the measuring to the forecast height, i.e. the wind turbines' hub height, by assuming a stability-corrected logarithmic wind profile. The objective of this paper is the significant reduction of large forecasting errors associated with the height extrapolation. Hence, we introduce two new approaches and characterise their skill under different atmospheric conditions. The first one is based on an empirical set of parameters derived from lidar data and operational wind turbine data. The second approach derives the wind speed tendency of two consecutive forecasts at the measuring height and applies this to operational wind speed data at hub height. We identified the uncertainty in stability estimates and measurement height as the main cause for large extrapolation errors of the existing lidar-based forecast. Monte Carlo simulations revealed the new approaches' low sensitivity to uncertainty in lidar data processing, propagation and height extrapolation. Forecasting errors of a 5‑minute-ahead wind speed forecast of free-flow turbines at an offshore wind farm were significantly reduced for the two newly developed methods as compared to the existing forecast during stable atmospheric conditions. Persistence could be outperformed during unstable and neutral atmospheric conditions and for situations with higher turbulence intensity. Overall, we found lidar-based forecasts to be less sensitive to atmospheric conditions than persistence. We discuss the importance of accurate vertical wind speed profile estimation, the advantages and shortcomings of the two newly introduced methods and their skill compared to persistence. In conclusion, the additional use of wind turbine operational data can significantly improve minute-scale lidar-based forecasts. We further conclude that the characterisation of forecast skill dependent on atmospheric conditions can be valuable for decision-making processes.

中文翻译：

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不同大气条件下基于激光雷达的分钟尺度海上风速预测分析

近年来，基于遥感的微小尺度预测在改善风力发电与我们能源系统的整合方面的潜力已经显现。在基于激光雷达的预报中，通过假设稳定性校正的对数风廓线，将风速从测量值外推到预报高度，即风力涡轮机的轮毂高度。本文的目的是显着减少与高度外推相关的大预测误差。因此，我们介绍了两种新方法，并描述了它们在不同大气条件下的技能。第一个是基于从激光雷达数据和运行风力涡轮机数据得出的一组经验参数。第二种方法推导出测量高度处两次连续预报的风速趋势，并将其应用于轮毂高度处的运行风速数据。我们将稳定性估计和测量高度的不确定性确定为现有基于激光雷达的预测的大外推误差的主要原因。蒙特卡罗模拟揭示了新方法对激光雷达数据处理、传播和高度外推中的不确定性的低敏感性。与稳定大气条件下的现有预测相比，这两种新开发的方法显着降低了海上风电场自由流涡轮机提前 5 分钟预测风速的预测误差。在不稳定和中性大气条件下以及湍流强度较高的情况下，持久性可能会表现出色。总体而言，我们发现基于激光雷达的预测对大气条件的敏感性不如持久性。我们讨论了准确的垂直风速廓线估计的重要性、两种新引入的方法的优缺点以及它们与持久性相比的技巧。总之，额外使用风力涡轮机运行数据可以显着改善基于激光雷达的分钟级预测。我们进一步得出结论，依赖于大气条件的预测技能的特征对于决策过程可能是有价值的。我们讨论了准确的垂直风速廓线估计的重要性、两种新引入的方法的优缺点以及它们与持久性相比的技巧。总之，额外使用风力涡轮机运行数据可以显着改善基于激光雷达的分钟级预测。我们进一步得出结论，依赖于大气条件的预测技能的特征对于决策过程可能是有价值的。我们讨论了准确的垂直风速廓线估计的重要性、两种新引入的方法的优缺点以及它们与持久性相比的技巧。总之，额外使用风力涡轮机运行数据可以显着改善基于激光雷达的分钟级预测。我们进一步得出结论，依赖于大气条件的预测技能的特征对于决策过程可能是有价值的。