The downburst wind is an extreme wind event with rapidly increasing and afterwards descending wind velocities in a short period. The prediction of downburst winds, especially for the peak wind velocity and its occurrence time, is a critical issue in engineering communities. In this paper, typical commonly used time-series models are firstly employed to predict the downburst winds based on measured data, and the measured results are found highly underestimated with a prominent time lag. According to the faced problem, a double-step modification enhanced approach is developed to improve the prediction accuracy of time-series models. Central to this approach is the dual modifications, in which the first modification remedies the underestimation by correcting the error linearly correlated to the measured wind velocity, while the second modification compensates the residual error negatively proportional to the wind velocity after the first modification. The developed approach is then compared with other time-varying and nonlinear forecasting models, and the dual modifications are found to be able to highly improve the prediction accuracies of these models. Finally, the efficacy of the developed approach is further verified via case studies concerning the one-step and multi-step ahead predictions. The satisfied predicted results prove the effectiveness of the developed approach in the short-term prediction of downburst winds. Thus, the double-step modification enhanced approach can be used for early-warning purposes.

下行暴风是一种极端风事件，在短时间内风速迅速增加，然后下降。下突风的预测，特别是峰值风速及其发生时间的预测，在工程界是一个至关重要的问题。在本文中，首先使用典型的常用时间序列模型基于实测数据来预测暴风，并且发现实测结果被低估了，并且存在明显的时间滞后。针对所面临的问题，提出了一种改进的双步修正方法，以提高时间序列模型的预测精度。这种方法的核心是双重修改，其中第一个修改通过校正与测量风速线性相关的误差来纠正低估，而第二修改则补偿了与第一修改后的风速成负比例的残余误差。然后将开发的方法与其他时变和非线性预测模型进行比较，发现双重修改能够高度提高这些模型的预测精度。最后，通过有关单步和多步提前预测的案例研究，进一步验证了所开发方法的有效性。满意的预测结果证明了该方法在短期预测暴风中的有效性。因此，双步修改增强方法可用于预警目的。然后将开发的方法与其他时变和非线性预测模型进行比较，发现双重修改能够高度提高这些模型的预测精度。最后，通过有关单步和多步提前预测的案例研究，进一步验证了所开发方法的有效性。满意的预测结果证明了该方法在短期预测暴风中的有效性。因此，双步修改增强方法可用于预警目的。然后将开发的方法与其他时变和非线性预测模型进行比较，发现双重修改能够高度提高这些模型的预测精度。最后，通过有关单步和多步提前预测的案例研究，进一步验证了所开发方法的有效性。满意的预测结果证明了该方法在短期预测暴风中的有效性。因此，双步修改增强方法可用于预警目的。通过有关一步和一步预测的案例研究，进一步验证了所开发方法的有效性。满意的预测结果证明了该方法在短期预测暴风中的有效性。因此，双步修改增强方法可用于预警目的。通过关于一步法和多步预测的案例研究，进一步验证了开发方法的有效性。满意的预测结果证明了该方法在短期预测暴风中的有效性。因此，双步修改增强方法可用于预警目的。