密歇根湖风浪预报不确定性分析初探
Preliminary Investigations on Uncertainty Analysis of Wind-Wave Predictions in Lake Michigan
摘要：
随着波浪和流体动力学数值模型的不断改进，强迫函数及其输入的准确性如何影响结果的问题出现在我们的脑海中。本文应用第三代波浪数值模式SWAN对密歇根湖的波浪进行了预报。风数据进行了分析，以确定密歇根湖的风变化频率。波浪预测的不确定性，由于风的局部影响进行了比较，在一个时期内，风有一个相当恒定的速度和方向在北部和南部盆地。研究表明，尽管在密歇根湖地区的模式校准，模式的不足之处，在小尺度上忽略风的影响。波浪预报还强调，气象力量中的小尺度湍流会使预报误差增加35%。波频和相干分析表明，两种模型都能以相同的精度揭示波浪变化的时间尺度。气象站数量不足可能导致忽略当地风的影响和当前预测的差异。波浪数值模型的不确定性，由于输入的不确定性和模型的原则，应考虑设计的风险因素。
英文摘要：
With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data were analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects were compared during a period where wind had a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase error in predictions up to 35%. Wave frequency and coherence analysis showed that both models are able to reveal the time scale of the wave variation with same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.
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