基于Wallops谱的短期波浪波高周期联合分布

doi:10.16183/j.cnki.jsjtu.2024.477

摘要/Abstract

摘要：

为了准确预报短期波浪波高周期的联合分布,基于条件概率方法提出一种新模型. 在新模型中波高分布采用双参数威布尔分布描述,条件周期分布采用对数正态分布描述. 考虑波浪谱形状的影响,采用谱宽范围较大的Wallops谱,并给出新模型中参数表达式.以Wallops谱和实测波浪谱为靶谱,通过仿真得到波高周期的联合分布.以仿真数据为基准,将新模型与5种常用联合分布模型进行对比;同时分析各模型的波高和周期分布以及各模型预报误差产生原因.结果表明,在Wallops谱和实测波浪谱中,新模型与仿真数据最接近,其他5种模型仅在部分情况下较接近. 此外,新模型采用显式闭合表达式,便于推广到非高斯波浪情况.

关键词: 波浪谱, 波高周期联合分布, 短期波浪, 谱宽参数, 波高分布, 周期分布

Abstract:

To accurately predict the joint distribution of short-term wave height and period, this paper proposes a new model based on the conditional probability approach. In this model, wave height follows a two-parameter Weibull distribution, while the conditional period distribution is characterized by a log-normal distribution. To incorporate the influence of wave spectral shape, the broad-width Wallops spectrum is adopted, and the corresponding model parameters are derived. Simulations are conducted using both the Wallops spectrum and measured wave spectra as target spectra to obtain the wave height-period joint distribution. Taking the simulated data as a benchmark, the proposed model is compared with five commonly used joint distribution models. Additionally, the wave height and period distributions are analyzed, with the sources of prediction errors discussed. The results indicate that the model proposed closely matches the simulated data for both Wallops and measured spectra, whereas the other five models only perform well in specific cases. Moreover, this model takes an explicit closed-form expression, making it applicable to non-Gaussian wave conditions.

Key words: wave spectrum, joint distribution of wave height and period, short-term waves, spectral width parameter, distribution of wave height, distribution of wave period

中图分类号:

P751

马永亮, 陈炜, 韩超帅, 张应铭, 陈小康. 基于Wallops谱的短期波浪波高周期联合分布[J]. 上海交通大学学报, 2026, 60(3): 377-386.

MA Yongliang, CHEN Wei, HAN Chaoshuai, ZHANG Yingming, CHEN Xiaokang. Joint Distribution of Short-Term Waves Height and Period Based on Wallops Spectrum[J]. Journal of Shanghai Jiao Tong University, 2026, 60(3): 377-386.

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模型	m=4.4, ν= 0.5265	m=5, ν= 0.4247	m=7, ν= 0.2805	m=9, ν= 0.2218
CNEXO模型	0.4189	0.3450	0.1920	0.1563
孙模型	0.1501	0.1343	0.1044	0.0907
修正的孙模型	0.1794	0.1560	0.1143	0.0969
Zhang-Soares模型	0.1572	0.1486	0.1326	0.1282
Tayfun模型	0.1971	0.1748	0.1343	0.1199
新模型	0.0601	0.0551	0.0567	0.0555

模型	46059站 01:40 ν=0.5265	46059站 03:40 ν=0.4380	46028站 15:50 ν=0.3544
CNEXO模型	0.2386	0.2401	0.1985
孙模型	0.1405	0.1398	0.1396
修正的孙模型	0.1563	0.1581	0.1549
Zhang-Soares模型	0.1542	0.1547	0.1486
Tayfun模型	0.1900	0.1767	0.1555
新模型	0.0454	0.0606	0.0855