Coastal wave forecasting by dynamical model coupled with a statistical method
-
摘要: 针对数值模式和统计模型预报近岸海浪存在的局限性，构建了数值模式和统计模型相耦合的近岸海浪预报框架，在模式计算格点和近岸预报目标点之间定义一个海浪能量密度谱传递系数，通过经验正交函数分解和卡尔曼滤波方法建立传递系数的统计预报模型并与数值模式进行耦合。经过对近岸波浪观测站1 a的预报试验表明:该方法能够提高近岸海浪有效波高预报精度，有效波高的均方根误差降低了约0.16 m，平均相对误差降低约9%。进一步试验和分析发现，该方法的预报有效时间小于24 h，将海浪能量密度谱经过分解后得到的基本模态反映了近岸波侯的主要特征，海浪能量密度谱传递系数的变化体现了波侯的季节变化特点。Abstract: The coupled coastal wave prediction scheme ,which is a combination of a multi-scale numerical model and a statistical method,is proposed in order to avoid the limitations of one single scheme. By ocean wave model,the wave energy density spectrum of the computational grid in the coastal model is forecasted. We have defined a transfer coefficient matrix for thewave energy density spectrum between the computational grid and the coastal forecasting point. A statistical model for the prediction of this transfer coefficient is established using empirical orthogonal function (EOF) and Kalman filtering method. This statistical model is then coupled with the numerical model. The wave energy density spectrum of computational grid is optimized using the observed coastal buoy data. The coastal wave forecasting are validated by the observations of NAHA station for one year,indicating that this coupled method significantly improved the prediction power compared with the numerical model on its own. The rootmeansquare error of the significant wave height reduces about 0.16mand the average relative error is reduced by about 9%. It is also found that the forecasting accuracy of this method is limited within 24 hours; the principal components decomposed from the wave energy density spectrum reflect the main characteristics of local wave climate; and the change transfer coefficient of the spectrum reflects the seasonal variation of the wave climate.
-
Key words:
- coastal wave /
- numerical model /
- statistical method /
- coupled scheme /
- Kalman filtering method
-
Tolman H L,Banner M L,Kaihatu J M. The NOPP operational wave model improvement project[J]. Ocean Modelling,2013,70: 2-10. Dietrich J C,Zijlema M,Allier P E,et al. Limiters for Spectral Propagation Velocities in SWAN[J]. Ocean Modelling,2012,70: 85. Özger M. Significant wave height forecasting using wavelet fuzzy logic approach[J]. Ocean Engineering,2010,37(16): 1443-1451. Tseng C M,Jan C D,Wang J S,et al. Application of artificial neural networks in typhoon surge forecasting[J]. Ocean Engineering,2007,34(11/12): 1757-1768. Zamani A,Solomatine D,Azimian A,et al. Learning from data for wind-wave forecasting[J]. Ocean Engineering,2008,35(10): 953-962. Reikard G. Forecasting ocean wave energy: Tests of time-series models[J]. Ocean Engineering,2009,36(5): 348-356. Roulston M S,Ellepola J,Hardenberg J,et al. Forecasting wave height probabilities with numerical weather prediction models[J]. Ocean Engineering,2005,32(14/15): 1841-1863. An GÖner H A,YÖksel Y,Özkan Cevik E. Estimation of wave parameters based on nearshore wind-wave correlations[J]. Ocean Engineering,2013,63: 52-62. Breivik R,Gusdal Y,Furevik B R,et al. Nearshore wave forecasting and hindcasting by dynamical and statistical downscaling[J]. Journal of Marine Systems,2009,78: S235-S243. Camus P,Mendez F J,Medina R,et al. High resolution downscaled ocean waves (DOW) reanalysis in coastal areas[J]. Coastal Engineering,2013,72: 56-68 Reikard G,Rogers W E. Forecasting ocean waves: Comparing a physics-based model with statistical models[J]. Coastal Engineering,2011,58(5): 409-416. -
计量
- 文章访问数: 1473
- HTML全文浏览量: 17
- PDF下载量: 692
- 被引次数: 0
下载:
