基于WAVEWATCH III v3.14海浪数值模式,建立混合双向嵌套网格,分别采用CCMP和GDAS海面10m风场资料,模拟2009年10月西北太平洋第20号超强台风Lupit的海浪场有效波高,对比分析不同风场资料对台风海浪模拟的影响,并根据浮标观测数据分析模...基于WAVEWATCH III v3.14海浪数值模式,建立混合双向嵌套网格,分别采用CCMP和GDAS海面10m风场资料,模拟2009年10月西北太平洋第20号超强台风Lupit的海浪场有效波高,对比分析不同风场资料对台风海浪模拟的影响,并根据浮标观测数据分析模拟的效果。结果表明,WAVEWATCH III模式能够较好的模拟台风海浪场分布形势,模式对风场资料具有较强的敏感性,基于融合了多种观测资料的CCMP高分辨风场资料所模拟的结果更为准确。展开更多
The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South Ch...The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.展开更多
文摘基于WAVEWATCH III v3.14海浪数值模式,建立混合双向嵌套网格,分别采用CCMP和GDAS海面10m风场资料,模拟2009年10月西北太平洋第20号超强台风Lupit的海浪场有效波高,对比分析不同风场资料对台风海浪模拟的影响,并根据浮标观测数据分析模拟的效果。结果表明,WAVEWATCH III模式能够较好的模拟台风海浪场分布形势,模式对风场资料具有较强的敏感性,基于融合了多种观测资料的CCMP高分辨风场资料所模拟的结果更为准确。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41230421 and 41605075)the National Basic Research Program of China(Grant No.2013CB430101)
文摘The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.
