为提高对南海波浪场的认识,采用基于多卫星融合的2009年9月-2011年11月的AVISO（Archiving, Validation and Interpretation of Satellite Oceanographic data）有效浪高格点数据对南海浪高的月变化特征进行分析,并结合南海的波浪特征...为提高对南海波浪场的认识,采用基于多卫星融合的2009年9月-2011年11月的AVISO（Archiving, Validation and Interpretation of Satellite Oceanographic data）有效浪高格点数据对南海浪高的月变化特征进行分析,并结合南海的波浪特征和地形特点,将南海划分为6个海区,讨论南海浪高的空间分布规律。研究发现南海浪高具有以下2个特征：（1）南海浪高表现为由东向西、由北往南递减：北部深水区〉北部陆架区〉南海中部≈北部湾〉南部陆架区〉泰国湾。（2）浪高的月变化与季风的变化密不可分：10月-次年3月（冬季风影响期间）〉4月和9月（季风转换期）〉5月-8月（夏季风影响期间）,1月最大,5月最小。该研究成果对开展南海海浪的中长期预报、保障南海资源开发和军事安全等有一定的借鉴意义和参考价值。展开更多
This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gr...This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gridded time series data sets of monthly mean temperatures for the period 1880- 2007/2008 are analysed to show that data sets and corresponding power spectra exhibit distributions close to the model predicted inverse power law distribution. The model predicted and observed universal spectrum for interannual variability rules out linear secular trends in global monthly mean temperatures. Global warming results in intensification of fluctuations of all scales and manifested immediately in high frequency fluctuations.展开更多
文摘为提高对南海波浪场的认识,采用基于多卫星融合的2009年9月-2011年11月的AVISO（Archiving, Validation and Interpretation of Satellite Oceanographic data）有效浪高格点数据对南海浪高的月变化特征进行分析,并结合南海的波浪特征和地形特点,将南海划分为6个海区,讨论南海浪高的空间分布规律。研究发现南海浪高具有以下2个特征：（1）南海浪高表现为由东向西、由北往南递减：北部深水区〉北部陆架区〉南海中部≈北部湾〉南部陆架区〉泰国湾。（2）浪高的月变化与季风的变化密不可分：10月-次年3月（冬季风影响期间）〉4月和9月（季风转换期）〉5月-8月（夏季风影响期间）,1月最大,5月最小。该研究成果对开展南海海浪的中长期预报、保障南海资源开发和军事安全等有一定的借鉴意义和参考价值。
文摘This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gridded time series data sets of monthly mean temperatures for the period 1880- 2007/2008 are analysed to show that data sets and corresponding power spectra exhibit distributions close to the model predicted inverse power law distribution. The model predicted and observed universal spectrum for interannual variability rules out linear secular trends in global monthly mean temperatures. Global warming results in intensification of fluctuations of all scales and manifested immediately in high frequency fluctuations.
