摘要
以SeaWinds散射计为例,利用其L2A卫星测量数据和相应的海面浮标测量数据,以温度作为独立变量,借助人工神经网络首次尝试建立了包含温度因子的海水地球物理模型函数。建模实验结果表明,在3—15m.s-1风速范围内,对于两种极化方式,后向散射系数先随温度的升高而增大,当风速大于某一数值后,又随温度的升高而减小,但两种极化方式的转折风速存在差异。该研究结果为海面微波散射机理认识的进一步深入和模型函数精度的进一步提高展示了潜在的可能途径。
Based on L2A data of SeaWinds and corresponding ocean surface buoy data, an ocean water geophysical model function including the factor of temperature is established by using neural network method. Modeling experiment results indicate that within the range of 3-15m · s^-1 wind speed, for the two polari- zations, the backscattering coefficient increases with temperature rising at lower wind speed and then decreases when the wind speed is larger than a certain value. But the wind speed inflexions of the two polarizations are different. These results demonstrate the potential possibility for further understanding the ocean surface microwave scattering mechanism and establishing more precise model function.
出处
《热带海洋学报》
CAS
CSCD
北大核心
2007年第6期14-20,共7页
Journal of Tropical Oceanography
基金
985工程项目(105203200400006)
关键词
风场反演
海面温度
神经网络
地球物理模型函数(GMF)
wind field retrieval
ocean surface temperature
neural network
geophysical model function （GMF）
