海况偏差(Sea State Bias,SSB)是卫星高度计测量海表面高度主要的误差源之一.本文采用Jason-1高度计Ku波段cycle060-cycle140的地球物理数据(GDR),基于泰勒展开公式对海况偏差构造以有效波高和风速为变量的32种参数模型.对测高数据进行...海况偏差(Sea State Bias,SSB)是卫星高度计测量海表面高度主要的误差源之一.本文采用Jason-1高度计Ku波段cycle060-cycle140的地球物理数据(GDR),基于泰勒展开公式对海况偏差构造以有效波高和风速为变量的32种参数模型.对测高数据进行编辑,根据海面高、有效波高和风速对所有模型参数进行求解,对求得的模型进行决定系数检验,获得最优的六参数模型.通过将六参数模型的海况偏差校正值与Jason-1高度计GDR数据的非参数模型海况偏差校正值相对比,计算两者互差的rms为1.47 cm,说明六参数模型的海况偏差估计值和非参数模型的海况偏差整体吻合度较好.利用交叉点处海面高不符值和验潮站数据分析六参数模型的海况偏差估计值改正后的海面高观测精度.六参数模型的海况偏差估计值可以将交叉点海面高不符值的rms降低0.001 m.在其他条件都相同的情况下,六参数模型得到的海况偏差估计值改正后的海面高与验潮站数据差值的标准差以及相关系数均略优于GDR中非参数模型的海况偏差改正的海面高与验潮站数据的差值.所以本文的海况偏差六参数模型可以提高雷达高度计的测高精度.展开更多
Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where on...Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.展开更多
文摘海况偏差(Sea State Bias,SSB)是卫星高度计测量海表面高度主要的误差源之一.本文采用Jason-1高度计Ku波段cycle060-cycle140的地球物理数据(GDR),基于泰勒展开公式对海况偏差构造以有效波高和风速为变量的32种参数模型.对测高数据进行编辑,根据海面高、有效波高和风速对所有模型参数进行求解,对求得的模型进行决定系数检验,获得最优的六参数模型.通过将六参数模型的海况偏差校正值与Jason-1高度计GDR数据的非参数模型海况偏差校正值相对比,计算两者互差的rms为1.47 cm,说明六参数模型的海况偏差估计值和非参数模型的海况偏差整体吻合度较好.利用交叉点处海面高不符值和验潮站数据分析六参数模型的海况偏差估计值改正后的海面高观测精度.六参数模型的海况偏差估计值可以将交叉点海面高不符值的rms降低0.001 m.在其他条件都相同的情况下,六参数模型得到的海况偏差估计值改正后的海面高与验潮站数据差值的标准差以及相关系数均略优于GDR中非参数模型的海况偏差改正的海面高与验潮站数据的差值.所以本文的海况偏差六参数模型可以提高雷达高度计的测高精度.
基金This work was supported by the Major Project for New Generation of AI(No.2018AAA0100400)the National Natural Science Foundation of China(No.41706010)+1 种基金the Joint Fund of the Equipments Pre-Research and Ministry of Education of China(No.6141A020337)and the Fundamental Research Funds for the Central Universities of China.
文摘Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.
