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改进的高度计海况偏差估计参数模型研究 总被引：2，自引：0，他引：2
《中国海洋大学学报(自然科学版)》2015,(12)
本文基于JASON-2雷达高度计共144个周期的8 640万个数据,按照距离加权平均法对其进行共线处理。采用直接估计的方法得到海况偏差(SSB),以此为真值,利用最小二乘法,对有效波高(SWH)和风速(U)的32种参数模型进行拟合并筛选,获得最优海况偏差估计参数模型。将最优参数模型应用于中国HY-2高度计,并与HY-2采用的传统SSB估计参数模型结果比较。结果表明:改进后的SSB估计参数模型有效,优于传统SSB参数模型。 相似文献
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利用机器学习的方法,对14个周期HY-2A卫星高度计数据:风速、有效波高和海面高度差值进行训练,探究海况偏差和风速、有效波高之间的关系,创建海况偏差核函数非参数模型(NPSSB),并与参数模型中具有代表性的BM3、BM4模型进行对比。研究表明:(1)核函数NPSSB模型能够很好的反映SSB与U、SWH之间的关系,SSB与U呈二次函数关系,SSB与SWH呈反比例函数关系;(2)核函数NPSSB模型对SSB的模拟能力与训练数据集相关,数据量越多,模拟能力越好;(3)核函数NPSSB模型与BM3、BM4模型都存在0^-0.03 m的差值,随着风速和有效波高的增加,差值的绝对值越大。 相似文献
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雷达高度计海况偏差估计神经网络模型研究 总被引：2，自引：2，他引：0
本文基于Jason-2高度计数据,在12个不同季节的cycle数据中组合1~6个cycle的有效波高、风速和海况偏差为训练集,选取Jason-2的另外3个不同季节的cycle数据集为测试集。经检验分析,确定3个cycle对应的BP神经网络模型。将该模型应用于HY-2高度计海况偏差的估计,通过海况偏差与有效波高及风速的拟合优度、解释方差和残差对比分析,结果表明:神经网络BP模型可以有效应用于HY-2的海况偏差估计并明显优于传统海况偏差参数模型。 相似文献
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本文基于HY-2高度计与Jason-2高度计时空匹配数据集,将匹配点Jason-2的海况偏差视为真值、HY-2的有效波高和风速为变量,利用最小二乘法建立海况偏差估计六参数模型。将所建模型应用于HY-2第70、71cycle数据,结果表明:改进后的海况偏差估计参数模型优于传统海况偏差估计参数模型,能够有效提高HY-2的海况偏差估计精度。 相似文献
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本文基于Amarouche的二阶理论回波模型,导出了带有偏度系数的二阶理论回波模型;针对HY-2A卫星高度计波形特点,引入了奇异值分解滤波,并根据最大似然估计算法反演参数的不同得到6种重跟踪方案;利用其中的五参数方案处理该波形数据,获得海面散射点高度概率密度函数中偏度的合理取值为0.15;将结果分别与浮标、Jason-1和HY-2AIDR有效波高对比,分析6种方案反演有效波高的准确度,确定了MLE4_SVD(波形重跟踪之前进行滤波)对HY-2A高度计重跟踪更适合反演有效波高。 相似文献
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通过网格定点法对我国东南沿海区域性台风危险性进行了分析。利用对各网格点有影响的历史台风数据,建立了各网格点的台风关键参数的最优概率模型。利用Monte-Carlo方法产生每个网格点1000年间的虚拟台风事件。采用YanMeng(YM)风场模型模拟了100个历史台风的最大风速,通过使这些最大风速与观测的最大风速误差和最小,建立了一组新的计算最大风半径Rmax和Holland气压参数B的公式,结果表明新的台风参数计算方案效果良好。利用新的参数计算方案、YM风场模型、特定地点的台风衰减模型以及合适的极值分布模型,预测了各个网格点不同重现期的极值风速,进而绘制了台风多发区域的设计风速图。最后研究了不同B模型、Rmax模型和极值分布模型对预测的极值风速的影响。可以为结构抗风设计和台风防灾减灾提供新的参考。 相似文献
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SYLVIE LABROUE PHILIPPE GASPAR JOEL DORANDEU OZ ZANIFé F. MERTZ PATRICK VINCENT 《Marine Geodesy》2013,36(3-4):453-481
The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved. 相似文献
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Sylvie Labroue Philippe Gaspar Joel Dorandeu Oz Zanif F. Mertz Patrick Vincent Denis Choquet 《Marine Geodesy》2004,27(3):453-481
The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved. 相似文献
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Calibration/Validation of an Altimeter Wave Period Model and Application to TOPEX/Poseidon and Jason-1 Altimeters 总被引：1，自引：0，他引：1
The altimeter radar backscatter cross-section is known to be related to the ocean surface wave mean square slope statistics, linked to the mean surface acceleration variance according to the surface wave dispersion relationship. Since altimeter measurements also provide significant wave height estimates, the precedent reasoning was used to derive empirical altimeter wave period models by combining both significant wave height and radar backscatter cross-section measurements. This article follows such attempts to propose new algorithms to derive an altimeter mean wave period parameter using neural networks method. Two versions depending on the required inputs are presented. The first one makes use of Ku-band measurements only as done in previous studies, and the second one exploits the dual-frequency capability of modern altimeters to better account for local environmental conditions. Comparison with in situ measurements show high correlations which give confidence in the derived altimeter wave period parameter. It is further shown that improved mean wave characteristics can be obtained at global and local scales by using an objective interpolation scheme to handle relatively coarse altimeter sampling and that TOPEX/Poseidon and Jason-1 altimeters can be merged to provide altimeter mean wave period fields with a better resolution. Finally, altimeter mean wave period estimates are compared with the WaveWatch-III numerical wave model to illustrate their usefulness for wave models tuning and validation. 相似文献
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The altimeter radar backscatter cross-section is known to be related to the ocean surface wave mean square slope statistics, linked to the mean surface acceleration variance according to the surface wave dispersion relationship. Since altimeter measurements also provide significant wave height estimates, the precedent reasoning was used to derive empirical altimeter wave period models by combining both significant wave height and radar backscatter cross-section measurements. This article follows such attempts to propose new algorithms to derive an altimeter mean wave period parameter using neural networks method. Two versions depending on the required inputs are presented. The first one makes use of Ku-band measurements only as done in previous studies, and the second one exploits the dual-frequency capability of modern altimeters to better account for local environmental conditions. Comparison with in situ measurements show high correlations which give confidence in the derived altimeter wave period parameter. It is further shown that improved mean wave characteristics can be obtained at global and local scales by using an objective interpolation scheme to handle relatively coarse altimeter sampling and that TOPEX/Poseidon and Jason-1 altimeters can be merged to provide altimeter mean wave period fields with a better resolution. Finally, altimeter mean wave period estimates are compared with the WaveWatch-III numerical wave model to illustrate their usefulness for wave models tuning and validation. 相似文献
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This article describes absolute calibration results for both JASON-1 and TOPEX Side B (TSB) altimeters obtained at the Lake Erie calibration site, Marblehead, Ohio, USA. Using 15 overflights, the estimated JASON altimeter bias at Marblehead is 58 ± 38 mm, with an uncertainty of 19 mm based on detailed error analysis. Assuming that the TSB bias is negligible, relative bias estimates using both data from the TSB-JASON formation flight period and data from 48 water level gauges around the entire Great Lakes confirmed the Marblehead results. Global analyses using both the formation flight data and dual-satellite (TSB and JASON) crossovers yield a similar relative bias estimate of 146 ± 59 mm, which agrees well with open ocean absolute calibration results obtained at Harvest, Corsica, and Bass Strait (e.g., Watson et al. 2003). We find that there is a strong dependence of bias estimates on the choice of sea state bias (SSB) models. Results indicate that the invariant JASON instrument bias estimated oceanwide is 71 mm, with additional biases of 76 mm or 28 mm contributed by the choice of Collecte Localisation Satellites (CLS) SSB or Center for Space Research (CSR) SSB model, respectively. Similar analysis in the Great Lakes yields the invariant JASON instrument bias at 19 mm, with the SSB contributed biases at 58 mm or 13 mm, respectively. The reason for the discrepancy is currently unknown and warrants further investigation. Finally, comparison of the TOPEX/POSEIDON mission (1992-2002) data with the Great Lakes water level gauge measurements yields a negligible TOPEX altimeter drift of 0.1 mm/yr. 相似文献
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《Marine Geodesy》2013,36(3-4):335-354
This article describes absolute calibration results for both JASON-1 and TOPEX Side B (TSB) altimeters obtained at the Lake Erie calibration site, Marblehead, Ohio, USA. Using 15 overflights, the estimated JASON altimeter bias at Marblehead is 58 ± 38 mm, with an uncertainty of 19 mm based on detailed error analysis. Assuming that the TSB bias is negligible, relative bias estimates using both data from the TSB-JASON formation flight period and data from 48 water level gauges around the entire Great Lakes confirmed the Marblehead results. Global analyses using both the formation flight data and dual-satellite (TSB and JASON) crossovers yield a similar relative bias estimate of 146 ± 59 mm, which agrees well with open ocean absolute calibration results obtained at Harvest, Corsica, and Bass Strait (e.g., Watson et al. 2003). We find that there is a strong dependence of bias estimates on the choice of sea state bias (SSB) models. Results indicate that the invariant JASON instrument bias estimated oceanwide is 71 mm, with additional biases of 76 mm or 28 mm contributed by the choice of Collecte Localisation Satellites (CLS) SSB or Center for Space Research (CSR) SSB model, respectively. Similar analysis in the Great Lakes yields the invariant JASON instrument bias at 19 mm, with the SSB contributed biases at 58 mm or 13 mm, respectively. The reason for the discrepancy is currently unknown and warrants further investigation. Finally, comparison of the TOPEX/POSEIDON mission (1992–2002) data with the Great Lakes water level gauge measurements yields a negligible TOPEX altimeter drift of 0.1 mm/yr. 相似文献
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Altimeter measurements of sea‐level variability have errors due to the altimeter not repeatedly sampling the same point on the ocean surface. The errors are proportional to the local slope of the mean sea surface. Accurate removal of geoid error is essential if altimeter data are to be used to study the relationship between geostrophic turbulence and bathymetry. The error can be reduced by using an accurate model of the mean surface. We use the multiyear TOPEX altimeter data set to develop a model for the mean sea surface along the TOPEX/POSEIDON ground track by estimating the coefficients of a local plane centered on every 2 km x 7 km bin sampled by the altimeter. We have evaluated the ability of this model. compared against two global mean sea‐surface models, to reduce the error associated with steep gradients. The two global models are the Center for Space Research 1995 model and the Ohio State University 1995 model. The three models show similar variability over the oceans, and none shows the large residual errors that can be seen in collinear analysis near some seamounts and trenches. The standard deviation of the variability using the plane model, however, is consistently smaller in low‐variability, high‐geoid‐gradient areas, indicating a slightly better performance than the two global models. 相似文献
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研究一般的回归模型中误差方差的二次型估计的容许性，研究方法是模型的整体转化和局部转化，结果有：（１）二次约束下的线性模型等价于相应的无约束的线性模型。（２）线性（齐次或非齐次）等式约束下的线性模型等价于某个无约束的线性模型。（３）单个非齐次不等式约束下的线性模型等价于某个无约束的线性模型。（４）通过例子证明了多个线性不等式约束的线性模型不能等价于某个无约束的线性模型。（５）某类非齐次二次型估计的容许性等价于相应的齐次二次型估计的容许性 相似文献