利用卫星测高技术监测2006-2007年厄尔尼诺现象

利用欧空局(ESA)提供的最新 Envisat-1 卫星测高数据由共线平差方法计算出热带太平洋地区海平面相对于正常年份的高度变化并绘制 30 ′× 30 ′月海面异常图,对 2006-2007 年厄尔尼诺现象进行了研究分析,监测到其发生、发展、消亡的全过程,揭示了利用卫星测高技术研究厄尔尼诺现象的可行性.     

卫星测高技术在监测海啸现象中的研究及应用

利用卫星测高技术,通过共线平差和沿轨、垂直轨迹测高大地水准面梯度的计算,得到了海啸发生2个小时后的海面波高和海面梯度异常现象,为利用卫星测高技术监测海啸、模拟海啸现象提供了依据。     

卫星测高技术的校正和性能

卫星测高技术的校正和性能１引言利用人造卫星测高技术去测量海平面高度和大洋表面形态，已被一系列星载测高仪的成功试验得以验证。在以往发射的人造卫星中，如“天空实验室”（Ｓｋｙｌａｂ）、Ｇｅｏｓ３、Ｇｅｏｓａｔ、Ｓｅａｓａｔ等卫星上都搭载了高准确度和精密度...     

联合卫星测高和卫星重力数据研究热容海平面变化

首先用卫星测高资料计算了1993～2009年6月的全球平均海平面变化。用GRACE(gravity recovery andclimate experiment)时变重力场系数反演了2003～2009年6月全球平均海水质量变化。联合GRACE和卫星测高资料计算了2003～2009年6月的热容海平面变化,该变化呈上升趋势。用日本气象局Ishii等提供的海温数据计算了1993～2006年的海水引起的平均热膨胀海平面变化,1993～2003年间,全球海洋热膨胀引起的热容海平面呈上升趋势,约占同期平均海平面变化的一半。利用ARGO温盐数据计算了2004～2009年6月平均热容海平面变化,也呈上升态势,只是变化速率有所减慢。     

卫星测高技术应用研究回顾与展望

简要介绍了笔者所在单位近十年来密切跟踪世界发展动态,灵活运用高新技术,致力于卫星测高技术应用研究所取得的一些有理论意义和实用价值的成果,这些成果主要包括四个方面：卫星测高径向轨道误差时域和空域特征分析、卫星测高反演海洋重力场、卫星测高反演海底地形以及利用测高重力异常扩展超高阶地球位模型研究成果。最后对这一研究领域未来的发展方向作了展望。     

海平面变化对厄尔尼诺事件的响应

本文对厄尔尼诺事件影响下的东中国海月平均海平面变化进行了对比分析，结果发现，在厄尔尼诺形成和发展期间．海平面下降并出现负距平值．在厄尔尼诺过程温度距平强度指数急剧上升时，海平面下降明显，最后对厄尔尼诺事件的发生和发展与海平面变化的关系做了讨论．     

中国近海海平面变化区域相关分析

由测高卫星升、降弧段海面高交叉点约束方法,用TOPEX／POSEIDON测高数据计算了黄海、东海、南海海域的海平面变化;分析了三个海区海平面变化的相关性;在频域内讨论了它们之间的相干性;分析了海水面积随纬度带的变化对不同纬度分布的海区海平面变化量的影响。     

2004年厄尔尼诺事件的理论预测和实践检验

地球自转速度减慢、赤道东太平洋表层海温升高、日食-厄尔尼诺系数增大与厄尔尼诺事件发生时间在一切细节上有惊人的对应关系。这个综合检验结果给出了地球自转减速和日食-厄尔尼诺系数预测厄尔尼诺事件的有效性和准确性。2004年的弱厄尔尼诺已得到初步证实,2005年的拉尼娜有待检验。     

1997/1998厄尔尼诺的发生与发展

厄尔尼诺现象是大尺度海气相互作用过程而引起的异常变化.由于伴随着厄尔尼诺的发生往往出现全球范围的气候异常,因此厄尔尼诺的诊断和预测引起了海洋学家和气象学家的密切注意.为了建立和改善厄尔尼诺的预报模式,寻求可供厄尔尼诺预报使用的预报因子和诊断厄尔尼诺发生与发展的物理条件是科学工作者面临的重大挑战.长期以来,为了选取那些具有明显的物理意义、预报提前量足够大的物理量,及早地诊断厄尔尼诺的发生、发展和消衰,海洋和气象学界付出了不懈的努力.例如,各种厄尔尼诺和南方涛动指数及反映全球气候异常的指标等都是这种努力的结果.回顾既往发生的厄尔尼诺过程,虽然有其共同的一面,但具体个例之间却又不尽相同.     

El Ni(n)o phenomenon in simple ocean data assimilation data

To study how the air and sea interact with each other during El Nino/La Nina onsets, extended associate pattern analysis (EAPA) is adopted with the simple ocean data assimilation (SODA) data. The results show that as El Nino/La Nina' s parents their behaviors are quite different, there does not exist a relatively independent tropical atmosphere but does exist a relatively independent tropical Pacific Ocean because the air is heated from the bottom surface instead of the top surface and of much stronger baroclinic instability than the sea and has a very large inter-tropical convergence zone covering the most tropical Pacific Ocean. The idea that it is the wester burst and wind convergence, coming from middle latitudes directly that produce the seawater eastward movement and meridional convergence in the upper levels and result in the typical El Nino sea surface temperature warm signal is confirmed again.     

Trends in Extreme Mean Sea Level Quantiles from Satellite Altimetry

Satellite altimetry allows the study of sea-level long-term variability on a global and spatially uniform basis. Here quantile regression is applied to derive robust median regression trends of mean sea level as well as trends in extreme quantiles from radar altimetry time series. In contrast with ordinary least squares regression, which only provides an estimate on the rate of change of the mean of data distribution, quantile regression allows the estimation of trends at different quantiles of the data distribution, yielding a more complete picture of long-term variability. Trends derived from basin-wide averaged regional mean sea level time series are robust and similar for all quantiles, indicating that all parts of the data distribution are changing at the same rate. In contrast, trends are not robust and diverge across quantiles in the case of local time series. Trends are under- (over-)estimated in the western (eastern) equatorial Pacific. Furthermore, trends in the lowermost quantile (0.05) are larger than the median trend in the western Pacific, while trends in the uppermost quantile (0.95) are lower than the median trend in the eastern Pacific. These differences in trends in extreme mean sea level quantiles are explained by the exceptional effect of the strong 1997–1998 El Niño–Southern Oscillation (ENSO) event.     

Perturbed solution of sea-air oscillator for the E1 Ni(n)o/La Ni(n)a-Southern Oscillation mechanism

A class of coupled system of the E1 Ni(n)o/La Ni(n)a-Southern Oscillation (ENSO) mechanism is studied. Using the perturbed theory, the asymptotic expansions of the solution for ENSO model are obtained and the asymptotic behavior of solution for corresponding problem is considered.     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

Extension of Satellite Altimetry Jason-2 Sea Level Anomalies Towards the Red Sea Coast Using Polynomial Harmonic Techniques

Satellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 30–50 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output.     

Comparison of Sea Surface Heights Derived from Satellite Altimetry and from Ocean Bottom Pressure Gauges: The SW Pacific MOTEVAS Project

A bottom pressure gauge (BPG) was installed in proximity (3.7 km at closest approach) of Jason-1 and formerly TOPEX/Poseidon (T/P) ground track No. 238 at the Wusi site, located ∼ 10 km offshore off the west coast of Santo Island, Vanuatu, Southwest (SW) Pacific. Sea level variations are inferred from the bottom pressure, seawater temperature, and salinity, corrected for the measured surface atmospheric pressure. The expansion of the water column (steric increase in sea surface height, SSH) due to temperature and salinity changes is approximated by the equation of state. We compare time series of SSH derived from T/P Side B altimeter Geophysical Data Records (GDR) and Jason-1 Interim Geophysical Data Records (IGDR), with the gauge-inferred sea level variations. Since altimeter SSH is a geocentric measurement, whereas the gauge-inferred observation is a relative sea level measurement, SSH comparison is conducted with the means of both series removed in this study. In addition, high-rate (1-Hz) bottom pressure implied wave heights (H_1/3) are compared with the significant wave height (SWH) measured by Jason-1. Noticeable discrepancy is found in this comparison for high waves, however the differences do not contribute significantly to the difference in sea level variations observed between the altimeter and the pressure gauge. In situ atmospheric pressure measurements are also used to verify the inverse barometer (IB) and the dry troposphere corrections (DTC) used in the Jason IGDR. We observe a bias between the IGDR corrections and those derived from the local sensors. Standard deviations of the sea level differences between T/P and BPG is 52 mm and is 48 mm between Jason and BPG, indicating that both altimeters have similar performance at the Wusi site and that it is feasible to conduct long-term monitoring of altimetry at such a site.     

Comparison of Sea Surface Heights Derived from Satellite Altimetry and from Ocean Bottom Pressure Gauges: The SW Pacific MOTEVAS Project

A bottom pressure gauge (BPG) was installed in proximity (3.7 km at closest approach) of Jason-1 and formerly TOPEX/Poseidon (T/P) ground track No. 238 at the Wusi site, located ～ 10 km offshore off the west coast of Santo Island, Vanuatu, Southwest (SW) Pacific. Sea level variations are inferred from the bottom pressure, seawater temperature, and salinity, corrected for the measured surface atmospheric pressure. The expansion of the water column (steric increase in sea surface height, SSH) due to temperature and salinity changes is approximated by the equation of state. We compare time series of SSH derived from T/P Side B altimeter Geophysical Data Records (GDR) and Jason-1 Interim Geophysical Data Records (IGDR), with the gauge-inferred sea level variations. Since altimeter SSH is a geocentric measurement, whereas the gauge-inferred observation is a relative sea level measurement, SSH comparison is conducted with the means of both series removed in this study. In addition, high-rate (1-Hz) bottom pressure implied wave heights (H _1/3 ) are compared with the significant wave height (SWH) measured by Jason-1. Noticeable discrepancy is found in this comparison for high waves, however the differences do not contribute significantly to the difference in sea level variations observed between the altimeter and the pressure gauge. In situ atmospheric pressure measurements are also used to verify the inverse barometer (IB) and the dry troposphere corrections (DTC) used in the Jason IGDR. We observe a bias between the IGDR corrections and those derived from the local sensors. Standard deviations of the sea level differences between T/P and BPG is 52 mm and is 48 mm between Jason and BPG, indicating that both altimeters have similar performance at the Wusi site and that it is feasible to conduct long-term monitoring of altimetry at such a site.