共查询到14条相似文献，搜索用时 109 毫秒
1.
The in situ sea surface salinity(SSS) measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°–60°S, 80°–120°E are used to assess the SSS retrieved from Aquarius(Aquarius SSS).Wind speed and sea surface temperature(SST) affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from My Ocean model. Results show that:(1) Before adjustment: compared with My Ocean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean.(2) After adjustment: the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity(the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is-0.05 compared with My Ocean SSS). Near the latitude 42°S, the adjusted SSS is well consistent with the My Ocean and the difference is approximately 0.004. 相似文献
2.
The performance of a z-level ocean model, the Modular Ocean Model Version 4(MOM4), is evaluated in terms of simulating the global tide with different horizontal resolutions commonly used by climate models. The performance using various sets of model topography is evaluated. The results show that the optimum filter radius can improve the simulated co-tidal phase and that better topography quality can lead to smaller rootmean square(RMS) error in simulated tides. Sensitivity experiments are conducted to test the impact of spatial resolutions. It is shown that the model results are sensitive to horizontal resolutions. The calculated absolute mean errors of the co-tidal phase show that simulations with horizontal resolutions of 0.5° and 0.25° have about 35.5% higher performance compared that with 1° model resolution. An internal tide drag parameterization is adopted to reduce large system errors in the tidal amplitude. The RMS error of the best tuned 0.25° model compared with the satellite-altimetry-constrained model TPXO7.2 is 8.5 cm for M_2. The tidal energy fluxes of M_2 and K_1 are calculated and their patterns are in good agreement with those from the TPXO7.2. The correlation coefficients of the tidal energy fluxes can be used as an important index to evaluate a model skill. 相似文献
3.
一种利用SMOS卫星观测的海表面盐度反演海水盐度廓线的方法 总被引：4，自引：1，他引：3
This paper proposes a new method to retrieve salinity profiles from the sea surface salinity(SSS) observed by the Soil Moisture and Ocean Salinity(SMOS) satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS(SMOS_SSS) with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error(RMSE) of the linear and nonlinear model are 0.08–0.16 and 0.08–0.14 for the upper 400 m, which are 0.01–0.07 and 0.01–0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed. 相似文献
4.
An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products 总被引：1，自引：0，他引：1
For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products. 相似文献
5.
The European Space Agency will launch the first salinity satellite for remotely sensing the global soil moisture and ocean salinity (SMOS) at a sun-synchronous orbit in 2009. One of the payloads on the satellite is a synthetic aperture microwave radiometer (MIRAS), which is an innovative instrument designed as a two-dimensional (2D) interferometer for acquiring brightness temperature (TB) at L-band (1.4 GHz). MIRAS allows measuring TB at a series of incidences for full polarizations. As the satellite travels, a given location within the 2D field of view is observed from different incidence angles. The authors develop a new scheme to retrieve the sea-surface salinity (SSS) from SMOS’s TB at multi-incidence angles in a pixel, utilizing the properties of emissivity changing with incidence angles. All measurements of a given Stokes parameter in a pixel are first fitted to incidence angles in three order polynomial, and then the smoothed data are used for retrieving the SSS. The procedure will remove the random noise in TB greatly. Furthermore, the new method shows that the error in retrieved SSS is very sensitive to the system biases in the calibrated TB of the sensor, but the error in the retrieval is also a system bias, which can be corrected by post-launch validation. Therefore, this method may also serve as a means to evaluate the calibration precision in TB. 相似文献
6.
Rainfall has two significant effects on the sea surface, including salinity decreasing and surface becoming rougher,which have further influence on L-band sea surface emissivity. Investigations using the Aquarius and TRMM 3B42 matchup dataset indicate that the retrieved sea surface salinity(SSS) is underestimated by the present Aquarius algorithm compared to numerical model outputs, especially in cases of a high rain rate. For example, the bias between satellite-observed SSS and numerical model SSS is approximately 2 when the rain rate is 25 mm/h. The bias can be eliminated by accounting for rain-induced roughness, which is usually modeled by rain-generated ring-wave spectrum. The rain spectrum will be input into the Small Slope Approximation(SSA) model for the simulation of sea surface emissivity influenced by rain. The comparison with theoretical model indicated that the empirical model of rain spectrumis more suitable to be used in the simulation. Further, the coefficients of the rain spectrum are modified by fitting the simulations with the observations of the 2–year Aquarius and TRMM matchup dataset. The calculations confirm that the sea surface emissivity increases with the wind speed and rain rate. The increase induced by the rain rate is rapid in the case of low rain rate and low wind speed. Finally, a modified model of sea surface emissivity including the rain spectrum is proposed and validated by using the matchup dataset in May 2014. Compared with observations, the bias of the rain-induced sea surface emissivity simulated by the modified modelis approximately 1e–4, and the RMSE is slightly larger than 1e–3. With using more matchup data, thebias between model retrieved sea surface salinities and observationsmay be further corrected,and the RMSE may be reduced to less than 1 in the cases of low rain rate and low wind speed. 相似文献
7.
The Argo float observations are used to investigate the mesoscale characteristics of the Antarctic Intermediate Water(AAIW) in the South Pacific in this paper. It is shown that a subsurface mesoscale phenomenon is probably touched by an Argo float during the float's ascent-descent cycles and is identified by the horizontal salinity gradient between the vertical temperature-salinity profiles. This shows that the transportation of the AAIW may be accompanied with the rich mesoscale characteristics. To derive the spatial length, time, and propagation characteristics of the mesoscale variability of the AAIW, the gridded temperature-salinity dataset ENACT/ENSEMBLE Version 3 constructed on the in-situ observations in the South Pacific since 2005 is used. The Empirical Mode Decomposition method is applied to decompose the isopycnal-averaged salinity anomaly from26.8 σθ–27.4 σθ, where the AAIW mainly resides, into the basin scale and two mesoscale modes. It is found that the first mesoscale mode with the length scale on the order of 1 000 km explains nearly 50% variability of the mesoscale characteristics of the AAIW. Its westward-propagation speeds are slower in the mid-latitude(around 1cm/s) and faster in the low latitude(around 6 cm/s), but with an increasing in the latitude band on 25°–30°S. The second mesoscale mode is of the length scale on the order of 500 km, explaining about 30% variability of the mesoscale characteristics of the AAIW. Its westward-propagation speed keeps nearly unchanged(around 0.5cm/s). These results presented the stronger turbulent motion of the subsurface ocean on the spatial scale, and also described the significant role of Argo program for the better understanding of the deep ocean. 相似文献
8.
Sampling errors of the global mean sea level derived from TOPEX/Poseidon (T/P) altimetry are explored using 31/ 4a of eddy-resolving numerical model outputs for sea level. By definition, the sampling errors would not exist if data were available everywhere at all times. Four problems with increasing and progressively added complexities are examined to understand the causes of the sampling errors. The first problem (P1) explores the error incurred because T/P with turning latitudes near 66° latitudes does not cover the entire globe. The second problem (P2) examines, in addition, the spatial sampling issue because samples are only available along T/P ground tracks. The third problem (P3) adds the additional complexity that sea level at any along track location is sampled only once every 10 d versus every 3 d for the model (i.e., the temporal sampling issue). The fourth problem (P4) incorporates the full complexity with the addition of real T/P data outages. The numerical model (Los Alamos POP model Run 11) conserves the total water volume, thus generating no global mean sea level variation. Yet when the model sea level is sampled in the four problems (with P4 using the real T/P sampling), variations occur as manifestations of the sampling errors. The results show root-mean-squares (rms) sampling errors for P1 of 0.67 (0.75) mm for 10 d (3 d) global mean sea level, 0.78 (0.86) mm for P2, 0.79 mm for P3, and 1.07 mm for P4, whereas the amplitudes of the sampling errors can be as large as 2.0 (2.7) mm for P1, 2.1 (2.7) mm for P2, 2.2 mm for P3, and 2.5 mm for P4. The results clearly show the largest source of the sampling errors to be the lack of global coverage (i.e., P1), which the model has actually underestimated due to its own less-than-global coverage (between latitudes about 77° latitudes). We have extrapolated that a truly global model would show the rms sampling error to be 1.14 (1.28) mm for P1, thus implying a substantially larger sampling error for P4. 相似文献
9.
In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product. 相似文献
10.
The distributional features of sea surface temperature and salinity(SST and SSS) in the Taiwan Straits have been analyzed using the SST and SSS underway measurements in August,1999.The characteristics of SST and SSS are summarized as foloows:There are several upwellings and diluted water in the Taiwan Straits.The upwellings are divided into two kinds:those along the western coast of the Taiwan Straits and those around the Taiwan Shoal.There are three sources of diluted water:diluted water of the Jiulongjiang River,diluted water of the Zhujiang River and diluted water of the Minjiang River. 相似文献
11.
针对SMOS和Aquarius海表盐度误差分析没有区分不同空间频谱信噪特征的问题,基于6种主要的遥感盐度分析产品,根据定性图像、纬向波数谱、均方根误差等指标,分析产品的有效分辨率并探讨其原因机制。研究表明:CATDS-0.25°分析产品所描述的盐度场中小尺度结构失真,其较高谱能量密度在热带海域以噪音为主,而在西边界流等海域以信号为主;BEC-L3-0.25°有着较小的均方根误差、清晰的盐度图像、显著的中尺度能量,最适于描绘中尺度(25~100 km)盐度特征;BEC-L4-0.25°被奇异谱分析方法过度平滑了盐度场;Aquarius-V2-1.00°通过局部平滑处理,在描述大尺度(＞100 km)盐度现象方面表现较好;Aquarius-CAP-1.00°通过主动-被动联合算法(CAP)减小了均方根误差,但图像中卫星轨道形态明显;CATDS-1.00°的图像形态、能量分布和误差特征与Aquarius-V2-1.00°相当。这些结论可为用户正确使用产品进行地球物理学研究提供参考。 相似文献
12.
热带印度洋降水、蒸发的时空特征及其对海表盐度的影响 总被引：3，自引：2，他引：1
本文利用降水、蒸发等资料分析热带印度洋年降水量、蒸发量、净淡水通量的分布特征，并选取4个典型海域来分析降水量、蒸发量、净淡水通量的季节变化和年际变化。结果表明:东印度洋的苏门答腊岛西部海域年降水量最大，季节变化较小，属全年降雨型；孟加拉湾的东北部和安达曼海的北部海域年降水量较大，其年际变化以4.2 mm/a的速率增长，强降水出现在5-9月；阿拉伯海的西部海域年降水量较小；南印度洋东部（20°~30°S，80°~110°E）海域年降水量较小，年蒸发量较大，年蒸发量在2000年之前以5.1 mm/a的速率增长，之后以4.5 mm/a的速率减小。本文还采用Argo盐度等资料探讨降水、蒸发对海表盐度的影响，研究结果表明:降水量远大于蒸发量的海域，海表盐度较低；降水量远小于蒸发量的海域，海表盐度较高。表层水平环流是导致高净淡水通量中心与低盐中心并不重合的主要原因，也是导致强蒸发中心与高盐中心并不重合的主要原因。选取的4个典型海域海表盐度的季节变化与净淡水通量关系不大，而是与表层水平环流有关。孟加拉湾强降水对表层盐度的影响显著，强降水发生后表层盐度降低0.2~0.8，其影响深度为30~50 m。 相似文献
13.
自欧洲土壤湿度和盐度卫星SMOS和美国宝瓶座盐度卫星Aquarius相继发射之后,多个数据中心发布了两颗卫星的海表盐度网格化产品,其中包括法国海洋研究院SMOS卫星数据小组发布SMOS Locean L3盐度产品、西班牙巴塞罗那专家中心发布SMOS BEC L4盐度产品和美国宇航局喷气动力实验室发布AquariusV3.0 CAP L3盐度产品。本文利用精确盐度现场观测资料从产品精度和模拟海洋现象能力两个方面对以上3种产品质量进行了评估。研究表明:(1) 在精度方面,与盐度现场资料相比,Aquarius CAP 产品质量最高,产品盐度偏差和均方根误差全年稳定且偏差较小,部分海域达到了设计精度;SMOS两种卫星产品在全球海域偏差较不稳定,个别月份出现异常偏差值;SMOS产品在低纬和开阔海域的数据质量相对较高,但在高纬海域仍存在较大误差,需要进一步提升;(2) 在刻画海洋现象方面,Aquarius产品在热带太平洋较好刻画了淡池东缘盐度锋,SMOS BEC产品的刻画能力次之,SMOS Locean产品在热带太平洋充满了小尺度噪音,描述物理现象方面表现偏差。 相似文献
14.
为解决海洋中大量观测数据只含有温度剖面而缺乏盐度观测的问题, 基于历史观测的温盐剖面资料, 考虑到盐度卫星数据的发展, 采用回归分析方法, 在孟加拉湾建立了盐度与温度、经纬度、表层盐度的关系, 并对不同反演方法的反演结果进行检验评估。结果发现, 在不引入海表盐度(sea surface salinity, SSS)时, 最佳反演模型是温度、温度的二次项与经纬度确定的回归模型, 而SSS的引入则可以进一步优化反演结果。将反演结果与观测结果进行对比, 显示用反演的盐度剖面计算的比容海面高度误差超过2cm, 而引入SSS后的误差低于1.5cm。SSS的引入能够较为真实地反映海洋盐度场的垂直结构和内部变化特征, 既能够捕捉到对上混合层有重要影响的SSS信号, 又能够反映盐度在跃层上的季节内变化以及盐度障碍层的季节变化。水团分析显示, 与气候态相比, 盐度反演结果可以更好地表征海洋上层水团的变化特征。 相似文献