共查询到20条相似文献，搜索用时 669 毫秒
1.
This paper summarizes the development steps of a 4D variational assimilation scheme for nearshore wave models. A partition method is applied for adjusting both wave boundary conditions and wind fields. Nonstationary conditions are assimilated by providing defined correlations of model inputs in time. The scheme is implemented into the SWAN model. Twin experiments covering both stationary and nonstationary wave conditions are carried out to assess the adequacy of the proposed scheme. Stationary experiments are carried out considering separately windsea, swells, and mixed sea. Cost functions decline to less than 5% and RMS spectrum errors are reduced to less than 10%. The nonstationary experiment covers 1 day simulation under mixed wave conditions with assimilation windows of 3 h. RMS spectrum errors are reduced to less than 10% after 30 iterations in most assimilation windows. The results show that for spacially uniform model inputs, model accuracy is improved notably by the assimilation scheme throughout the computational domain. It is found that under wave conditions in which observed spectra can be well classified, the assimilation scheme is able to improve model results significantly. 相似文献
2.
Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification. 相似文献
3.
Gregoire Broquet Pierre Brasseur David Rozier Jean-Michel Brankart Jacques Verron 《Ocean Dynamics》2008,58(1):1-17
The characterization of model errors is an essential step for effective data assimilation into open-ocean and shelf-seas models.
In this paper, we propose an experimental protocol to properly estimate the error statistics generated by imperfect atmospheric
forcings in a regional model of the Bay of Biscay, nested in a basin-scale North Atlantic configuration. The model used is
the Hybrid Coordinate Ocean Model (HYCOM), and the experimental protocol involves Monte Carlo (or ensemble) simulations. The
spatial structure of the model error is analyzed using the representer technique, which allows us to anticipate the subsequent
impact in data assimilation systems. The results show that the error is essentially anisotropic and inhomogeneous, affecting
mainly the model layers close to the surface. Even when the forcings errors are centered around zero, a divergence is observed
between the central forecast and the mean forecast of the Monte Carlo simulations as a result of nonlinearities. The 3D structure
of the representers characterizes the capacity of different types of measurement (sea level, sea surface temperature, surface
velocities, subsurface temperature, and salinity) to control the circulation. Finally, data assimilation experiments demonstrate
the superiority of the proposed methodology for the implementation of reduced-order Kalman filters. 相似文献
4.
A new methodology for the extension of the impact of data assimilation on ocean wave prediction 总被引：1，自引：1，他引：0
It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting
the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing”
the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues
to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited
portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination
of two different statistical tools—Kolmogorov–Zurbenko and Kalman filters—is employed so as to eliminate any systematic error
of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated
to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific
Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The
results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to
a significant reduction of the forecast bias. 相似文献
5.
A Bayesian hierarchical spatio-temporal model for significant wave height in the North Atlantic 总被引：1，自引：1，他引：0
Erik Vanem Arne Bang Huseby Bent Natvig 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(5):609-632
Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account in ship design and operation. Hence, there is a need for appropriate stochastic models describing the variability of sea states, taking into account long-term trends related to climate change. Various stochastic models of significant wave height are reported in the literature, but most are based on point measurements without considering spatial variations. As far as the authors are aware, no model of significant wave height to date exploits the flexible framework of Bayesian hierarchical space-time models. This framework allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space-time model for significant wave height. The model has been fitted by significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined, and the results from applying the model to monthly and daily data will be discussed. Different model alternatives have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are in reasonable agreement and also agree fairly well with previous studies. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms with relevant meteorological data will be presented. 相似文献
6.
Towards a data assimilation system for morphodynamic modeling: bathymetric data assimilation for wave property estimation 总被引：1，自引：1，他引：0
Data assimilation is mainly concerned with the proper management of uncertainties. The main objective of the present work is to implement and analyze a data assimilation technique capable of assimilating bathymetric data into a coupled flow, wave, and morphodynamic model. For the case presented here, wave significant height, wave direction of incidence, and wave peak period are being optimized based on bathymetric data taken from a twin experiment. An adjoint-free variational scheme is used. In this approach, a linear reduced order model (ROM) is constructed as an approximation of the full model. The ROM is an autoregressive model of order 1 (AR1) that preserves the parametrization. Since the ROM is linear, the construction of its adjoint is straightforward, making the implementation of 4D variational data assimilation effortless. The scheme is able to update the morphodynamic model satisfactorily despite the fact that the model shows nonlinear behavior even for very small perturbations of all three parameters. The size and direction of the perturbations necessary for constructing the ROM have a significant impact on the performance of the technique. 相似文献
7.
Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean 总被引：2，自引：0，他引：2
Xunqiang Yin Fangli Qiao Yongzeng Yang Changshui Xia Xianyao Chen 《Ocean Dynamics》2012,62(7):1059-1071
The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10?% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85?% and 80?%, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models. 相似文献
8.
Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations. 相似文献
9.
Spatial characteristics of ocean surface waves 总被引：2，自引：2，他引：0
Johannes Gemmrich Jim Thomson W. Erick Rogers Andrey Pleskachevsky Susanne Lehner 《Ocean Dynamics》2016,66(8):1025-1035
The spatial variability of open ocean wave fields on scales of O (10km) is assessed from four different data sources: TerraSAR-X SAR imagery, four drifting SWIFT buoys, a moored waverider buoy, and WAVEWATCH III? model runs. Two examples from the open north-east Pacific, comprising of a pure wind sea and a mixed sea with swell, are given. Wave parameters attained from observations have a natural variability, which decreases with increasing record length or acquisition area. The retrieval of dominant wave scales from point observations and model output are inherently different to dominant scales retrieved from spatial observations. This can lead to significant differences in the dominant steepness associated with a given wave field. These uncertainties have to be taken into account when models are assessed against observations or when new wave retrieval algorithms from spatial or temporal data are tested. However, there is evidence of abrupt changes in wave field characteristics that are larger than the expected methodological uncertainties. 相似文献
10.
We explore the ocean circulation estimates obtained by assimilating observational products made available by the Global Ocean
Data Assimilation Experiment (GODAE) and other sources in an incremental, four-dimensional variational data assimilation system
for the Intra-Americas Sea. Estimates of the analysis error (formally, the inverse Hessian matrix) are computed during the
assimilation procedure. Comparing the impact of differing sea surface height and sea surface temperature products on both
the final analysis error and difference between the model state estimates, we find that assimilating GODAE and non-GODAE products
yields differences between the model and observations that are comparable to the differences between the observation products
themselves. While the resulting analysis error estimates depend on the configuration of the assimilation system, the basic
spatial structures of the standard deviations of the ocean circulation estimates are fairly robust and reveal that the assimilation
procedure is capable of reducing the circulation uncertainty when only surface data are assimilated. 相似文献
11.
A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height (H s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model–data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point. 相似文献
12.
We compare different past sea level reconstructions over the 1950–2009 time span using the Empirical Orthogonal Function (EOF) approach. The reconstructions are based on 91 long (up to 60?years) but sparsely distributed tide-gauge records and gridded sea level data from two numerical ocean models over 1958–2007 (the DRAKKAR/NEMO model without data assimilation and the simple ocean data assimilation ocean reanalysis-SODA-) and satellite altimetry data over 1993–2009. We find that the reconstructed global mean sea level computed over the?~60-year-long time span little depends on the input spatial grids. This is unlike the regional variability maps that appear very sensitive to the considered input spatial grids. Using the DRAKKAR/NEMO model, we test the influence of the period covered by the input spatial grids and the number of EOFs modes used to reconstruct sea level. Comparing with tide-gauge records not used in the reconstruction, we determine optimal values for these two parameters. As suggested by previous studies, the longer the time span covered by the spatial grids, the better the fit with unused tide gauges. Comparison of the reconstructed regional trends over 1950–2009 based on the two ocean models and satellite altimetry grids shows good agreement in the tropics and substantial differences in the mid and high latitude regions, and in western boundary current areas as well. The reconstructed spatial variability seems very sensitive to the input spatial information. No clear best case emerges. Thus, using the longest available model-based spatial functions will not necessarily give the most realistic results as it will be much dependent on the quality of the model (and its associated forcing). Altimetry-based reconstructions (with 17-year long input grids) give results somewhat similar to cases with longer model grids. It is likely that better representation of the sea level regional variability by satellite altimetry compensates the shorter input grids length. While waiting for much longer altimetry records, improved past sea level reconstructions may be obtained by averaging an ensemble of different model-based reconstructions, as classically done in climate modelling. Here, we present such a ‘mean’ reconstruction (with associated uncertainty) based on averaging the three individual reconstructions discussed above. 相似文献
13.
Estimation of ocean circulation is investigated via assimilation of satellite measurements of the dynamic ocean topography (DOT) into the global finite-element ocean model (FEOM). The DOT was obtained by means of a geodetic approach from carefully cross-calibrated multi-mission altimeter data and GRACE gravity fields. The spectral consistency was achieved by consistently filtering both, the sea surface and the geoid. The filter length is determined by the spatial resolution of the gravity field and corresponds to approximately 241 km half width for the GRACE-based gravity field model ITG-Grace03s.The assimilation of the geodetic DOT was performed by employing a local singular evolutive interpolated Kalman (SEIK) filter in combination with the method of weighting of observations. It is shown that this approach leads to a successful assimilation technique that reduced the RMS difference between the model and the data from 16 cm to 5 cm during one year of assimilation. The ocean model returns an optimized mean dynamic ocean topography. The effects of assimilation on transport estimates across several hydrographic World Ocean Circulation Experiment (WOCE) sections show improvements compared to the FEOM run without data assimilation. As a result of the assimilation, DOT estimates are available in the polar or coastal regions where the geodetic estimates from satellite data alone are not adequate. Furthermore, more realistic features of the ocean can be seen in these areas compared to those obtained using the filtered data fields. 相似文献
14.
Long-term trends in the ocean wave climate because of global warming are of major concern to many stakeholders within the
maritime industries, and there is a need to take severe sea state conditions into account in design of marine structures and
in marine operations. Various stochastic models of significant wave height are reported in the literature, but most are based
on point measurements without exploiting the flexible framework of Bayesian hierarchical space–time models. This framework
allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge,
yet remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space–time model with a log-transform
for significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined,
and the results from applying the model to data of different temporal resolutions will be discussed. Different model alternatives
have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are
in reasonable agreement and also agree fairly well with previous studies. The log-transform was included in order to account
for observed heteroscedasticity in the data, and results are compared to previous results where a similar model was employed
without a log-transform. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms
with relevant meteorological data, will be presented. 相似文献
15.
The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event. 相似文献
16.
Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment 总被引：3，自引：0，他引：3
A global ocean data assimilation system based on the ensemble optimum interpolation (EnOI) has been under development as the
Chinese contribution to the Global Ocean Data Assimilation Experiment. The system uses a global ocean general circulation
model, which is eddy permitting, developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. In
this paper, the implementation of the system is described in detail. We describe the sampling strategy to generate the stationary
ensembles for EnOI. In addition, technical methods are introduced to deal with the requirement of massive memory space to
hold the stationary ensembles of the global ocean. The system can assimilate observations such as satellite altimetry, sea
surface temperature (SST), in situ temperature and salinity from Argo, XBT, Tropical Atmosphere Ocean (TAO), and other sources
in a straightforward way. As a first step, an assimilation experiment from 1997 to 2001 is carried out by assimilating the
sea level anomaly (SLA) data from TOPEX/Poseidon. We evaluate the performance of the system by comparing the results with
various types of observations. We find that SLA assimilation shows very positive impact on the modeled fields. The SST and
sea surface height fields are clearly improved in terms of both the standard deviation and the root mean square difference.
In addition, the assimilation produces some improvements in regions where mesoscale processes cannot be resolved with the
horizontal resolution of this model. Comparisons with TAO profiles in the Pacific show that the temperature and salinity fields
have been improved to varying degrees in the upper ocean. The biases with respect to the independent TAO profiles are reduced
with a maximum magnitude of about 0.25°C and 0.1 psu for the time-averaged temperature and salinity. The improvements on temperature
and salinity also lead to positive impact on the subsurface currents. The equatorial under current is enhanced in the Pacific
although it is still underestimated after the assimilation. 相似文献
17.
This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained. 相似文献
18.
Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter 总被引：4，自引：0，他引：4
Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system. 相似文献
19.
数据同化是提升复杂机理过程模型精度的关键技术之一,而湖泊藻类模型的敏感参数具有随时间动态变化的特征,导致数据同化过程中无法精准更新某一时段的敏感参数,影响数据同化的模型精度提升效果.针对上述问题,本研究耦合了参数敏感性分析与集合卡尔曼滤波,研发了一种能够实时识别模型敏感参数的新型数据同化算法;为验证研发算法的效率,依托巢湖的高频水质自动监测数据,测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化,并根据监测数据实时更新模型敏感参数,实现了水质高频自动监测数据与藻类动态模型的深度融合,藻类生物量模拟精度提升了55%,即纳什系数（NSE）从0.49提升到0.76,模拟精度提升效果也显著优于传统数据同化算法（NSE=0.63）.研发算法可应用于其它水生态环境模型的数据同化,为水生态环境相关要素的精准模拟预测提供关键技术支撑. 相似文献
20.
S. Becker M. Losch J. M. Brockmann G. Freiwald W.-D. Schuh 《Surveys in Geophysics》2014,35(6):1507-1525
Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography—the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99–110, 2012; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie 2012). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates. 相似文献