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1.
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   

2.
A comparison between in situ and satellite sea surface temperature (SST) is presented for the Western Mediterranean Sea during 1999. Several international databases are used to extract in situ data (World Ocean Database, MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea and International Comprehensive Ocean-Atmosphere Data Set). The in situ data are classified into different platforms or sensors (conductivity–temperature–depth, expendable bathythermographs, drifters, bottles, and ships), in order to assess the relative accuracy of these type of data with respect to Advanced Very High Resolution Radiometer SST satellite data. It is shown that the results of the error assessment vary with the sensor type, the depth of the in situ measurements, and the database used. Ship data are the most heterogeneous data set, and therefore present the largest differences with respect to in situ data. A cold bias is detected in drifter data. The differences between satellite and in situ data are not normally distributed. However, several analysis techniques, as merging and data assimilation, usually require Gaussian-distributed errors. The statistics obtained during this study will be used in future work to merge the in situ and satellite data sets into one unique estimation of the SST.  相似文献   

3.
An ensemble adjustment Kalman filter (EAKF) is used to assimilate Argo profiles of 2008 in a global version of the Modular Ocean Model version 4. Four assimilation experiments are carried out to compare with the simulation without data assimilation, which serves as the control experiment. All experiment results are compared with dataset of Global Temperature–Salinity Profile Program and satellite sea surface temperature (SST). The first experiment (Exp 1) is implemented by perturbing temperature of upper layers in the initial conditions (ICs) with an amplitude of 1.0°C and no ensemble inflation. The results from Exp 1 show that the simulated temperature (salinity) deviation in the upper 400 m (500 m) is reduced through Argo data assimilation; however, these deviations are increased in deeper layers. The error reduction in SST is much greater during January to June than during the rest of the year. Three more experiments are designed to understand the responses in different layers and months. Two of them test model sensitivities to ICs by perturbing them vertically: one over the vertical extent of the whole water column (Exp 2) and the other employs smaller perturbation amplitude of 0.1°C (Exp 3). Exp 2 shows that the simulated temperature and salinity deviations are systematically improved in the whole water column. Comparison between Exps 2 and 3 suggests that perturbation amplitude is important. Exp 4 tests the influence of the optimal inflation factor of 5%, which is determined by other set of numerical tests. Exp 4 improves assimilation performance much more than the other three experiments without inflation. Therefore, we conclude that the perturbation should be introduced to all model layers, proper perturbation amplitude is important for Ocean data assimilation using EAKF, and the ensemble inflation by an optimal inflation is critical to improve the skill of the EAKF analysis.  相似文献   

4.
Using Ensemble Adjustment Kalman Filter (EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model (FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly (SLA); another, to assimilate sea surface temperature (SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.  相似文献   

5.
Four existing sea surface temperature (SST) assimilation schemes are evaluated in terms of their performances in assimilating the advanced very high resolution radiometer pathfinder best SST data in the South China Sea using the Princeton Ocean Model. Schemes 1 and 2 project SST directly to subsurface according to model-based correlations between SST and subsurface temperature. The difference between these two schemes is related to the order of vertical projection and horizontal optimal interpolation (OI). In Scheme 1, the spatially non-uniform SST observations are first projected to subsurface levels, followed by horizontal OI at each level. While in Scheme 2, the remotely sensed SSTs are first optimally interpolated to all grid points at the surface, followed by projecting gridded SSTs to subsurface levels. Scheme 3 assumes that the mixed layer is well mixed and has a uniform temperature vertically. In Scheme 4, SST is propagated to subsurface levels using a linear relationship of temperature between any two neighboring depths (Scheme 4a) or between surface and subsurface (Scheme 4b), which is derived by empirical orthogonal function (EOF) technique. To verify the results of the four schemes, the authors use the hydrographic data from two cruises during the South China Sea Monsoon Experiment in April and June 1998. It was shown that all four schemes could improve the SST field by reducing about 50% of the root mean square errors (RMSEs). All but Scheme 3 can improve model thermocline structure that is too diffused otherwise, though the RMSEs increase in the thermocline, especially for Scheme 2 when the model has opposite bias between upper layers and lower layers. Scheme 3 fails in the subsurface depth by increasing the thermocline depth, especially when there is a cold model bias. Projecting SST downward by EOF technique can deepen the depth of assimilation especially in Scheme 4a. Both Schemes 4a and b can correct the bias in the mixed layer and do not change the vertical thermal structure.  相似文献   

6.
Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency radar surface currents are assimilated using an ensemble scheme which aims to obtain improved surface winds taking into account European Centre for Medium-Range Weather Forecasts winds as a first guess and surface current measurements. The objective of this study is to show that wind forcing can be improved using an approach similar to parameter estimation in ensemble data assimilation. Like variational assimilation schemes, the method provides an improved wind field based on surface current measurements. However, the technique does not require an adjoint, and it is thus easier to implement. In addition, it does not rely on a linearization of the model dynamics. The method is validated directly by comparing the analyzed wind speed to independent in situ measurements and indirectly by assessing the impact of the corrected winds on model sea surface temperature (SST) relative to satellite SST.  相似文献   

7.
In this study, we implement Particle Filter (PF)-based assimilation algorithms to improve root-zone soil moisture (RZSM) estimates from a coupled SVAT-vegetation model during a growing season of sweet corn in North Central Florida. The results from four different PF algorithms were compared with those from the Ensemble Kalman Filter (EnKF) when near-surface soil moisture was assimilated every 3 days using both synthetic and field observations. In the synthetic case, the PF algorithm with the best performance used residual resampling of the states and obtained resampled parameters from a uniform distribution and provided reductions of 76% in root mean square error (RMSE) over the openloop estimates. The EnKF provided the RZSM and parameter estimates that were closer to the truth than the PF with an 84% reduction in RMSE. When field observations were assimilated, the PF algorithm that maintained maximum parameter diversity offered the largest reduction of 16% in root mean square difference (RMSD) over the openloop estimates. Minimal differences were observed in the overall performance of the EnKF and PF using field observations since errors in model physics affected both the filters in a similar manner, with maximum reductions in RMSD compared to the openloop during the mid and reproductive stages.  相似文献   

8.
Coupled assimilation for an intermediated coupled ENSO prediction model   总被引：4，自引：0，他引：4  
Fei Zheng  Jiang Zhu 《Ocean Dynamics》2010,60(5):1061-1073
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   

9.
Assimilation of SLA and SST data into an OGCM for the Indian Ocean   总被引：6，自引：0，他引：6  
 Remotely sensed observations of sea-level anomaly and sea-surface temperature have been assimilated into an implementation of the Miami Isopycnic Coordinate Ocean Model (MICOM) for the Indian Ocean using the Ensemble Kalman Filter (EnKF). The system has been applied in a hindcast validation experiment to examine the properties of the assimilation scheme when used with a full ocean general circulation model and real observations. This work is considered as a first step towards an operational ocean monitoring and forecasting system for the Indian Ocean. The assimilation of real data has demonstrated that the sequential EnKF can efficiently control the model evolution in time. The use of data assimilation requires a significant amount of additional processing and computational resources. However, we have tried to justify the cost of using a sophisticated assimilation scheme by demonstrating strong regional and temporal dependencies of the covariance statistics, which include highly anisotropic and flow-dependent correlation functions. In particular, we observed a marked difference between error statistics in the equatorial region and at off-equatorial latitudes. We have also demonstrated how the assimilation of SLA and SST improves the model fields with respect to real observations. Independent in situ temperature profiles have been used to examine the impact of assimilating the remotely sensed observations. These intercomparisons have shown that the model temperature and salinity fields better resemble in situ observations in the assimilation experiment than in a model free-run case. On the other hand, it is also expected that assimilation of in situ profiles is needed to properly control the deep ocean circulation. Received: 8 January 2002 / Accepted: 8 April 2002  相似文献   

10.
Twenty-four years of AVHRR-derived sea surface temperature (SST) data (1985–2008) and 35 years of NOCS (V.2) in situ-based SST data (1973–2008) were used to investigate the decadal scale variability of this parameter in the Mediterranean Sea in relation to local air–sea interaction and large-scale atmospheric variability. Satellite and in situ-derived data indicate a strong eastward increasing sea surface warming trend from the early 1990s onwards. The satellite-derived mean annual warming rate is about 0.037°C year–1 for the whole basin, about 0.026°C year–1 for the western sub-basin and about 0.042°C year–1 for the eastern sub-basin over 1985–2008. NOCS-derived data indicate similar variability but with lower warming trends for both sub-basins over the same period. The long-term Mediterranean SST spatiotemporal variability is mainly associated with horizontal heat advection variations and an increasing warming of the Atlantic inflow. Analysis of SST and net heat flux inter-annual variations indicates a negative correlation, with the long-term SST increase, driving a net air–sea heat flux decrease in the Mediterranean Sea through a large increase in the latent heat loss. Empirical orthogonal function (EOF) analysis of the monthly average anomaly satellite-derived time series showed that the first EOF mode is associated with a long-term warming trend throughout the whole Mediterranean surface and it is highly correlated with both the Eastern Atlantic (EA) pattern and the Atlantic Multidecadal Oscillation (AMO) index. On the other hand, SST basin-average yearly anomaly and NAO variations show low and not statistically significant correlations of opposite sign for the eastern (negative correlation) and western (positive correlation) sub-basins. However, there seems to be a link between NAO and SST decadal-scale variations that is particularly evidenced in the second EOF mode of SST anomalies. NOCS SST time series show a significant SST rise in the western basin from 1973 to the late 1980s following a large warming of the inflowing surface Atlantic waters and a long-term increase of the NAO index, whereas SST slowly increased in the eastern basin. In the early 1990s, there is an abrupt change from a very high positive to a low NAO phase which coincides with a large change in the SST spatiotemporal variability pattern. This pronounced variability shift is followed by an acceleration of the warming rate in the Mediterranean Sea and a change in the direction (from westward to eastward) of its spatial increasing tendency.  相似文献   

11.
In this work, a dual-pass data assimilation scheme is developed to improve predictions of surface flux. Pass 1 of the dual-pass data assimilation scheme optimizes the model vegetation parameters at the weekly temporal scale, and Pass 2 optimizes the soil moisture at the daily temporal scale. Based on ensemble Kalman filter(EnKF), the land surface temperature(LST) data derived from the new generation of Chinese meteorology satellite(FY3A-VIRR) are assimilated into common land model(CoLM) for the first time. Six sites, Daman, Guantao, Arou, BJ, Miyun and Jiyuan, are selected for the data assimilation experiments and include different climatological conditions. The results are compared with those from a dataset generated by a multi-scale surface flux observation system that includes an automatic weather station(AWS), eddy covariance(EC) and large aperture scintillometer(LAS). The results indicate that the dual-pass data assimilation scheme is able to reduce model uncertainties and improve predictions of surface flux with the assimilation of FY3A-VIRR LST data.  相似文献   

12.
A coupled ocean and boundary layer flux numerical modeling system is used to study the upper ocean response to surface heat and momentum fluxes associated with a major hurricane, namely, Hurricane Dennis (July 2005) in the Gulf of Mexico. A suite of experiments is run using this modeling system, constructed by coupling a Navy Coastal Ocean Model simulation of the Gulf of Mexico to an atmospheric flux model. The modeling system is forced by wind fields produced from satellite scatterometer and atmospheric model wind data, and by numerical weather prediction air temperature data. The experiments are initialized from a data assimilative hindcast model run and then forced by surface fluxes with no assimilation for the time during which Hurricane Dennis impacted the region. Four experiments are run to aid in the analysis: one is forced by heat and momentum fluxes, one by only momentum fluxes, one by only heat fluxes, and one with no surface forcing. An equation describing the change in the upper ocean hurricane heat potential due to the storm is developed. Analysis of the model results show that surface heat fluxes are primarily responsible for widespread reduction (0.5°–1.5°C) of sea surface temperature over the inner West Florida Shelf 100–300 km away from the storm center. Momentum fluxes are responsible for stronger surface cooling (2°C) near the center of the storm. The upper ocean heat loss near the storm center of more than 200 MJ/m2 is primarily due to the vertical flux of thermal energy between the surface layer and deep ocean. Heat loss to the atmosphere during the storm’s passage is approximately 100–150 MJ/m2. The upper ocean cooling is enhanced where the preexisting mixed layer is shallow, e.g., within a cyclonic circulation feature, although the heat flux to the atmosphere in these locations is markedly reduced.  相似文献   

13.
A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63℃ and 0.34 psu.  相似文献   

14.
A global eddy-admitting ocean/sea-ice simulation driven over 1958–2004 by daily atmospheric forcing is used to evaluate spatial patterns of sea level change between 1993 and 2001. In the present study, no data assimilation is performed. The model is based on the Nucleus for European Models of the Ocean code at the 1/4° resolution, and the simulation was performed without data assimilation by the DRAKKAR project. We show that this simulation correctly reproduces the observed regional sea level trend patterns computed using satellite altimetry data over 1993–2001. Generally, we find that regional sea level change is best simulated in the tropical band and northern oceans, whereas the Southern Ocean is poorly simulated. We examine the respective contributions of steric and bottom pressure changes to the total regional sea level changes. For the steric component, we analyze separately the contributions of temperature and salinity changes as well as upper and lower ocean contributions. Generally, the model results show that most regional sea level changes arise from temperature changes in the upper 750 m of the ocean. However, contributions of salinity changes and deep steric changes can be locally important. We also propose a map of ocean bottom pressure changes. Finally, we assess the robustness of such a model by comparing this simulation with a second simulation performed by MERCATOR-Ocean based on the same core model, but differing by its short length of integration (1992–2001) and its surface forcing data set. The long simulation presents better performance over 1993–2001 than the short simulation, especially in the Southern Ocean where a long adjustment time seems to be needed. In memory of my little brother Jean-Eudes, whose thirst for science filled out the rich discussions we had about my investigations and his job as user-service provider for MERCATOR-Ocean.  相似文献   

15.
Application of altimetry data assimilation on mesoscale eddies simulation   总被引：3，自引：0，他引：3  
Mesoscale eddy plays an important role in the ocean circulation. In order to improve the simulation accuracy of the mesoscale eddies, a three-dimensional variation (3DVAR) data assimilation system called Ocean Variational Analysis System (OVALS) is coupled with a POM model to simulate the mesoscale eddies in the Northwest Pacific Ocean. In this system, the sea surface height anomaly (SSHA) data by satellite altimeters are assimilated and translated into pseudo temperature and salinity (T-S) profile data. Then, these profile data are taken as observation data to be assimilated again and produce the three-dimensional analysis T-S field. According to the characteristics of mesoscale eddy, the most appropriate assimilation parameters are set up and testified in this system. A ten years mesoscale eddies simulation and comparison experiment is made, which includes two schemes: assimilation and non-assimilation. The results of comparison between two schemes and the observation show that the simulation accuracy of the assimilation scheme is much better than that of non-assimilation, which verified that the altimetry data assimilation method can improve the simulation accuracy of the mesoscale dramatically and indicates that it is possible to use this system on the forecast of mesoscale eddies in the future.  相似文献   

16.
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   

17.
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   

18.
Two mutually exclusive ocean models, Ocean general circulation model for the Earth Simulator (OFES) and the Bluelink ReANalysis (version 2.1; BRAN2.1), and the spin-up model (SPINUP4) of BRAN2.1 were used to investigate seasonal variability of the East Australian Current (EAC). These model outputs were tested against satellite and in situ data. The seasonally averaged sea surface temperature (SST) in the OFES and SPINUP4 shows a negative bias of 1 °C. However, the OFES, SPINUP4, and BRAN2.1 have a similar seasonal cycle in SST. The annual mean EAC transport computed at 28°S from the three models shows a good agreement with annual mean transport computed using the in situ data. However, they have considerable differences in terms of annual cycle. A better performance of the BRAN2.1 in simulating the temperature field is a result of data assimilation. The advection of heat across the open boundaries contributes ～50 % of the heat content change in the region. This study suggests that the advection by the EAC plays a significant role in heat content change of the region.  相似文献   

19.
Active and break phases of the Indian summer monsoon are associated with sea surface temperature (SST) fluctuations at 30–90 days timescale in the Arabian Sea and Bay of Bengal. Mechanisms responsible for basin-scale intraseasonal SST variations have previously been discussed, but the maxima of SST variability are actually located in three specific offshore regions: the South-Eastern Arabian Sea (SEAS), the Southern Tip of India (STI) and the North-Western Bay of Bengal (NWBoB). In the present study, we use an eddy-permitting 0.25° regional ocean model to investigate mechanisms of this offshore intraseasonal SST variability. Modelled climatological mixed layer and upper thermocline depth are in very good agreement with estimates from three repeated expendable bathythermograph transects perpendicular to the Indian Coast. The model intraseasonal forcing and SST variability agree well with observed estimates, although modelled intraseasonal offshore SST amplitude is undere-stimated by 20–30 %. Our analysis reveals that surface heat flux variations drive a large part of the intraseasonal SST variations along the Indian coastline while oceanic processes have contrasted contributions depending of the region considered. In the SEAS, this contribution is very small because intraseasonal wind variations are essentially cross-shore, and thus not associated with significant upwelling intraseasonal fluctuations. In the STI, vertical advection associated with Ekman pumping contributes to ～30 % of the SST fluctuations. In the NWBoB, vertical mixing diminishes the SST variations driven by the atmospheric heat flux perturbations by 40 %. Simple slab ocean model integrations show that the amplitude of these intraseasonal SST signals is not very sensitive to the heat flux dataset used, but more sensitive to mixed layer depth.  相似文献   

20.
A new methodology for using buoy measurements in sea wave data assimilation   总被引：3，自引：2，他引：1  
One of the main drawbacks in modern sea wave data assimilation models is the limited temporal and spatial improvement obtained in the final forecasting products. This is mainly due to deviations coming either from the relevant atmospheric input or from the dynamics of the wave model, resulting to systematic errors of the forecasted fields of numerical wave models, when no observation is available for assimilation. A potential solution is presented in this work, based on a combination of advanced statistical techniques, data assimilation systems, and wave models. More precisely, Kalman filtering algorithms are implemented into the wave model WAM and the results are assimilated by an Optimum Interpolation Scheme, in order to extend the beneficial influence of the latter in time and space. The case studied concerns a 3-month period in an open sea area near the South-West coast of the USA (Pacific Ocean).  相似文献   

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