Correcting surface winds by assimilating high-frequency radar surface currents in the German Bight

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.     

Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment

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.     

Ocean response to typhoon Nuri (2008) in western Pacific and South China Sea

Typhoon Nuri formed on 18 August 2008 in the western North Pacific east of the Philippines and traversed northwestward over the Kuroshio in the Luzon Strait where it intensified to a category 3 typhoon. The storm weakened as it passed over South China Sea (SCS) and made landfall in Hong Kong as a category 1 typhoon on 22 August. Despite the storm’s modest strength, the change in typhoon Nuri’s intensity was unique in that it strongly depended on the upper ocean. This study examines the ocean response to typhoon Nuri using the Princeton Ocean Model. An ocean state accounting for the sea-surface temperature (SST) and mesoscale eddy field prior to Nuri was constructed by assimilating satellite SST and altimetry data 12 days before the storm. The simulation then continued without further data assimilation, so that the ocean response to the strong wind can be used to understand processes. It is found that the SST cooling was biased to the right of the storm’s track due to inertial currents that rotated in the same sense as the wind vector, as has previously been found in the literature. However, despite the comparable wind speeds while the storm was in western Pacific and SCS, the SST cooling was much more intense in SCS. The reason was because in SCS, the surface layer was thinner, the vorticity field of the Kuroshio was cyclonic, and moreover a combination of larger Coriolis frequency as the storm moved northward and the typhoon’s slower translational speed produced a stronger resonance between wind and current, resulting in strong shears and entrainment of cool subsurface waters in the upper ocean.     

Evaluation on surface current observing network of high frequency ground wave radars in the Gulf of Thailand

Due to the high cost of ocean observation system, the scientific design of observation network becomes much important. The current network of the high frequency radar system in the Gulf of Thailand has been studied using a three-dimensional coastal ocean model. At first, the observations from current radars have been assimilated into this coastal model and the forecast results have improved due to the data assimilation. But the results also show that further optimization of the observing network is necessary. And then, a series of experiments were carried out to assess the performance of the existing high frequency ground wave radar surface current observation system. The simulated surface current data in three regions were assimilated sequentially using an efficient ensemble Kalman filter data assimilation scheme. The experimental results showed that the coastal surface current observation system plays a positive role in improving the numerical simulation of the currents. Compared with the control experiment without assimilation, the simulation precision of surface and subsurface current had been improved after assimilated the surface currents observed at current networks. However, the improvement for three observing regions was quite different and current observing network in the Gulf of Thailand is not effective and a further optimization is required. Based on these evaluations, a manual scheme has been designed by discarding the redundant and inefficient locations and adding new stations where the performance after data assimilation is still low. For comparison, an objective scheme based on the idea of data assimilation has been obtained. Results show that all the two schemes of observing network perform better than the original network and optimal scheme-based data assimilation is much superior to the manual scheme that based on the evaluation of original observing network in the Gulf of Thailand. The distributions of the optimal network of radars could be a useful guidance for future design of observing system in this region.     

Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

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.     

Assimilation of SLA and SST data into an OGCM for the Indian Ocean

 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     

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

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.     

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.     

Correction of inertial oscillations by assimilation of HF radar data in a model of the Ligurian Sea

This article aims at analyzing if high-frequency radar observations of surface currents allow to improve model forecasts in the Ligurian Sea, where inertial oscillations are a dominant feature. An ensemble of ROMS models covering the Ligurian Sea, and nested in the Mediterranean Forecasting System, is coupled with two WERA high-frequency radars. A sensitivity study allows to determine optimal parameters for the ensemble filter. By assimilating observations in a single point, the obtained correction shows that the forecast error covariance matrix represents the inertial oscillations, as well as large- and meso-scale processes. Furthermore, it is shown that the velocity observations can correct the phase and amplitude of the inertial oscillations. Observations are shown to have a strong effect during approximately half a day, which confirms the importance of using a high temporal observation frequency. In general, data assimilation of HF radar observations leads to a skill score of about 30% for the forecasts of surface velocity.     

Local ensemble assimilation scheme with global constraints and conservation

Ensemble assimilation schemes applied in their original, global formulation respect linear conservation properties if the ensemble perturbations are set up accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is therefore necessary to filter out spurious long-range correlations. The conservation of the global properties will be lost if the assimilation is performed locally, since the conservation requires a coupling between all model grid points which is removed by the localization. The distribution of ocean observations is often highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious adjustment of the non-observed parts via data assimilation and thus to a spurious increase or decrease in long-term simulations of global properties which should be conserved. In this paper, we propose a local assimilation scheme (with different variants and assumptions) which can satisfy global conservation properties. The proposed scheme can also be used for non-local observation operators. Different variants of the proposed scheme are tested in an idealized model and compared to the traditional covariance localization with an ad-hoc step enforcing conservation. It is shown that the inclusion of the conservation property reduces the total RMS error and that the presented stochastic and deterministic schemes avoiding error space rotation provide better results than the traditional covariance localization.     

Estimating the 4DVAR analysis error of GODAE products

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.     

On the assessment of Argo float trajectory assimilation in the Mediterranean Forecasting System

The Mediterranean Forecasting System (MFS) has been operational for a decade, and is continuously providing forecasts and analyses for the region. These forecasts comprise local- and basin-scale information of the environmental state of the sea and can be useful for tracking oil spills and supporting search-and-rescue missions. Data assimilation is a widely used method to improve the forecast skill of operational models and, in this study, the three-dimensional variational (OceanVar) scheme has been extended to include Argo float trajectories, with the objective of constraining and ameliorating the numerical output primarily in terms of the intermediate velocity fields at 350 m depth. When adding new datasets, it is furthermore crucial to ensure that the extended OceanVar scheme does not decrease the performance of the assimilation of other observations, e.g., sea-level anomalies, temperature, and salinity. Numerical experiments were undertaken for a 3-year period (2005–2007), and it was concluded that the Argo float trajectory assimilation improves the quality of the forecasted trajectories with ~15%, thus, increasing the realism of the model. Furthermore, the MFS proved to maintain the forecast quality of the sea-surface height and mass fields after the extended assimilation scheme had been introduced. A comparison between the modeled velocity fields and independent surface drifter observations suggested that assimilating trajectories at intermediate depth could yield improved forecasts of the upper ocean currents.     

Performance of four sea surface temperature assimilation schemes in the South China Sea

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.     

An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction

Cyclogenesis and long-fetched winds along the southeastern coast of South America may lead to floods in populated areas, as the Buenos Aires Province, with important economic and social impacts. A numerical model (SMARA) has already been implemented in the region to forecast storm surges. The propagation time of the surge in such extensive and shallow area allows the detection of anomalies based on observations from several hours up to the order of a day prior to the event. Here, we investigate the impact and potential benefit of storm surge level data assimilation into the SMARA model, with the objective of improving the forecast. In the experiments, the surface wind stress from an ensemble prediction system drives a storm surge model ensemble, based on the operational 2-D depth-averaged SMARA model. A 4-D Local Ensemble Transform Kalman Filter (4D-LETKF) initializes the ensemble in a 6-h cycle, assimilating the very few tide gauge observations available along the northern coast and satellite altimeter data. The sparse coverage of the altimeters is a challenge to data assimilation; however, the 4D-LETKF evolving covariance of the ensemble perturbations provides realistic cross-track analysis increments. Improvements on the forecast ensemble mean show the potential of an effective use of the sparse satellite altimeter and tidal gauges observations in the data assimilation prototype. Furthermore, the effects of the localization scale and of the observational errors of coastal altimetry and tidal gauges in the data assimilation approach are assessed.     

年代际预测和耦合资料同化研究综述

年代际预测是近年来气候变化研究的一个迅速发展的新兴热点领域,其首要步骤是进行初始化,目的是为年代际预测提供包含观测变率信息的初值.发展效果好且省时的初始化方法是年代际预测的重大挑战之一,目前国际上主流的初始化方法是耦合资料同化,即在耦合模式框架下进行同化.在年代际预测时,由于模式偏差和初始化方法性能的限制会产生初始冲击问题.目前国际上的各模式机构普遍对北大西洋、热带东西太平洋和印度洋海表温度的年代际预测水平高,而对全球平均近地面气温和北太平洋海表温度的年代际预测水平相对较差.本文主要从初始化方法和年代际预测这两方面的研究现状进行全面回顾,指出存在的问题并讨论未来的发展趋势和挑战.

     

On the oceanic impact of a data-assimilation method in a coupled ocean-land-atmosphere model

 Based on the Kalman filter theory, a new data-assimilation method has been used to improve the 3-D oceanic temperature field of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled general circulation model. This method is applied to assimilate surface and subsurface temperature of in situ measurements from the Pilot Research Moored Array in the Tropical Atlantic project (PIRATA). The assimilation of the PIRATA data produces an improved representation of the thermal state of the ocean and allows a better estimation of other oceanographic quantities, like meridional heat fluxes and zonal currents. The present paper focuses on the tropical Atlantic and, in particular, it contains new reconstructed temperature profiles. One-month forecast experiments during 1999 were performed and the impact of the assimilation is discussed. Received: 24 April 2001 / Accepted: 8 March 2002     

Newtonian nudging for a Richards equation-based distributed hydrological model

The objective of data assimilation is to provide physically consistent estimates of spatially distributed environmental variables. In this study a relatively simple data assimilation method has been implemented in a relatively complex hydrological model. The data assimilation technique is Newtonian relaxation or nudging, in which model variables are driven towards observations by a forcing term added to the model equations. The forcing term is proportional to the difference between simulation and observation (relaxation component) and contains four-dimensional weighting functions that can incorporate prior knowledge about the spatial and temporal variability and characteristic scales of the state variable(s) being assimilated. The numerical model couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on digital elevation data for surface water dynamics. We describe the implementation of the data assimilation algorithm for the coupled model and report on the numerical and hydrological performance of the resulting assimilation scheme. Nudging is shown to be successful in improving the hydrological simulation results, and it introduces little computational cost, in terms of CPU and other numerical aspects of the model’s behavior, in some cases even improving numerical performance compared to model runs without nudging. We also examine the sensitivity of the model to nudging term parameters including the spatio-temporal influence coefficients in the weighting functions. Overall the nudging algorithm is quite flexible, for instance in dealing with concurrent observation datasets, gridded or scattered data, and different state variables, and the implementation presented here can be readily extended to any of these features not already incorporated. Moreover the nudging code and tests can serve as a basis for implementation of more sophisticated data assimilation techniques in a Richards equation-based hydrological model.     

Using an eddy-tracking algorithm to understand the impact of assimilating altimetry data on the eddy characteristics of the Agulhas system

A complex and highly dynamical ocean region, the Agulhas Current System plays an important role in the transfer of energy, nutrients and organic material from the Indian to the Atlantic Ocean. Its dynamics are not only important locally, but affect the global ocean-atmosphere system. In working towards improved ocean reanalysis and forecasting capabilities, it is important that numerical models simulate mesoscale variability accurately—especially given the scarcity of coherent observational platforms in the region. Data assimilation makes use of scarce observations, a dynamical model and their respective error statistics to estimate a new, improved model state that minimises the distance to the observations whilst preserving dynamical consistency. Qualitatively, it is unclear whether this minimisation directly translates to an improved representation of mesoscale dynamics. In this study, the impact of assimilating along-track sea-level anomaly (SLA) data into a regional Hybrid Coordinate Ocean Model (HYCOM) is investigated with regard to the simulation of mesoscale eddy characteristics. We use an eddy-tracking algorithm and compare the derived eddy characteristics of an assimilated (ASSIM) and an unassimilated (FREE) simulation experiment in HYCOM with gridded satellite altimetry-derived SLA data. Using an eddy tracking algorithm, we are able to quantitatively evaluate whether assimilation updates the model state estimate such that simulated mesoscale eddy characteristics are improved. Additionally, the analysis revealed limitations in the dynamical model and the data assimilation scheme, as well as artefacts introduced from the eddy tracking scheme. With some exceptions, ASSIM yields improvements over FREE in eddy density distribution and dynamics. Notably, it was found that FREE significantly underestimates the number of eddies south of Madagascar compared to gridded altimetry, with only slight improvements introduced through assimilation, highlighting the models’ limitation in sustaining mesoscale activity in this region. Interestingly, it was found that the threshold for the maximum eddy propagation velocity in the eddy detection scheme is often exceeded when data assimilation relocates an eddy, causing the algorithm to interpret the discontinuity as eddy genesis, which directly influences the eddy count, lifetime and propagation velocity, and indirectly influences other metrics such as non-linearity. Finally, the analysis allowed us to separate eddy kinetic energy into contributions from detected mesoscale eddies and meandering currents, revealing that the assimilation of SLA has a greater impact on mesoscale eddies than on meandering currents.     

A three-dimensional variational ocean data assimilation system:Scheme and preliminary results

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.     

Assimilation of sea surface temperature into CECOM by flux correction

Sea surface temperature (SST) from a near real-time data set produced from satellites data has been assimilated into a coupled ice–ocean forecasting model (Canadian East Coast Ocean Model) using an efficient data assimilation method. The method is based on an optimal interpolation scheme by which SST is melded into the model through the adjustment of surface heat flux. The magnitude and space–time variation of the adjustment depend on the depth of heat diffusion into the water column in response to changes in surface flux, the correlation time scale of the data, and model and data errors. The diffusion depth is scaled by the eddy diffusivity for temperature. The ratio of the model and data errors is treated as an adjustable parameter. To evaluate the quality of the assimilation, the results from the model with and without assimilation are compared to independent ship data from the Atlantic Zone Monitoring Program and the World Ocean Circulation Experiment. It is shown that the assimilation has a significant impact on the modeled SST, reducing the root mean square difference (RMSD) between the model SST and the ship SST by 0.63°C or 37%. The RMSD of the assimilated SST is smaller than that of the satellite SST by 0.23°C. This suggests that model simulations or predictions with data assimilation can provide the best estimate of the true SST. A sensitivity study is performed to examine the change of the model RMSD with the adjustable parameter in the assimilation equation. The results show that there is an optimal value of the parameter and the model SST is not very sensitive to the parameter.