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1.
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   

2.
Surface current mapping from HF/VHF coastal radars traditionally requires at least two distant sites. Vector velocities are estimated by combining the radial velocity components measured by the radars. In many circumstances (e.g., failures, interferences, logistics constraints), such a combination is not possible by lack of data from one station. Two methods are evaluated to get information on surface circulation from a single site radar: the Vectorial Reconstruction Method (VRM) for current vector mapping and the Vortex Identification Method (VIM) for detecting eddy-like structures. The VRM assumes a non-divergent horizontal surface current, and the VIM analyzes radial velocities and their radial and orthoradial gradients. These two methods are tested on modeled and measured data sets in the Northwestern Mediterranean Sea where both high-resolution ocean circulation model and radar campaigns are available. The VRM performance is strongly limited by the divergence-free hypothesis which was not satisfied in our real data. The VIM succeeded in detection of vortex in the Gulf of Lions and from an operating single site radar located on the Provence coasts in summer.  相似文献   

3.
High-frequency (HF) surface wave radars provide the unique capability to continuously monitor the coastal environment far beyond the range of conventional microwave radars. Bragg-resonant backscattering by ocean waves with half the electromagnetic radar wavelength allows ocean surface currents to be measured at distances up to 200 km. When a tsunami propagates from the deep ocean to shallow water, a specific ocean current signature is generated throughout the water column. Due to the long range of an HF radar, it is possible to detect this current signature at the shelf edge. When the shelf edge is about 100 km in front of the coastline, the radar can detect the tsunami about 45 min before it hits the coast, leaving enough time to issue an early warning. As up to now no HF radar measurements of an approaching tsunami exist, a simulation study has been done to fix parameters like the required spatial resolution or the maximum coherent integration time allowed. The simulation involves several steps, starting with the Hamburg Shelf Ocean Model (HAMSOM) which is used to estimate the tsunami-induced current velocity at 1 km spatial resolution and 1 s time step. This ocean current signal is then superimposed to modelled and measured HF radar backscatter signals using a new modulation technique. After applying conventional HF radar signal processing techniques, the surface current maps contain the rapidly changing tsunami-induced current features, which can be compared to the HAMSOM data. The specific radial tsunami current signatures can clearly be observed in these maps, if appropriate spatial and temporal resolution is used. Based on the entropy of the ocean current maps, a tsunami detection algorithm is described which can be used to issue an automated tsunami warning message.  相似文献   

4.
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   

5.
In order to predict eutrophication events in coastal areas we tested an assimilation scheme based on sequential data assimilation of SeaWiFS chlorophyll data into a coupled 3D physical–biogeochemical model. The area investigated is a semi-enclosed estuarine system (Gulf of Fos–North-western Mediterranean Sea) closely linked to the Rhone River delta. This system is subjected to episodic eutrophication caused by certain hydrodynamic conditions and intermittent nutrient inputs. The 3D hydrodynamic model Symphonie was coupled to the biogeochemical modelling platform Eco3M. Surface chlorophyll concentrations were derived from SeaWiFS data using the OC5 algorithm and were sequentially assimilated using a singular evolutive extended Kalman filter. Assimilation efficiency was evaluated through an independent in situ data set collected during a field survey that took place in May 2001 (ModelFos cruise). An original approach was used in constructing the state vector and the observation vector. By assimilating pseudo-salinity extracted from the model biogeochemical dynamics in both open sea and plume region were respected. We proved that substantial improvements were made in short-term forecasts by integrating such satellite-estimated chlorophyll maps. We showed that missing freshwater inputs could be corrected to a certain extent by the assimilation process. Simulated concentrations of surface chlorophyll and other basic components of the pelagic ecosystem such as nitrates were improved by assimilating surface chlorophyll maps. Finally we showed the coherent spatial behaviour of the filter over the whole modelled domain.  相似文献   

6.
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   

7.
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   

8.
The rapid expansion of urbanization along the world’s coastal areas requires a more comprehensive and accurate understanding of the coastal ocean. Over the past several decades, numerical ocean circulation models have tried to provide such insight, based on our developing understanding of physical ocean processes. The systematic establishment of coastal ocean observation systems adopting cutting-edge technology, such as high frequency (HF) radar, satellite sensing, and gliders, has put such ocean model predictions to the test, by providing comprehensive observational datasets for the validation of numerical model forecasts. The New York Harbor Observing and Prediction System (NYHOPS) is a comprehensive system for understanding coastal ocean processes on the continental shelf waters of New York and New Jersey. To increase confidence in the system’s ocean circulation predictions in that area, a detailed validation exercise was carried out using HF radar and Lagrangian drifter-derived surface currents from three drifters obtained between March and October 2010. During that period, the root mean square (RMS) differences of both the east–west and north–south currents between NYHOPS and HF radar were approximately 15 cm s?1. Harmonic analysis of NYHOPS and HF radar surface currents shows similar tidal ellipse parameters for the dominant M2 tide, with a mean difference of 2.4 cm s?1 in the semi-major axis and 1.4 cm s?1 in the semi-minor axis and 3° in orientation and 10° in phase. Surface currents derived independently from drifters along their trajectories showed that NYHOPS and HF radar yielded similarly accurate results. RMS errors when compared to currents derived along the trajectory of the three drifters were approximately 10 cm s?1. Overall, the analysis suggests that NYHOPS and HF radar had similar skill in estimating the currents over the continental shelf waters of the Middle Atlantic Bight during this time period. An ensemble-based set of particle tracking simulations using one drifter which was tracked for 11 days showed that the ensemble mean separation generally increases with time in a linear fashion. The separation distance is not dominated by high frequency or short spatial scale wavelengths suggesting that both the NYHOPS and HF radar currents are representing tidal and inertial time scales correctly and resolving some of the smaller scale eddies. The growing ensemble mean separation distance is dominated by errors in the mean flow causing the drifters to slowly diverge from their observed positions. The separation distance for both HF radar and NYHOPS stays below 30 km after 5 days, and the two technologies have similar tracking skill at the 95 % level. For comparison, the ensemble mean distance of a drifter from its initial release location (persistence assumption) is estimated to be greater than 70 km in 5 days.  相似文献   

9.
A high-resolution numerical model system is essential to resolve multi-scale coastal ocean dynamics. So a multi-scale unstructured grid-based finite-volume coastal ocean model (FVCOM) system has been established for the East China Sea and Changjiang Estuary (ECS–CE) with the aim at resolving coastal ocean dynamics and understanding different physical processes. The modeling system consists of a three-domain-nested weather research and forecasting model, FVCOM model with the inclusion of FVCOM surface wave model in order to understand the wave–current interactions. The ECS–CE system contains three different scale models: a shelf-scale model for the East China Sea, an estuarine-scale model for the Changjiang Estuary and adjacent region, and a fine-scale model for the deep waterway regions. These three FVCOM-based models guarantee the conservation of mass and momentum transferring from outer domain to inner domain using the one-way common-grid nesting procedure. The model system has been validated using data from various observation data, including surface wind, tides, currents, salinity, and wave to accurately reveal the multi-scale dynamics of the East China Sea and Changjiang Estuary. This modeling system has been demonstrated via application to the seasonal variations of Changjiang diluted water and the bottom saltwater intrusion in the North Passage, and it shows strong potential for estuarine and coastal ocean dynamics and operational forecasting.  相似文献   

10.
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.  相似文献   

11.
For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.  相似文献   

12.
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   

13.
The greater Agulhas Current is one of the most energetic current systems in the global ocean. It plays a fundamental role in determining the mean state and variability of the regional marine environment, affecting its resources and ecosystem, the regional weather and the global climate on a broad range of temporal and spatial scales. In the absence of a coherent in-situ and satellite-based observing system in the region, modelling and data assimilation techniques play a crucial role in both furthering the quantitative understanding and providing better forecasts of this complicated western boundary current system. In this study, we use a regional implementation of the Hybrid Coordinate Ocean Model and assimilate along-track satellite sea level anomaly (SLA) data using the Ensemble Optimal Interpolation (EnOI) data assimilation scheme. This study lays the foundation towards the development of a regional prediction system for the greater Agulhas Current system. Comparisons to independent in-situ drifter observations show that data assimilation reduces the error compared to a free model run over a 2-year period. Mesoscale features are placed in more consistent agreement with the drifter trajectories and surface velocity errors are reduced. While the model-based forecasts of surface velocities are not as accurate as persistence forecasts derived from satellite altimeter observations, the error calculated from the drifter measurements for eddy kinetic energy is significantly lower in the assimilation system compared to the persistence forecast. While the assimilation of along-track SLA data introduces a small bias in sea surface temperatures, the representation of water mass properties and deep current velocities in the Agulhas system is improved.  相似文献   

14.
基于正则化方法的高频地波雷达海浪方向谱反演   总被引：1，自引：0，他引：1       下载免费PDF全文
提出了一种从高频地波雷达海面回波谱中提取海浪方向谱的新方法:基于高频电波海洋探测基本原理将线性化后的非线性积分方程离散为矩阵方程组,通过引入正则化数学模型将不适定方程转化为正规方程,然后采用奇异值分解法求解.对于正则化方法中正则化参数,采用L曲线法来确定.为了验证本文算法的有效性,分别在单部雷达和双部雷达探测下进行了不同噪声水平下的数值模拟,结果表明了该正则化反演方法的有效性.文中还给出了实测数据分析初步结果.该算法的工程性应用还需进一步研究.对于实际的地波雷达海浪反演具有良好的应用前景.  相似文献   

15.
The hydrodynamics of coastal areas is characterized by the interaction among phenomena occurring at different spatial and temporal scales, such as the interaction of a large-scale ocean current with the local bathymetry and coastline, and local forcing conditions. In order to take into account all relevant phenomena, the study of the hydrodynamics of coastal zones requires a high-spatial and temporal resolution for both observations and simulation of local currents. This resolution can be obtained by using X-band radar, which allows simultaneous measurement of waves and currents in a range of 1–3 miles from the coastline, as well as high-resolution numerical models implemented in the area and configured through multiple nesting techniques in order to reach resolutions comparable to such coastal observations. Such an integrated monitoring system was implemented at the Isola del Giglio in 2012, after the accident of the Costa Concordia ship. Results can be used as a cross-validation of data produced independently by radar observations and numerical models. In addition, results give some important insights on the dynamics of the coastal boundary layer, both for what concerns the attenuation in the profile of the depth-averaged velocities which typically occur in turbulent boundary layers, as well as for the production, detachment and evolution of vorticity produced by the interaction of large-scale ocean currents with the coastline and the subsequent time evolution of such boundary layer. This transition between large-scale regional currents and the coastal boundary layer is often neglected in regional forecasting systems, but it has an important role in the ocean turbulence processes.  相似文献   

16.
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   

17.
In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.  相似文献   

18.
海洋表面流的高频雷达遥感   总被引：9，自引：2，他引：9       下载免费PDF全文
解释了利用高频雷达探测海洋表面流的基本原理，简要介绍了新研制的海态监测分析雷达OSMAR的组成及其工作流程，着重分析了利用OSMAR探测北部湾海流的实验结果．  相似文献   

19.
A three-dimensional primitive-equation model is used to simulate the Long Island Sound (LIS) outflow for a 1-year (2001) period. The model domain includes LIS and New York Bight (NYB). Tidal and wind forcing are included, and seasonal salinity and temperature variations are assimilated. The model results are validated with the HF radar, moored acoustic Doppler current profiler (ADCP), and ferry-based ADCP observations. The agreement between simulated and observed flow patterns generally is very good. The difference in seasonal mean currents between the model and moored ADCP is about 0.01 m/s; the correlation of dominant velocity fluctuations between the model and HF radar is 0.83; and the difference in mean LIS transport between the model and shipboard ADCP is about 5%. However, the model predicts a prominent tidally generated headland eddy not supported by the HF radar observation. The model sensitivity study indicates that the tides, winds, and ambient coastal front all have important impact on the buoyant outflow. The tides and winds cause stronger vertical mixing, which reduces the surface plume strength. The ambient coastal front, on the other hand, tends to enhance the plume.  相似文献   

20.
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.  相似文献   

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