共查询到20条相似文献，搜索用时 31 毫秒
1.
The objective of this paper was to explore the potentialities of sequential statistical estimation methods to assimilate ocean color observations in a primary production model coupled to a 3D hydrodynamic model. The study site was the gulf of Fos—Rhone delta region on the French Mediterranean coast. The high rate of primary production generally observed in this area is mainly due to strong nutrient inputs of the Rhone River. The assimilation method is derived from the singular evolutive extended Kalman filter (SEEK), which uses an error subspace represented by multivariate empirical orthogonal functions (EOF). SeaWiFS chlorophyll data were assimilated by the ecosystem model during a simulation performed under realistic meteorological conditions for the year 2001. An ‘adaptive’ computing method of the EOF was applied in order to lower the instabilities of the filter. Data assimilation system permitted to reduce the mean absolute error between model and data from 1.51 to 0.77 mg m−3 thanks to the SEEK filter, showing a substantial 49% gain. Efficiency of the SEEK filter was then investigated considering several areas of interest inside the modelled domain. Finally, impact of the assimilation scheme on non-observed variables was illustrated and discussed. Throughout this experimentation the data assimilation system showed its potential regarding operational systems. 相似文献
2.
This paper reviews the developments of the singular evolutive extended Kalman (SEEK) filter method used for data assimilation
in oceanography, since the original paper by Pham et al. (J Mar Syst 16:3–4, 323–340 1998a). First, a short review is presented of the context of data assimilation in oceanography and of the variety of numerical
ocean codes and configurations in which the SEEK filter has been implemented using different data sets for assimilation. Then,
the paper provides an exposition of the different versions of the SEEK filter developed during the past 10 years and discusses
their relative merits for scientific or operational implementations. A classification of the algorithmic variants is proposed,
and several possible improvements of the generic methodology are mentioned in the perspective of new assimilation challenges. 相似文献
3.
Traditional variational data assimilation (VDA) with only one regularization parameter constraint cannot produce optimal error tuning for all observations. In this paper, a new data assimilation method of “four dimensional variational data assimilation (4D-Var) with multiple regularization parameters as a weak constraint (Tikh-4D-Var)” is proposed by imposing different regularization parameters for different observations. Meanwhile, a new multiple regularization parameters selection method, which is suitable for actual high-dimensional data assimilation system, is proposed based on the posterior information of 4D-Var system. Compared with the traditional single regularization parameter selection method, computation of the proposed multiple regularization parameters selection method is smaller. Based on WRF3.3.1 4D-Var data assimilation system, initialization and simulation of typhoon Chaba (2010) with the new Tikh-4D-Var method are compared with its counterpart 4D-Var to demonstrate the effectiveness of the new method. Results show that the new Tikh-4D-Var method can accelerate the convergence with less iterations. Moreover, compared with 4D-Var method, the typhoon track, intensity (including center surface pressure and maximum wind speed) and structure prediction are obviously improved with Tikh-4D-Var method for 72-h prediction. In addition, the accuracy of the observation error variances can be reflected by the multiple regularization parameters. 相似文献
4.
The singular evolutive extended Kalman (SEEK) filter has been proposed recently by Pham et al. (1997) for data assimilation into numerical oceanic models. This filter has been applied in different realistic ocean frameworks and has provided satisfactory results ( Pham et al., 1997; Verron et al., 1998). However, the SEEK filter remains expensive in real operational assimilation. To reduce cost and obtain a better representativity, we introduce the idea ‘local correction basis'. Such basis however cannot be made to evolve according to the model without destroying its locality property. Therefore we shall keep this basis fixed and we augment it by a few global basis vectors which evolve. The resulting semi-evolutive partially local filter is much less costly to implement than the SEEK filter and yet can yield better results. In the first application, validation twin experiments are conducted in a realistic setting of the OPA model over the tropical Pacific Ocean. 相似文献
5.
Nedjeljka ?agar 《Pure and Applied Geophysics》2012,169(3):367-379
This paper reports recent advances in understanding of dynamical aspects of the tropical data assimilation. In contrast with
the mid-latitudes, there is no a well-defined approach for the tropical data assimilation in numerical weather prediction
(NWP) community which has traditionally been concentrated on the mid-latitude analysis problem. In particular, the impact
of the equatorial Rossby, inertio-gravity, and mixed Rossby-gravity waves on the tropical forecast-error covariances is difficult
to quantify. Various tropical waves are characterized by different couplings between the mass field and the wind field. The
average mixture of these waves, built into the background-error covariance matrix for data assimilation provides analysis
increments which appear nearly univariate even though they result from the advanced multivariate assimilation methodology.
This applies to both dry and moist idealized tropical systems as well as to a 4D-Var NWP assimilation system. 相似文献
6.
Variational data assimilation for parameter estimation: application to a simple morphodynamic model 总被引：2，自引：1，他引：1
Polly J. Smith Sarah L. Dance Michael J. Baines Nancy K. Nichols Tania R. Scott 《Ocean Dynamics》2009,59(5):697-708
Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce
state and parameter estimates that most accurately approximate the current and future states of the true system. The technique
is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce
more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe
how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved
using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var,
Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var
scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation
is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves
the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more
complex morphodynamic models. 相似文献
7.
Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational
data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation
Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts
of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance
has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance)
as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July
2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in
comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the
assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control
run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible
for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis
indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded
only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature
and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B
and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture,
due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model. 相似文献
8.
Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the joint approach. Furthermore, the dual estimation is proven to be very effective computationally, recovering accurate estimates at a reasonable cost. 相似文献
9.
Frédéric Castruccio Jacques Verron Lionel Gourdeau Jean-Michael Brankart Pierre Brasseur 《Ocean Dynamics》2008,58(1):43-63
The altimetric satellite signal is the sum of the geoid and the dynamic topography, but only the latter is relevant to oceanographic
applications. Poor knowledge of the geoid has prevented oceanographers from fully exploiting altimetric measurements through
its absolute component, and applications have concentrated on ocean variability through analyses of sea level anomalies. Recent
geodetic missions like CHAMP, GRACE and the forthcoming GOCE are changing this perspective. In this study, data assimilation
is used to reconstruct the Tropical Pacific Ocean circulation during the 1993–1996 period. Multivariate observations are assimilated
into a primitive equation ocean model (OPA) using a reduced order Kalman filter (the Singular Evolutive Extended Kalman filter).
A 6-year (1993–1998) hindcast experiment is analyzed and validated by comparison with observations. In this experiment, the
new capability offered by an observed absolute dynamic topography (built using the GRACE geoid to reference the altimetric
data) is used to assimilate, in an efficient way, the in-situ temperature profiles from the TAO/TRITON moorings together with
the T/P and ERS1&2 altimetric signal. GRACE data improves compatibility between both observation data sets. The difficulties
encountered in this regard in previous studies such as Parent et al. (J Mar Syst 40–41:381–401, 2003) are now circumvented. This improvement helps provide more efficient data assimilation, as evidenced, by assessing the results
against independent data. This leads in particular to significantly more realistic currents and vertical thermal structures. 相似文献
10.
Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model 总被引：1，自引：0，他引：1
This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. 相似文献
11.
This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed. 相似文献
12.
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. 相似文献
13.
由于GRACE卫星数据解算的时变重力场模型中高阶位系数存在误差,这些误差在重力异常图中表现为南北向的条带噪声,在应用GRACE时变重力场数据时必须进行滤波.本文在空间域引入了一种有效消除GRACE时变重力场条带噪声的平滑先验信息方法,并将其与目前常用的高斯滤波和去相关误差等滤波方法分别应用于合成的质量变化趋势数字模型,检测不同滤波方法消除条带噪声的能力及其对真实信号的影响.滤波结果显示,与目前常用的高斯滤波和去相关误差滤波器相比,本文滤波方法在有效移除条带噪声的同时,具有有效信号幅度衰减小、有效信号形变小以及保存了更多的短波长细节信息等优势;此外,统计结果显示,本文滤波结果在信号最大值、最小值以及残差均方根等方面均与模拟真实信号最为接近.相比300km高斯平滑和组合滤波结果,有效信号振幅的极小值和极大值分别提高了约18%和6%,残差均方根分别降低了25%和33%.说明本文滤波方法移除GRACE相关误差的同时,在保留有效信号方面具有明显的优势. 相似文献
14.
Clément Fontana Christian Grenz Christel Pinazo Patrick Marsaleix Frédéric Diaz 《Continental Shelf Research》2009,29(11-12):1397-1409
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
15.
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
16.
Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data 总被引：1，自引：1，他引：0
Juxiu Tong Bill X. Hu Jinzhong Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1211-1223
A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient
pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity
and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess
the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from
observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly
improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the
hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation
steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity
field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or
larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic
conductivity field calibration. 相似文献
17.
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
18.
《中国科学:地球科学(英文版)》2017,(5)
Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem. 相似文献
19.
TingLong Zhang Rui Sun ChangHui Peng GuoYi Zhou ChunLing Wang QiuAn Zhu YanZheng Yang 《中国科学:地球科学(英文版)》2016,59(2):337-348
Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach. 相似文献
20.