Data assimilation with the EnKF in a 1-D numerical model of a North Sea station

A series of numerical experiments for data assimilation with the Ensemble Kalman Filter (EnKF) in a shallow water model are reported. Temperature profiles measured at a North Sea location, 55°30ˊ North and 0°55ˊ East (referred to as the CS station of the NERC North Sea project), are assimilated in 1-D simulations. Comparison of simulations without assimilation to model results obtained when assimilating data with the EnKF allows us to assess the filter performance in reproducing features of the observations not accounted for by the model. The quality of the model error sampling is tested as well as the validity of the Gaussian hypothesis underlying the analysis scheme of the EnKF. The influence of the model error parameters and the frequency of the data assimilation are investigated and discussed. From these experiments, a set of optimal parameters for the model error sampling are deduced and used to test the behavior of the EnKF when propagating surface information into the water column.     

Computing near coastal tidal dynamics from observations and a numerical model

A two-dimensional vertically integrated model of the North Sea is used to compute the distribution of M₂ and M₄ tidal elevations and currents over the region. Comparison of computed and observed elevations and currents in the area shows that the model can accurately reproduce the M₂ tide in the North Sea, although there are difficulties with the M₄ tide particularly in the northern North Sea.Comparison between model and a large number of observations collected in a shallow water region off the east coast of England, revealed that the model can accurately reproduce the tides even in near coastal regions, where model resolution problems can occur. Comparisons of computed and observed M₂ tidal energy fluxes in this region, show that model and observations agree to within the order of 10% (the error associated with the necessary interpolation of the observations in order to compute the energy flux).The problem of computing energy dissipation in the area by subtracting the energy fluxes into and out of the region is shown to be ill-conditioned in that the energy dissipation in the area is comparable to the error in the energy flux. Consequently for the sea region considered here it is not meaningful to compare this energy budget with energy dissipation due to bottom friction.Energy dissipation for the whole of the North Sea is computed using the numerical model and the geographical distribution of dissipation due to bottom friction is given for the M₂ tide.     

On the influence of adequate Weddell Sea characteristics in a large-scale global ocean circulation model

Global ocean circulation models usually lack an adequate consideration of high-latitude processes due to a limited model domain or insufficient resolution. Without the processes in key areas of the global thermohaline circulation, the characteristics and flow of deep and bottom waters cannot be modeled realistically. In this study, a high-resolution (~20 km) ocean model focused on the Weddell Sea sector of the Southern Ocean is combined with a low-resolution (2^° × 2^°) global ocean model applying the state estimation technique. Temperature, salinity, and velocity data on two Weddell Sea sections from the regional model are used as constraints for the large-scale model in addition to satellite altimetry and sea-surface temperatures. The differences between the model with additional constraints and without document that the Weddell Sea circulation exerts significant influence on the course of the Antarctic Circumpolar Current with consequences for Southern Ocean water mass characteristics and the spreading of deep and bottom waters in the South Atlantic. Furthermore, a warming trend in the period 1993–2001 was found in the Weddell Sea and adjacent basins in agreement with float measurements in the upper Southern Ocean. Teleconnections to the North Atlantic are suggested but need further studies to demonstrate their statistical significance.     

Verification of Parameters of Major Historical Earthquakes in the North Yellow Sea

Based on existing historical data and observations from the Dalian seismic station,this paper rechecks the parameters of the three historical earthquakes of 1916,1917 and 1944 in the North Yellow Sea,near the Yalu River region.The results show that their relocated epicenters are located at contemporarily intense seismic active zones,consistent with the results derived from the data of the Dalian seismic station.The results are valuable in both revising earthquake catalogues and understanding the local background seismicity.     

联合卫星测高和模式资料研究海水热含量变化

本文在考虑洋底压力变化的情况下,利用2003~2008年融合多颗卫星的测高资料估计了全球和中国近海的海水热含量变化.顾及洋底压力(OBP)变化以及热膨胀系数随海水深度变化的影响,提出的改进方法提高了对中高纬度地区热含量变化的估算精度.在OBP变化较为明显的北太平洋区域I(30°N~50°N,170°E~190°E)、南印度洋区域II(40°S~60°S,100°E~120°E)和南太平洋区域III(40°S~60°S,100°W~120°W),改进方法的均方差较传统方法分别降低了16.3%、60.5%和48.4%.同时研究表明,卫星测高的精度以及盐度变化是影响中高纬度地区热含量估计精度的重要因素.在中国近海地区,东海和黄海的热含量主要表现为周年变化;南海区域的热含量除周年变化外,还存在半周年项和年际变化项,且南海的海水热含量近年有增加的趋势.     

Investigating the effects of a summer storm on the North Sea stratification using a regional coupled ocean-atmosphere model

The influence of a summer storm event in 2007 on the North Sea and its effects on the ocean stratification are investigated using a regional coupled ocean (Regional Ocean Modeling System, ROMS)-atmosphere (Weather Research & Forecasting model, WRF) modeling system. An analysis of potential energy anomaly (PEA, Φ) and its temporal development reveals that the loss of stratification due to the storm event is dominated by vertical mixing in almost the entire North Sea. For specific regions, however, a considerable contribution of depth-mean straining is observed. Vertical mixing is highly correlated with wind induced surface stresses. However, peak mixing values are observed in combination with incoming flood currents. Depending on the phase between winds and tides, the loss of stratification differs strongly over the North Sea. To study the effects of interactive ocean-atmosphere exchange, a fully coupled simulation is compared with two uncoupled ones for the same vertical mixing parameters to identify the impact of spatial resolution as well as of SST feedback. While the resulting new mixed layer depth after the storm event in the uncoupled simulation with lower spatial and temporal resolution of the surface forcing data can still be located in the euphotic zone, the coupled simulation is capable to mix the entire water column and the vertical mixing in the uncoupled simulation with higher resolution of the surface forcing data is strongly amplified. These differences might have notable implications for ecosystem modeling since it could determine the development of new phytoplankton blooms after the storm and for sediment modeling in terms of sediment mobilization. An investigation of restratification after the extreme event illustrates the persistent effect of this summer storm.     

Simulating the temporal and spatial dynamics of the North Sea using the new model GETM (general estuarine transport model)

Here we present results of a 1-year realistic North Sea simulation from the new model GETM (general estuarine transport model) and assess the capabilities of this model by comparing them to model results from the well-known HAMSOM (Hamburg shelf sea and ocean model) model, in situ data from the North Sea project and satellite-derived sea-surface temperature data. The annual cycle and the spatial variability of stratification and mixing in the North Sea is simulated. It is shown that the new model is successful in reproducing the general temporal and spatial dynamics of the North Sea. The major advantages of GETM for achieving improved results in this simulation are the implementation of general vertical coordinates, of a state-of-the-art turbulence model and of higher-order advection schemes. By exploiting the full capabilities of these features a more realistic simulation could be achieved. We found that the greatest differences in the model results are produced by applying advection schemes of different complexity. Here we are able to demonstrate that better advection schemes lead to stronger horizontal gradients and stronger vertical stratification during summer. When comparing these results to measurements from the North Sea project and to satellite data, we find that these stronger gradients are more realistic. Therefore, we consider it as essential to use such high-order advection schemes if the spatial variability of estuarine or shelf seas like the North Sea is to be resolved adequately. The advanced turbulence closure scheme also contributed to more realistic simulation of the vertical stratification. Finally, general vertical coordinates better resolve the shallow regions, but are also useful for the deeper regions, as they allow a better estimation of sea-surface temperature compared to traditional coordinates.Responsible Editor: Phil Dyke     

Addressing snow model uncertainty for hydrologic prediction

Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction.     

东海及琉球沟弧盆系的海底热流测量与热流分布

利用钻井资料获得了东海陆架地区15个热流值，分析了海底热流测量的误差来源及数据精度，对东海及琉球沟弧盆系的热流值进行了分类整理，将海底热流分为可信热流值、较可信热流值和参考热流值，本文使用了前两类热流数据，共得305个，对研究区的热流站位进行了分析，发现热流测站分布很不均匀，冲绳海槽几个高热流异常区的热流测站总数占全部东海热流测站的一半多，仍有相当一些构造单元热流站住很少或者几乎没有热流值，总体上，研究区的热流分布明显地和沟弧盆系的构造特征相吻合，呈现北东向条带状分布，东海陆架为正常热流值区，冲绳海槽为高热流异常区，琉球群岛为正常热流值区，琉球海沟和菲律宾海都为低热流异常值区，根据热流推测冲绳海槽下存在深部热物质上拱，琉球海沟垭口之下存在海洋板块的俯冲，本研究区自东向西初步表现出长波长热流振荡现象，但由于测量数据稀少，目前还不能对此作更进一步的研究。     

基于置信区间的地震压埋人员实时伤亡状态预估模型

在震后应急救援宏观决策中,地震压埋人员的实时伤亡状态是最重要的定量指标和参考依据。因此,以置信度区间为工具,对影响伤亡状态的参数即初始伤亡指数Co、囤陷环境S和体质综合指数B进行了区间分析,建立了实时伤亡状态模型,并以汶川地震灾区为例,对不同人员的伤亡状态进行了预估,预估值与实际情况的误差为10%左右,表明该模型可供震后应急救援工作参考。     

Multivariate contemporaneous ARMA model with hydrological applications

In order to allow contemporaneous autoregressive moving average (CARMA) models to be properly applied to hydrological time series, important statistical properties of the CARMA family of models are developed. For calibrating the model parameters, efficient joint estimation procedures are investigated and compared to a set of uivariate estimation procedures. It is shown that joint estimation procedures improve the efficiency of the autoregressive and moving average parameter estimates, but no improvements are expected on the estimation of the mean vector and the variance covariance matrix of the model. The effects of the different estimation procedures on the asymptotic prediction error are also considered. Finally, hydrological applications demonstrate the usefulness of the CARMA models in the field of water resources.     

BARENTS16: a 1-D velocity model for the western Barents Sea

A minimum 1-D seismic velocity model for routine seismic event location purposes was determined for the area of the western Barents Sea, using a modified version of the VELEST code. The resulting model, BARENTS16, and corresponding station corrections were produced using data from stations at regional distances, the vast majority located in the periphery of the recorded seismic activity, due to the unfavorable land–sea distribution. Recorded seismicity is approached through the listings of a joint bulletin, resulting from the merging of several international and regional bulletins for the region, as well as additional parametric data from temporary deployments. We discuss the challenges posed by this extreme network-seismicity geometry in terms of velocity estimation resolution and result stability. Although the conditions do not facilitate the estimation of meaningful station corrections at the farthermost stations, and even well-resolved corrections do not have a convincing contribution, we show that the process can still converge to a stable velocity average for the crust and upper mantle, in good agreement with a priori information about the regional structure and geology, which reduces adequately errors in event location estimates.     

Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic

The estimation of the monthly mean flow is a critical issue in many water resource development projects. However, in practice the mean flow is not easily determined in ungauged and poorly gauged basins. Therefore, in the literature, various flow estimation methods have been developed recently for mountainous regions which are generally ungauged or poorly gauged basins. In this study a fuzzy logic model based on the Mamdani approach was developed to estimate the flow for poorly gauged mountainous basins. This model was applied to the Solakli Basin which is located in the Eastern Black Sea Region of Turkey. Limited rainfall and flow data are available for this basin. In addition to these variables, the stream and time coefficients were introduced and used as variables for modeling. The data was divided into training and testing phases. The model results were compared with the measured data. The comparison depends on seven statistical characteristics, four different error modes and the contour map method. It was observed that the fuzzy model developed in this study yielded reliable results.     

A scheme for regional rice yield estimation using ENVISAT ASAR data

Information on rice growing areas and rice production is critical for most rice growing countries to make state and economic policies. However, the areas where rice crop is cultivated are often cloudy and rainy, which entails the use of radar remote sensing data for rice monitoring. In this paper, a practical scheme to integrate multi-temporal and multi-polarization ENVISAT ASAR data into rice crop model for regional rice yield estimation has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the observed multi-temporal rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000 to predict rice yield. The assimilation method re-initializes the model with optimal input parameters, allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e., the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced. The scheme was validated over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% on average and with a root mean square error of approximately 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation. Supported by the ESA-NRSCC Dragon Cooperation Program (), the Project for Jiangsu Graduate in Scientific Research and Innovation (No. CX07B_048z), and the Special Program for Scientific Research in Public Welfare Meteorological Services (No. GYHY200806008)     

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.     

Joint multivariate statistical model and its applications to the synthetic earthquake prediction

Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sample variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to characterize and predict earthquakes in North China (30°～42°N, 108°～125°E) and better prediction results are obtained.     

A hydrographic time series station in the Wadden Sea (southern North Sea)

In the tidal inlet between the East Frisian islands of Langeoog and Spiekeroog, southern North Sea, a time-series station was set up in autumn 2002 as part of the research programme BioGeoChemistry of Tidal Flats run by the University of Oldenburg. The purpose of the station is to provide continuous data on physical, biological and chemical parameters. In addition to instruments recording basic hydrographic and meteorological parameters, the time-series station is equipped with acoustic Doppler profilers for measuring surface waves and current profiles. Compact optical spectrometers are being used for spectral measurements of seawater transmission and for daylight reflectance. Additional sensors were installed for measuring oxygen, nutrients and methane in the seawater. The data shall help to quantify the flux of dissolved and suspended matter between the backbarrier tidal flat and the open sea and to characterise the material transformation in the tidal flat area by biogeochemical processes over the tidal cycle. Due to its novel design, operation of the station is also possible during winter and under extreme weather conditions (gales, storm surges, and sea ice) when data sampling with conventional platforms such as research vessels, buoys, or smaller poles could not be performed in the past. In this way, time series of data are obtained, which include events that are most relevant to the evolution of this coastal area. The performance of the station and its equipment are presented with data covering 6 years of operation. Time series of air and water temperature as well as seawater salinity demonstrate the multiyear dynamics of these parameters in the East Frisian Wadden Sea. Hydrographic data collected under specific meteorological conditions such as gales and storm surges exemplify the all-weather capabilities of the station and its value for studying hydrographic processes in the Wadden Sea.     

Synergy of satellite remote sensing and numerical modeling for monitoring of suspended particulate matter

Monitoring and modeling of the distribution of suspended particulate matter (SPM) is an important task, especially in coastal environments. Several SPM models have been developed for the North Sea. However, due to waves in shallow water and strong tidal currents in the southern part of the North Sea, this is still a challenging task. In general there is a lack of measurements to determine initial distributions of SPM in the bottom sediment and essential model parameters, e.g., appropriate exchange coefficients. In many satellite-borne ocean color images of the North Sea a plume is visible, which is caused by the scattering of light at SPM in the upper ocean layer. The intensity and length of the plume depends on the wave and current climate. It is well known that the SPM plume is especially obvious shortly after strong storm events. In this paper a quasi-3-D and a 3-D SPM transport model are presented. Utilizing the synergy of satellite-borne ocean color data with numerical models, the vertical exchange coefficients due to currents and waves are derived. This results in models that for the first time are able to reproduce the temporal and spatial evolution of the plume intensity. The SPM models consist of several modules to compute ocean dynamics, the vertical and horizontal exchange of SPM in the water column, and exchange processes with the seabed such as erosion, sedimentation, and resuspension. In the bottom layer, bioturbation via benthos and diffusion processes is taken into account.Responsible Editor: Jörg-Olaf Wolff     

A mathematical model to predict the inelastic response of a steel frame: Formulation of the model

The purpose of this research is to use data from experiments to formulate a mathematical model that will predict the non-linear response of a single-storey steel frame to an earthquake input. The process used in this formulation is system identification. The form of the model is a second-order non-linear differential equation with linear viscous damping and Ramberg—Osgood type hysteresis. The damping coefficient and the three parameters in the hysteretic model are to be established. An integral weighted mean squared error function is used to evaluate the [goodness of fit] between the model's response and the structure's response when both are subjected to the same excitation. The function includes errors in displacement and acceleration and is integrated from zero to a time T, which may be the full duration of the recorded response or only a portion of it. The parameters are adjusted using a modified Gauss-Newton method until the error function is minimized. The computer program incorporating these steps in the system identification process is verified with simulated data. Results given in the paper show that in every case the program converges in few iterations to the assigned set of parameters.     

Einfluss korrelativer Abhängigkeiten auf simulierte Zeitreihen: Empfindlichkeit des Fiering-Modells gegenüber Veränderungen der Korrelationsstruktur

The simulation of time series is based on estimated statistical parameters of the empirical time series. The Fiering Model generating monthly sums of streamflows is used as an example for the simulation in order to account for the error of the model, theoretically and practically, caused by statistically inprecise parameter estimation. The sensitivity of this model, especially to the correlation coefficients, is analyzed by means of systematic variations of the correlation coefficients, since these are most affected by the error of estimation. No significant dependency could be found comparing the empirical and simulated parameters mean, standard deviation, and skewness. From this follows, that the importance of the correlation coefficients in the Fiering Model is generally overestimated. The results are given for monthly sums of streamflows at four stations with different hydrological characteristics.