GPS/LEO掩星观测的变分同化技术

在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。     

基于Mindlin理论Winkler地基参数的Kalman滤波识别

提出了Winkler地基参数的Kalman滤波识别方法。引入Mindlin理论后,推导了Winkler地基上板的控制微分方程。利用Fourier变换,推求了Winkler地基上简支板的Fourier闭式解。推导了Winkler地基参数的Kalman滤波方程,研究了Winkler地基参数的Kalman滤波识别的具体计算步骤。研究表明,运用Kalman滤波理论进行Winkler地基参数的识别,能有效地估计Winkler地基参数;Winkler地基参数的滤波收敛速度和精度受地基参数初始值选取和位移实测资料的影响,且Kalman滤波理论也可用于其他地基模型地基参数的识别。     

The impact of the M2 internal tide on data-assimilative model estimates of the surface tide

We use a synthetic data experiment to assess the accuracy of ocean tides estimated from satellite altimetry data, with emphasis on the impact of the phase-locked internal tide, which has a surface expression of several centimeters near its sites of genesis. Previous tidal estimates have regarded this signal as a random measurement error; however, it is deterministic and not scale-separated from the barotropic (surface) tide around complex bathymetric features. The synthetic data experiments show that the internal tide has a negligible impact on the barotropic tidal fields inferred under these circumstances, and the barotropic dissipation (a quadratic functional of the tidal fields) is in good agreement with the energetics of the three-dimensional regional primitive equations model which is the source of the synthetic data.     

Monitoring the Kuroshio Extension with Dynamically Constrained Synthesis of the Acoustic Tomography, Satellite Altimeter and in situ Data

A finite-difference quasigeostrophic (QG) model of an open ocean region has been employed to produce a dynamically constrained synthesis of acoustic tomography and satellite altimetry data with in situ observations. The assimilation algorithm is based upon the 4D variational data interpolation scheme controlled by the model's initial and boundary conditions. The data sets analyzed include direct and differential travel times measured at the array of five acoustic transceivers deployed by JAMSTEC in the region of the Kuroshio Extension in 1997, Topex/Poseidon altimetry, CTD soundings, and ADCP velocity profiles. The region monitored is located within the area 27.5°–36.5°N, 143°–155°. The results of assimilation show that mesoscale variability can be effectively reconstructed by five transceivers measuring direct and reciprocal travel times supported by relatively sparse in situ measurements. The misfits between model and data lie within the observational error bars for all the data types used in assimilation. We have compared the results of assimilation with the statistical inversion of travel time data and analyzed energy balances of the optimized model solution. Energy exchange between the depth-averaged and shear components of the observed currents reveals a weak decay of the barotropic mode at the rate of 0.2 ± 0.7⋅10⁻⁵ cm²/s³ due to topographic interaction. Mean currents in the region are unstable with an estimate of the available potential energy flux from the mean current to the eddies of 4.7 ± 2.3⋅10⁻⁵ cm²/s³. Kinetic energy transition has the same sign and is estimated as 2.8 ± 2.5⋅10⁻⁵ cm²/s³. Potential enstrophy is transferred to the mesoscale at a rate of 5.5 ± 2.7⋅10⁻¹⁸ s⁻³. These figures provide observational evidence of the properties of free geostrophic turbulence which were predicted by theory and observed in numerical experiments. This revised version was published online in July 2006 with corrections to the Cover Date.     

Data assimilation for a coastal area morphodynamic model: Morecambe Bay

This paper investigates the use of data assimilation in coastal area morphodynamic modelling using Morecambe Bay as a study site. A simple model of the bay has been enhanced with a data assimilation scheme to better predict large-scale changes in bathymetry observed in the bay over a 3-year period. The 2DH decoupled morphodynamic model developed for the work is described, as is the optimal interpolation scheme used to assimilate waterline observations into the model run. Each waterline was acquired from a SAR satellite image and is essentially a contour of the bathymetry at some level within the inter-tidal zone of the bay. For model parameters calibrated against validation observations, model performance is good, even without data assimilation. However the use of data assimilation successfully compensates for a particular failing of the model, and helps to keep the model bathymetry on track. It also improves the ability of the model to predict future bathymetry. Although the benefits of data assimilation are demonstrated using waterline observations, any observations of morphology could potentially be used. These results suggest that data assimilation should be considered for use in future coastal area morphodynamic models.     

Kuroshio extension variability explored through assimilation of TOPEX/POSEIDON altimeter data into a quasi-geostrophic model

Mesoscale features in the eastward extension of the Kuroshio were investigated using assimilation of TOPEX/POSEIDON (T/P) data into a three-layer quasi-geostrophic model. The T/P data exhibited an elongated state of the southern recirculation gyre in 1993–95 and 1997, between whose two periods the gyre had a contracted state in 1995–96. A few stationary eddies were located in the southern gyre during the contracted state. The baroclinic instability, which was indicated by the phase shift from the uppermost-to the lowest-layer anomalies toward the downstream side, was evident near the Kuroshio Extension (KE) path. Since the instability never appeared in the artificial model without bottom topography, the topographic barrier for the eastward flow in the lowest layer was a necessary condition for the instability. The instability synchronized with the transition in the western region of the KE axis from the elongated to the contracted states. This evolution was interpreted as if the baroclinic instability played some part in the KE states and was a trigger for the transition from the elongated to the contracted states.     

A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes

It is important to estimate hard-to-observe parameters in the ocean interior from easy-to-observe parameters. This study therefore demostrates a reconstruction of observed temperature and salinity profiles of the sea east of Japan (30°≈40°N, 140°≈150°E). The reconstruction was done by estimating suboptimal state from several values of the observed profiles and/or sea surface dynamic height (SDH) calculated from the profiles. The estimation used a variational method with vertical coupled temperature-salinity empirical orthogonal function (EOF) modes. Profiles of temperature and salinity in the subtropical region are effectively reconstructed from in situ temperature profile data, or sea surface temperature (SST) and SDH. For example, the analyzed temperature field from SST and SDH has an accuracy to within 1°C in the subtropical region. Salinity in the sea north of Kuroshio, however, is difficult to estimate because of its complex variability which is less correlated with temperature than in the subtropical region. Sea surface salinity is useful to estimate the subsurface structure. We also show the possibility that the estimation is improved by considering nonlinearity in the equation calculating SDH from temperature and salinity analysis values in order to examine the misfit between analysis and observation. Analysis using TOPEX/POSEIDON altimetry data instead of SDH was also performed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Parameter sensitivity of three Kalman filter schemes for assimilation of water levels in shelf sea models

In applications of data assimilation algorithms, a number of poorly known assimilation parameters usually need to be specified. Hence, the documented success of data assimilation methodologies must rely on a moderate sensitivity to these parameters. This contribution presents a parameter sensitivity study of three well known Kalman filter approaches for the assimilation of water levels in a three dimensional hydrodynamic modelling system. The filters considered are the ensemble Kalman filter (EnKF), the reduced rank square root Kalman filter (RRSQRT) and the steady Kalman filter. A sensitivity analysis of key parameters in the schemes is undertaken for a setup in an idealised bay. The sensitivity of the resulting root mean square error (RMSE) is shown to be low to moderate. Hence the schemes are robust within an acceptable range and their application even with misspecified parameters is to be encouraged in this perspective. However, the predicted uncertainty of the assimilation results are sensitive to the parameters and hence must be applied with care. The sensitivity study further demonstrates the effectiveness of the steady Kalman filter in the given system as well as the great impact of assimilating even very few measurements.     

Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar)

I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect.