Bayesian kriging—Merging observations and qualified guesses in kriging

Frequently a user wants to merge general knowledge of the regionalized variable under study with available observations. Introduction of fake observations is the usual way of doing this. Bayesian kriging allows the user to specify a qualified guess, associated with uncertainty, for the expected surface. The method will provide predictions which are based on both observations and this qualified guess.     

An Initial Guess for the Levenberg–Marquardt Algorithm for Conditioning a Stochastic Channel to Pressure Data

A standard procedure for conditioning a stochastic channel to well-test pressure data requires the minimization of an objective function. The Levenberg–Marquardt algorithm is a natural choice for minimization, but may suffer from slow convergence or converge to a local minimum which gives an unacceptable match of observed pressure data if a poor initial guess is used. In this work, we present a procedure to generate a good initial guess when the Levenberg–Marquardt algorithm is used to condition a stochastic channel to pressure data and well observations of channel facies, channel thickness, and channel top depth. This technique yields improved computational efficiency when the Levenberg–Marquardt method is used as the optimization procedure for generating realizations of the model by the randomized maximum likelihood method.     

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude. Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies. With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.     

A stochastic collocation based Kalman filter for data assimilation

In this paper, a stochastic collocation-based Kalman filter (SCKF) is developed to estimate the hydraulic conductivity from direct and indirect measurements. It combines the advantages of the ensemble Kalman filter (EnKF) for dynamic data assimilation and the polynomial chaos expansion (PCE) for efficient uncertainty quantification. In this approach, the random log hydraulic conductivity field is first parameterized by the Karhunen–Loeve (KL) expansion and the hydraulic pressure is expressed by the PCE. The coefficients of PCE are solved with a collocation technique. Realizations are constructed by choosing collocation point sets in the random space. The stochastic collocation method is non-intrusive in that such realizations are solved forward in time via an existing deterministic solver independently as in the Monte Carlo method. The needed entries of the state covariance matrix are approximated with the coefficients of PCE, which can be recovered from the collocation results. The system states are updated by updating the PCE coefficients. A 2D heterogeneous flow example is used to demonstrate the applicability of the SCKF with respect to different factors, such as initial guess, variance, correlation length, and the number of observations. The results are compared with those from the EnKF method. It is shown that the SCKF is computationally more efficient than the EnKF under certain conditions. Each approach has its own advantages and limitations. The performance of the SCKF decreases with larger variance, smaller correlation ratio, and fewer observations. Hence, the choice between the two methods is problem dependent. As a non-intrusive method, the SCKF can be easily extended to multiphase flow problems.     

Technology and Its Application： MIS with Geographical Information

Despite the constant expansion of the application of GIS, in recent years, the data process-ing ability of the application system developed with GIS_Tool is quite limited. Management information system with the geographical information can not only be used to deal with data commonly, but also deal with digitized maps as GIS_Tool does. Thus it can help the user develop an application system high-qual-ified both in maps and traditional data. This paper, by taking the method of integrated MapInfo in the application of Visual Basic as an example, mainly illustrates two technological problenm-integrated map technology and the multiuserization of Maplnfo geographical information. And also the concrete solu-tions to the problems are provided.     

A comparison between a Monte Carlo implementation of retrospective optimal interpolation and an ensemble Kalman filter in nonlinear dynamics

To more correctly estimate the error covariance of an evolved state of a nonlinear dynamical system, the second and higher-order moments of the prior error need to be known. Retrospective optimal interpolation (ROI) may require relatively less information on the higher-order moments of the prior errors than an ensemble Kalman filter (EnKF) because it uses the initial conditions as the background states instead of forecasts. Analogous to the extension of a Kalman filter into an EnKF, an ensemble retrospective optimal interpolation (EnROI) technique was derived using the Monte Carlo method from ROI. In contrast to the deterministic version of ROI, the background error covariance is represented by a background ensemble in EnROI. By sequentially applying EnROI to a moving limited analysis window and exploiting the forecast from the average of the background ensemble of EnROI as a guess field, the computation costs for EnROI can be reduced. In the numerical experiment using a Lorenz-96 model and a Model-III of Lorenz with a perfect-model assumption, the cost-effectiveness of the suboptimal version of EnROI is demonstrated to be superior to that of EnKF using perturbed observations.     

Opportunities and challenges of establishing coastal observing systems

Some of the challenges to establishing and sustaining environmental monitoring are potentially overcome under the framework of global observing systems. Observing systems go beyond monitoring by enabling links between user needs and observations and by providing valued information products to user groups at appropriate spatial and temporal scales. The United Nations established three global observing systems; for climate, oceans, and land and freshwater. Initiatives have also begun to address important issues within coastal ecosystems. Recent socio-political awareness and technical advances have imporoved the opportunities for establishing these observing systems and ensuring their sustainability. Awareness and current technology alone are not enough, and ongoing implementation of these systems is still stymied by a variety of factors. We make several recommendations to promote their success now and in the future.     

澳大利亚南极科学数据管理综述

系统概述了澳大利亚国家南极数据政策、组织机构、管理模式、实施策略与未来发展所面临的问题。在此基础上分析澳大利亚南极数据工作的发展与实效,并回顾了中国的极地数据管理工作,初步表明建立一个有效的组织框架是顺利开展数据管理与共享工作的基础,完全基于Web的数据管理与服务对科学项目的管理与研究成果的产出具有较大的促进作用。同时可以看出,国家层面的数据管理工作首先必须有明确的国家数据政策作引导;需要建立各种相应的数据标准与规范以及基于Web的数据管理、共享与服务平台;当然更离不开一支进行数据管理、服务与技术支持的专业队伍,并通过工作绩效评价系统对各个环节进行评价和考核。其中,为数据（集）编写完整、准确的元数据始终是数据管理中最重要的基础工作。     

山东省邹平铜矿17号矿体矿床成因

邹平铜矿17号矿体是高中温热液矿床并严格受隐爆角砾岩筒控制,而非伟晶岩型矿床,已被海内外众多同行所认可。该文在勘查结合坑道观测的基础上,通过深入研究和分析对比,进一步证实并坚定了对矿床成因的认识,并澄清了一些模糊看法及无根据的推测。     

On the initialization of tropical cyclones with a three dimensional variational analysis

A method of initializing tropical cyclones in high-resolution numerical models is developed by modifying a data assimilation system, the NRL atmospheric variational data assimilation system (NAVDAS), which was designed for general mesoscale weather prediction using a three-dimensional variational (3DVAR) analysis with intermittent updates. The method includes the following three upgrades to overcome difficulties resulting from tropical cyclone initialization with the NAVDAS analysis. First, synthetic observation soundings are generated on 9 vertical levels at 49 points for strong storms (v _max?>?23.1?m?s^?1) and 41 points for weak storms around each cyclone center to supplement the observations used by the analysis. Secondly, a vortex relocation method for nested grids is developed to correct the cyclone position in the background fields of the analysis for each nested mesh. Lastly, the 3DVAR analysis is modified to gradually reduce the horizontal length scale and geostrophic coupling constraint near the center of a tropical cyclone for minimizing the problems introduced by improper covariances and coupling constraint used in the analysis. The synthetic observations significantly improve the intensity and structure of the analysis and the track forecast. The vortex relocation significantly improves the first guess background, avoiding the large analysis corrections that would be needed to correct cyclone position, and reducing the imbalance introduced by such large analysis increments. The modifications to the analysis length scale and geostrophic coupling constraint successfully improve the inner core analysis, providing a tighter circulation, and reducing the underestimate of the mass field gradient. Among the three upgrades, the vortex relocation provides the largest improvement to the tropical cyclone initialization and forecast.