Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China

Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

Distributed Parallelization of a Global Atmospheric Data Objective Analysis System

It is difficult to parallelize a subsistent sequential algorithm. Through analyzing the sequential algorithm of a Global Atmospheric Data Objective Analysis System, this article puts forward a distributed parallel algorithm that statically distributes data on a massively parallel processing (MPP) computer. The algorithm realizes distributed parailelization by extracting the analysis boxes and model grid point Iatitude rows with leaped steps, and by distributing the data to different processors. The parallel algorithm achieves good load balancing, high parallel efficiency, and low parallel cost. Performance experiments on a MPP computer arc also presented.

Multivariate Objective Analysis of Wind and Height Fields in the Tropics

The commollly used objective analysis scheme(Scheme-A) for the analysis Of wind and geopotential height smoothen the divergent component of the wind which is rather important in the tropics,specifically over convective regions.To overcome this deficiellcy, a new analysis SCheme in which divergent component is included in the statistical model of the wind forecast errors,has been proposed by Daley(1985).Following this scheme,a new set of correlahon functions of forecast errors for the indian region during monsoon season which are suitable for analysing the tropical wind are obtained.This analysis scheme(Scheme--B) as well as Scheme-A were used to make analyses for the period from 4 July to & July 1979(12 GMT) at 850,700 and 200 hpa levels over an area bounded by l.875'N to 39.375'N and 41.250'E to 108.750'E and subsequently divergent component,velocity potential are computed for both schemes.Results from both these schemes show that in the monsoon depression region the velocity potential and divergence have increased in the later case(Scheme-B).This suggests that the divergent component has been enhanced in Scheme-B and that the objechve of this study is realized to some extent.

An Objective Analysis Method for Surface Residual Current Field in the East China Seas

Since the volume transport across the pycnocline is much smaller than that in the mixed layer, the current in the mixed layer can be regarded as non-divergent. An objective analysis method is deduced based on this hypothesis. The linear combination method is used to solve the non-divergent component of the current field of an ocean basin containing islands,which is equivalent to a mathematical problem of solving a Poisson equation in a multi-connected domain. The method is applied to the Bohai Sea, the Yellow Sea and the East China Sea (ECS). The modeled result is consistent with the current maps constructed by other oceanographers.

Some Experiments with Multivariate Objective Analysis Scheme of Heights and Winds Using Optimum Interpolation

A two-dimensional, multitvariate objective analysis scheme for simultaneous analysis of geopotential height and wind fields has been developed over Indian and adjoining region for use in numerical weather prediction. The height-height correlations calculated using daily data of four July months (1976-1979), are used to derive the other autocorrelations and cross-correlations assuming geostropic relationship. A Gaussian function is used to model the autocorrelation function. Since the scheme is multivariate the regression coefficients (weights) are matrix.Near the equator, the geostrophic approximation relating mass and wind is decoupled in a way similar to Bergman (1979). The objective analyses were made over Indian and adjoining region for 850, 700, 500, 300 and 200 hPa levels for the period from 4 July to 8 July 1979, 12 GMT. The analyses obtained using multivariate optimum interpolation scheme depict the synoptic situations satisfactorily. The analyses were also compared with the FGGE analyses (from ECMWF) and also with the station observations by computing the root mean square (RMS) errors and the RMS errors are comparable with those obtained in other similar studies.

Construction of Vertical Wind Profile from Satellite-Derived Winds for Objective Analysis of Wind Field

During summer Monex-79, a variety of observing systems viz. research ships, research aircrafts, constant pressure balloons and geostationary satellite etc. were deployed, besides the regular conventional observations. The purpose of these additional systems was to make the best possible data for the studies on various aspects of monsoon circulation. The present study is aimed at the construction of vertical wind profile using cloud motion vectors obtained from GOES (I-O) satellite and to examine whether the constructed wind profiles improves the representation of the monsoon system, flow pattern etc. in the objective analysis. For this purpose, climatological normals of the wind field are considered as the initial guess and the objective analyses of the wind field are made with, first using only data from conventional observations over land areas, subsequently including the constructed winds from cloud motion vectors. These analyses are then compared with the standard analyses of wind field obtained from Quick Look Atlas by T. N. Krishnamurti et al. (1979).It is inferred that satellite estimated mean wind profiles show good agreement with the mean wind profiles of the research ships with RMS errors less than 5 mps below 500 hPa and less than 8 mps above 500 hPa. It is further inferred that the inclusion of constructed winds shows a positive impact on the objective analysis and improvement is seen to be more marked in the data-sparse region of the Arabian sea. Analyses which include the constructed winds show better agreement with the standard analysis, than the analyses obtained using only conventional winds. Thus, results of our study suggest that the wind profiles constructed using cloud motion vectors are of potential use in objective analysis to depict the major circulation features over the Indian region.

OBJECTIVE CLASSIFICATION OF THE TRACKS OF TROPICAL STORMS IN THE BAY OF BENGAL

An objective analysis of tropical cyclone tracks is performed, with which the tracks of 131 tropical storms(TSs) in 1972-2011 are separated into three types that move west-, north- and northwestward, denoted as Types A, B and C, respectively. Type A(21 TSs and 16% of total) has the origin in the southwestern Bay of Bengal, with the TS in a unimodal distribution as its seasonal feature, occurring mainly in autumn; 18 of the 21 TSs(taking up 90%) land mostly on the western Bay coast(west of 85°E); 5% of Type-A TSs attains the wind speed of >42.7 to 48.9 m/s. Type A has little or no effect on Tibet. Type B(74 TSs, 56.6% of the total) has its preferable origin in the central Bay of Bengal, with the TS in a bimodal distribution as its seasonal pattern. This type denotes the travel in the north in spring,with the landfall of 67 of the 74 TSs(accounting for 91%) mainly on the middle coast of the Bay(85° to 95°E), and19% of the TSs reaching the wind velocity of >42.7 to 48.9 m/s, which exert great effect on Tibet and it is this TS track that gives strong precipitation on its way through this region. Type C(36 TSs, 27.5% of the total) has its main origin in the southern part of the bay, and these TSs are formed largely in autumn, moving in the northwest direction,and 23 of the 36 TSs(64%) land mostly on the western Bay coast, lasting for a longer time, with almost no impact upon Tibet.

Neutron activation method in the analysis of environmental objects

ＮｅｕｔｒｏｎａｃｔｉｖａｔｉｏｎｍｅｔｈｏｄｉｎｔｈｅａｎａｌｙｓｉｓｏｆｅｎｖｉｒｏｎｍｅｎｔａｌｏｂｊｅｃｔｓＧ．Ｍ．Ｋｏｌｅｓｏｖ（Ｖ．Ｉ．ＶｅｒｎａｄｓｋｙＩｎｓｔｉｔｕｔｅｏｆＧｅｏｃｈｅｍ。＆Ａｎａｌ．Ｃｈｅｍ．，ＲｕｓｓｉａｎＡｃａｄ...

TOPMODEL Hydrometeorological Modeling with Rain Gauge Data Integrated by High-Resolution Satellite Estimates. A Case Study in MuriaéRiver Basin, Brazil

This study consists of hydrological simulations of the Muriaé river watershed with the topography-based hydrological model (TOPMODEL) and available stream gauge and rain measurements between 2009 and 2013 for two subbasins, namely Carangola and Patrocínio do Muriaé. The simulations were carried out with the Climate Prediction Center morphing method (CMORPH) precipitation estimates and rain gauge measurements integrated into CM- ORPH by the Statistical Objective Analysis Scheme (SOAS). TOPMODEL calibration was performed with the shuffled complex evolution (SCE-UA) method with Nash-Sutcliffe efficiency (NSE). The best overall results were obtained with CMORPH (NSE ~ 0.6) for both subbasins. The simulations with SOAS resulted in an NSE ~ 0.2. However, in an analysis of days with high- level stages, SOAS simulations resulted in a better hit rate (23%) compared to CMORPH (10%). CMORPH simulations underestimated the flows at the flood periods, which indicates the importance to use multi-sensor precipitation data. The results with TOPMODEL allow an estimate of future discharges, which allows for better planning of a flood warning system and discharge measurement schedule.

Estimation of Winds at Different Isobaric Levels Based on the Observed Winds at 850 hPa Level Using Double Fourier Series

A technique based on the double Fourier series is developed to estimate the winds at different isobaric levels forthe limited area domain, 35°E to 140°E and 30°S to 40°N, using the observed winds at 850 hPa lcvcl for the month ofJune. For this purpose the wind field at a level under consideration is taken in the ratio form with that of 850 hPa level and the coefficients of the double Fouricr series are computed. These coefficients are subsequently used to computethe winds which are compared with the actual winds. The results of the double Fourier series technique are comparedwith those of the polynomial surface fitting method developed by Bavadekar and Khaladkar (1 992). The technique isalso applied for the daily wind data of 11. June, 1979 and the validation of the technique is tested for a few radiosondestations of india. The computed winds for these radiosonde stations arc quite close to observed winds.

Estimating Tropical Cyclone Precipitation from Station Observations

In this paper, an objective technique for estimating the tropical cyclone （TC） precipitation from station observations is proposed. Based on a comparison between the Original Objective Method （OOM） and the Expert Subjective Method （ESM）, the Objective Synoptic Analysis Technique （OSAT） for partitioning TC precipitation was developed by analyzing the western North Pacific （WNP） TC historical track and the daily precipitation datasets. Being an objective way of the ESM, OSAT overcomes the main problems in OOM, by changing two fixed parameters in OOM, the thresholds for the distance of the absolute TC precipitation （D0） and the TC size （D1）, into variable parameters.Case verification for OSAT was also carried out by applying CMORPH （Climate Prediction Center MORPHing technique） daily precipitation measurements, which is NOAA＇s combined satellite precipitation measurement system. This indicates that OSAT is capable of distinguishing simultaneous TC precipitation rain-belts from those associated with different TCs or with middle-latitude weather systems.

Objective Bayesian analysis for the accelerated degradation model usingWiener process with measurement errors

The Wiener process as a degradation model plays an important role in the degradation analysis.In this paper, we propose an objective Bayesian analysis for an acceleration degradation Wienermodel which is subjected to measurement errors. The Jeffreys prior and reference priors underdifferent group orderings are first derived, the propriety of the posteriors is then validated. It isshown that two of the reference priors can yield proper posteriors while the others cannot. A simulation study is carried out to investigate the frequentist performance of the approach comparedto the maximum likelihood method. Finally, the approach is applied to analyse a real data.

Big Earth data:disruptive changes in Earth observation data management and analysis?

Turning Earth observation(EO)data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community.Recently,the term‘big Earth data’emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges.We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains.The disruptive element is that analysts and end-users increasingly rely on Web-based workflows.In this contribution we study selected systems and portals,put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter

A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.

Posterior propriety of an objective prior for generalized hierarchical normal linear models

Bayesian Hierarchical models has been widely used in modern statistical application.To deal with the data having complex structures,we propose a generalized hierarchical normal linear(GHNL)model which accommodates arbitrarily many levels,usual design matrices and'vanilla'covari-ance matrices.Objective hyperpriors can be employed for the GHNL model to express ignorance or match frequentist properties,yet the common objective Bayesian approaches are infeasible or fraught with danger in hierarchical modelling.To tackle this issue,[Berger,J,Sun,D.&Song,C.(2020b).An objective prior for hyperparameters in normal hierarchical models.Journal of Multi-variate Analysis,178,104606.https://doi.org/10.1016/jmva.2020.104606]proposed a particular objective prior and investigated its properties comprehensively.Posterior propriety is important for the choice of priors to guarantee the convergence of MCMC samplers.James Berger conjec-tured that the resulting posterior is proper for a hierarchical normal model with arbitrarily many levels,a rigorous proof of which was not given,however.In this paper,we complete this story and provide an user friendly guidance.One main contribution of this paper is to propose a new technique for deriving an elaborate upper bound on the integrated likelihood but also one uni-fied approach to checking the posterior propriety for linear models.An eficient Gibbs sampling method is also introduced and outperforms other sampling approaches considerably.

Bayesian analysis for quantile smoothing spline

In Bayesian quantile smoothing spline[Thompson,P.,Cai,Y.,Moyeed,R.,Reeve,D.,&Stander,J.(2010).Bayesian nonparametric quantile regression using splines.Computational Statistics and Data Analysis,54,1138-1150.],a fixed-scale parameter in the asymmetric Laplace likelihood tends to result in misleading fitted curves.To solve this problem,we propose a new Bayesian quantile smoothing spline(NBQSS),which considers a random scale parameter.To begin with,we justify its objective prior options by establishing one sufficient and one necessary condition of the posterior propriety under two classes of general priors including the invariant prior for the scale component.We then develop partially collapsed Gibbs sampling to facilitate the compu-tation.Out of a practical concern,we extend the theoretical results to NBQSS with unobserved knots.Finally,simulation studies and two real data analyses reveal three main findings.Firstly,NBQSS usually outperforms other competing curve fitting methods.Secondly,NBQSS consid-ering unobserved knots behaves better than the NBQSS without unobserved knots in terms of estimation accuracy and precision.Thirdly,NBQSS is robust to possible outliers and could provide accurate estimation.

Evaluation of semivariogram features for objectbased image classification

Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

Some ISsues about Information System Integration

This paper discussed some issues about information system integration and presented a methodology for dealing with it. The IS integration is comprised of information integration, function integration, platform integration and human integration. Among them, Information Integration is the goal of the system integration, while the Human integration is the basis. Based upon the practice of an airline integration information system, a phase based methodology has developed, which combines different methods of development into the integration process.