Assimilation of all-sky seviri infrared brightness temperatures in a regional-scale ensemble data assimilation system

Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher order nonlinear BC terms were used. Overall, experiments employing the observed cloud top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background

REDUCED MOVEMENT ADAPTABILITY IN SIDESTEPPING – A POSSIBLE SOURCE OF INJURY RISK

Adapting to different task constraints provides insight into how malleable an athlete’s movement dynamics are. The purpose of this pilot study was to investigate whether athletes can adequately change their preferred movement strategy during sidestepping when exposed to a manipulation task. Reduced movement adaptability was hypothesized to be one risk factor for ACL injuries. Fourteen male team sport athletes were investigated. The response to the manipulation task was intra-individual, with rearfoot strikers being less able to adapt their movement strategy and the resulting movement was even higher associated with ACL risk factors. Forefoot strikers were able to adapt their movement. This suggests, that athletes need to be investigated individually as group-based analyses might cover effects and that movement adaptability should be considered when evaluating injury risk

Nonlinear bias correction for satellite data assimilation using Taylor series polynomials

Output from a high-resolution ensemble data assimilation system is used to assess the ability of an innovative nonlinear bias correction (BC) method that uses a Taylor series polynomial expansion of the observation-minus background departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Univariate and multivariate experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the BC predictors. The results showed that even though the bias of the entire observation departure distribution is equal to zero regardless of the order of the Taylor series expansion, there are often large conditional biases that vary as a nonlinear function of the BC predictor. The linear 1st order term had the largest impact on the entire distribution as measured by reductions in variance; however, large conditional biases often remained in the distribution when plotted as a function of the predictor. These conditional biases were typically reduced to near zero when the nonlinear 2nd and 3rd order terms were used. The univariate results showed that variables sensitive to the cloud top height are effective BC predictors especially when higher order Taylor series terms are used. Comparison of the statistics for clear-sky and cloudy-sky observations revealed that nonlinear departures are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases. Together, these results indicate that the nonlinear BC method is able to effectively remove the bias from all-sky infrared observation departures

Modification of softwood kraft pulp fibres using hydrogen peroxide at acidic conditions

The aim of this work was to provide softwood kraft pulp fibres with new functionalities by the introduction of carbonyl groups. Carbonyl groups are known to affect properties such as wet strength through the formation of covalent bonds, i.e. hemiacetals. The method developed involves oxidation using hydrogen peroxide at mildly acidic conditions. It was found that the carbonyl group content increased with both increasing temperature and residence time when oxidized at acidic conditions. The number of carboxylic groups, however, remained approximately constant. There was virtually no increase in carbonyl groups when oxidation was performed at alkaline conditions. The maximum increase in carbonyl groups was found at a residence time of 90\ua0min, a reaction temperature of 85\ua0\ub0C and a pH of 4. These conditions resulted in an increase in carbonyl groups from 30 to 122\ua0\ub5mol/g. When formed into a sheet, the pulp oxidized at acidic conditions proved to maintain its structural integrity at aqueous conditions. This indicates the formation of hemiacetal bonds between the introduced carbonyl groups and the hydroxyl groups on the carbohydrate chains. Thus, a possible application for the method could be fibre modification during the final bleaching stage of softwood kraft pulp, where the wet strength of the pulp could be increased

Ultra Rapid Data Assimilation Based on Ensemble Filters

The goal of this work is to analyse and study an ultra-rapid data assimilation (URDA) method for adapting a given ensemble forecast for some particular variable of a dynamical system to given observation data which become available after the standard data assimilation and forecasting steps. Initial ideas have been suggested and tested by Etherthon 2006 and Madaus and Hakim 2015 in the framework of numerical weather prediction. The methods are, however, much more universally applicable to general non-linear dynamical systems as they arise in neuroscience, biology and medicine as well as numerical weather prediction. Here we provide a full analysis in the linear case, we formulate and analyse an ultra-rapid ensemble smoother and test the ideas on the Lorentz 63 dynamical system. In particular, we study the assimilation and preemptive forecasting step of an ultra-rapid data assimilation in comparison to a full ensemble data assimilation step as calculated by an ensemble Kalman square root filter. We show that for linear systems and observation operators, the ultra-rapid assimilation and forecasting is equivalent to a full ensemble Kalman filter step. For non-linear systems this is no longer the case. However, we show that we obtain good results even when rather strong nonlinearities are part of the time interval [t0, tn] under consideration. Then, an ultra-rapid ensemble Kalman smoother is formulated and numerically tested. We show that when the numerical model under consideration is different from the true model, used to generate the nature run and observations, errors in the correlations will also lead to errors in the smoother analysis. The numerical study is based on the popular Lorenz 1963 model system used in geophysics and life sciences. We investigate both the situation where the full system forecast is calculated and the situation important to practical applications where we study reduced data, when only one or two variables are known to the URDA scheme

Overview of the 2nd international competition on plagiarism detection

This paper overviews 18 plagiarism detectors that have been developed and evaluated within PAN'10. We start with a unified retrieval process that summarizes the best practices employed this year. Then, the detectors' performances are evaluated in detail, highlighting several important aspects of plagiarism detection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year's competition

Overview of the 3rd international competition on plagiarism detection

This paper overviews eleven plagiarism detectors that have been developed and evaluated within PAN'11. We survey the detection approaches developed for the two sub-tasks "external plagiarism detection" and "intrinsic plagiarism detection," and we report on their detailed evaluation based on the third revised edition of the PAN plagiarism corpus PAN-PC-11

Overview of the 1st international competition on plagiarism detection

The 1st International Competition on Plagiarism Detection, held in conjunction with the 3rd PAN workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse, brought together researchers from many disciplines around the exciting retrieval task of automatic plagiarism detection. The competition was divided into the subtasks external plagiarism detection and intrinsic plagiarism detection, which were tackled by 13 participating groups. An important by-product of the competition is an evaluation framework for plagiarism detection, which consists of a large-scale plagiarism corpus and detection quality measures. The framework may serve as a unified test environment to compare future plagiarism detection research. In this paper we describe the corpus design and the quality measures, survey the detection approaches developed by the participants, and compile the achieved performance results of the competitors