Effects of Student Athletics on Academic Performance

The purpose of this study is to examine the effects of involvement in athletics at the collegiate level at South Dakota State University. This paper seeks to determine if participation in collegiate athletics is beneficial to a person or if its disadvantages outweigh the advantages. Sixty-seven student-athletes completed a survey during October of 2013 to determine the effects that athletic involvement has had on SDSU’s campus. This study found that participation in athletics is extremely beneficial. These athletes performed better in the classroom, developed impressive time management skills, felt motivated to complete their degree, were motivated to attend classes, and experienced a smoother transition into the college lifestyle

Ensemble model output statistics for wind vectors

A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-hour forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling

Skill forecasting from ensemble predictions of wind power

International audienceOptimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set of alternative scenarios for the coming period) for a single prediction horizon or over a look-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion of ECMWF and NCEP ensemble forecasts of meteorological variables to wind power ensemble forecasts, as well as by a lagged average approach alternative. The ability of prediction risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

During the second Wind Forecast Improvement Project (WFIP2; October 2015&ndash;March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR &ndash; 3&thinsp;km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST &ndash; 750&thinsp;m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80&thinsp;m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80&thinsp;m wind speed MAE is reduced by 3&thinsp;%&ndash;4&thinsp;% due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5&thinsp;% on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80&thinsp;m wind speed MAE, up to 7&thinsp;%&ndash;8&thinsp;%. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80&thinsp;m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors. </div

Unilateral Refusals to Deal as a Method of Deterring Private Antitrust Litigants: A Legitimate Method of Economic Coercion?

The role which the private litigant plays in the enforcement of the antitrust laws, either by an action to recover treble damages or to enjoin antitrust violations, is of increasing importance. As these actions become more frequent, additional problems are raised. One such problem is the use of a unilateral refusal to deal as a deterrent to the effective use of the treble damage suit by the private litigant. Two recent parallel cases have dealt with the question of allowing temporary injunctive relief where the defendant has refused to deal with a plaintiff asking for treble damages. In House of Materials, Inc. v. Simplicity Pattern Co., the Second Circuit denied injunctive relief in the above situation, whereas in Bergen Drug Co. v. Parke, Davis & Co., the Third Circuit granted the injunction pendente lite. The purpose of this comment is to examine these recent cases in the light of the interest protected and the propriety of injunctive relief. Since the two cases involve similar situations but reach different results, a close examination and comparison of their facts and a discussion of the possible implications of their holdings is required. Before analyzing the narrow question involved, it is first necessary to examine the existing law in the general area of refusals to deal, and in the particular area of the simple unilateral refusal to deal

The Scope of Judicial Review of Administrative Determinations in Nebraska

I. Statutory Appeal with No Statutory Definition of the Scope of Review … A. The Present Rule … (1) Competent evidence … (2) Agency acceptance of testimony … (3) The record on appeal … (4) Sufficiency of evidence … B. A Comparison of the Nebraska Rule to the Federal Tests II. Statutory Appeal with the Scope of Review Provided by Statute … A. De Novo Review in Taxation Matters … B. Statutory Interpretation Leading to De Novo Review … C. Various Statutory Tests Defining the Scope of Review … D. De Novo Review—A Critical View III. The Petition in Error IV. Conclusio

Abstract Implementation and Evaluation of a Mesoscale Short-range Ensemble Forecasting System over the Pacific Northwest

This is to certify that I have examined this copy of a master’s thesis b

Probabilistic mesoscale forecast error prediction using short-range ensembles

Thesis (Ph. D.)--University of Washington, 2004One measure of the utility of ensemble prediction systems is the relationship between ensemble spread and forecast error. Unfortunately, this relationship is often characterized by an inadequate measure (the spread-error correlation) that makes two critical assumptions: (1) a linear dependency between ensemble spread and forecast error and (2) an end user that has a continuous sensitivity to forecast error. The validity of these assumptions is investigated with a simple, stochastic model that estimates the upper bound in expected performance of real ensembles. The linear dependence assumption is shown to be invalid under a variety of spread and error metrics.A more complete understanding is achieved by considering the spread-skill relationship in a probabilistic context. A perfect spread-skill relationship can be interpreted as a higher-order statistical consistency, where ensemble variance equals ensemble-mean error variance for all individual classes of ensemble spread. This interpretation allows for a new approach to forecast error prediction, where error climatologies conditioned on the ensemble spread are used as probabilistic forecasts of error. The ensemble spread-skill relationship is evaluated by the skill of such probabilistic error forecasts relative to the skill of the overall error climatology.For ideal ensembles based on a stochastic model, the skill of spread-based conditional error climatology forecasts is nearly equal to the skill of forecasts taken directly from the ensemble probability density function. The skill of spread-based, conditional error climatology forecasts is highest for cases with extreme spread and lowest for cases with near-normal spread, which reinforces earlier results. Additionally, it is concluded that end users should choose a spread metric consistent with their own cost function to form appropriate error climatologies.A 361-case archive of mesoscale, short-range ensemble forecasts developed at the University of Washington is used to analyze the spread-skill relationship for real ensembles. Probabilistic error forecasts of near-surface winds and temperatures from spread-based, conditional error climatologies are more skillful than forecasts taken directly from the ensemble probability density function. This performance advantage is achieved because the direct ensemble forecasts are biased and uncalibrated. As direct ensemble probability forecasts improve, the advantage gained by using spread-based, conditional error climatologies diminishes