Computer Sciences Corporation is an American multinational corporation that provides information technology services and professional services. Its headquarters are located in Falls Church, Virginia. CSC has 74,000 employees in over 70 countries. Its clients include commercial enterprises and the U.S. federal government, as well as state, local and non-U.S. government agencies.In September 2009, when Xerox acquired Affiliated Computer Services, CSC became the only remaining major "hardware vendor independent" IT Service provider with headquarters and major operations in the US.CSC has been a Fortune 500 Company since 1995, ranked 185 in the 2014 rankings. The company also figures in the Forbes Global 2000 list. Wikipedia.
Sickles Ii J.E.,National Exposure Research Laboratory |
Shadwick D.S.,Computer Sciences Corp.
Atmospheric Chemistry and Physics | Year: 2015
Data collected in the eastern US between 1990 and 2009 at 34 paired dry and wet monitoring sites are examined. A goal is to evaluate the air quality impacts occurring between 1990 and 2009 that are associated with concurrent legislatively mandated changes in emissions. Four 5-year periods, 1990-1994 (P1), 1995-1999 (P2), 2000-2004 (P3), and 2005-2009 (P4) are considered, with a primary focus on P1-to-P4 changes. Results suggest that legislatively mandated air pollution mitigation strategies have been successful in improving air quality and reducing atmospheric deposition in the eastern US.
Respective P1-to-P4 reductions of estimated sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions in the eastern US are 50 and 42%. Corresponding behavior of the following metrics associated with these emissions reductions is examined: monitored atmospheric concentrations of SO2, aerosol sulfate (SO4), and oxidized sulfur (S); dry, wet, and total deposition of S; monitored atmospheric concentrations of nitric acid (HNO3), aerosol nitrate (NO3), and their sum, oxidized nitrogen (OxN); dry, wet, and total deposition of OxN; monitored atmospheric concentration of aerosol ammonium (NH4); dry, wet, and total deposition of NH4; summed monitored atmospheric concentration of oxidized and reduced nitrogen (N); dry, wet, and total deposition of N; wet deposition of hydrogen ion (H+); monitored atmospheric concentration of ozone (O3); dry deposition of O3; and the summed monitored atmospheric concentration of aerosol NO3, SO4, and NH4 (Clean Air Status and Trends Network particulate matter-CASTNET PM). Other metrics (e.g., ratios of dry to total deposition) are also considered.
Selected period-to-period changes of air quality and deposition metrics at site, regional, and seasonal scales are discussed. As an example, despite P1-to-P3 reductions in estimated emissions of both SO2 and NOx, aerosol NO3 concentration increased in the east, with widespread wintertime numerical increases in both aerosol NO3 concentration and CASTNET PM. However, a reversal of this behavior is associated with continuing P3-to-P4 reductions of SO2 and NOx emissions. Thus, additional P3-to-P4 reductions of these emissions, especially NOx, appear to have made progress in altering the chemical regime of the wintertime eastern US atmosphere so that future emissions reductions and their resulting reductions in aerosol concentrations may no longer be accompanied by sub-linear changes (or actual increases) in CASTNET PM.
Chuang M.-T.,North Carolina State University |
Zhang Y.,North Carolina State University |
Kang D.,Computer Sciences Corp.
Atmospheric Environment | Year: 2011
A Real-Time Air Quality Forecast (RT-AQF) system that is based on a three-dimensional air quality model provides a powerful tool to forecast air quality and advise the public with proper preventive actions. In this work, a new RT-AQF system is developed based on the online-coupled Weather Research and Forecasting model with Chemistry (WRF/Chem) with the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (referred to as WRF/Chem-MADRID) and deployed in the southeastern U.S. during May-September, 2009. Max 1-h and 8-h average ozone (O3) and 24-h average fine particulate matter (PM2.5) are evaluated against surface observations from the AIRNow database in terms of spatial distribution, temporal variation, and domain-wide and region-specific discrete and categorical performance statistics. WRF/Chem-MADRID demonstrates good forecasting skill that is consistent with current RT-AQF models. The overpredictions of O3 and underprediction of PM2.5 are likely due to uncertainties in emissions such as those of biogenic volatile organic compounds (BVOCs) and ammonia, inaccuracies in simulated meteorological variables such as 2-m temperature, 10-m wind speed, and precipitation, and uncertainties in the boundary conditions. Sensitivity simulations show that the use of the online BVOC emissions can improve PM2.5 forecast in areas with high BVOC emissions and adjusting lateral boundaries can improve domain-wide O3 and PM2.5 predictions. Several limitations and uncertainties are identified to further improve the model's forecasting skill. © 2011 Elsevier Ltd.
Tokay A.,University of Maryland Baltimore County |
Tokay A.,NASA |
Bashor P.G.,Computer Sciences Corp.
Journal of Applied Meteorology and Climatology | Year: 2010
An experimental study of small-scale variability of raindrop size distributions (DSDs) has been carried out at Wallops Island, Virginia. Three Joss-Waldvogel disdrometers were operated at a distance of 0.65, 1.05, and 1.70 km in a nearly straight line. The main purpose of the study was to examine the variability of DSDs and its integral parameters of liquid water content, rainfall, and reflectivity within a 2-km array: a typical size of Cartesian radar pixel. The composite DSD of rain events showed very good agreement among the disdrometers except where there were noticeable differences in midsize and large drops in a few events. For consideration of partial beam filling where the radar pixel was not completely covered by rain, a single disdrometer reported just over 10% more rainy minutes than the rainy minutes when all three disdrometers reported rainfall. Similarly two out of three disdrometers reported 5% more rainy minutes than when all three were reporting rainfall. These percentages were based on a 1-min average, and were less for longer averaging periods. Considering only the minutes when all three disdrometers were reporting rainfall, just over one quarter of the observations showed an increase in the difference in rainfall with distance. This finding was based on a 15-min average and was even less for shorter averaging periods. The probability and cumulative distributions of a gamma-fitted DSD and integral rain parameters between the three disdrometers had a very good agreement and no major variability. This was mainly due to the high percentage of light stratiform rain and to the number of storms that traveled along the track of the disdrometers. At a fixed time step, however, both DSDs and integral rain parameters showed substantial variability. The standard deviation (SD) of rain rate was near 3 mm h -1, while the SD of reflectivity exceeded 3 dBZ at the longest separation distance. These standard deviations were at 6-min average and were higher at shorter averaging periods. The correlations decreased with increasing separation distance. For rain rate, the correlations were higher than previous gauge-based studies. This was attributed to the differences in data processing and the difference in rainfall characteristics in different climate regions. It was also considered that the gauge sampling errors could be a factor. In this regard, gauge measurements were simulated employing existing disdrometer dataset. While a difference was noticed in cumulative distribution of rain occurrence between the simulated gauge and disdrometer observations, the correlations in simulated gauge measurements did not differ from the disdrometer measurements. © 2010 American Meteorological Society.
Moran P.J.,NASA |
Ellsworth D.,Computer Sciences Corp.
IEEE Transactions on Visualization and Computer Graphics | Year: 2011
We present a new technique for providing interpolation within cell-centered Adaptive Mesh Refinement (AMR) data that achieves C° continuity throughout the 3D domain. Our technique improves on earlier work in that it does not require that adjacent patches differ by at most one refinement level. Our approach takes the dual of each mesh patch and generates "stitching cells" on the fly to fill the gaps between dual meshes. We demonstrate applications of our technique with data from Enzo, an AMR cosmological structure formation simulation code. We show ray-cast visualizations that include contributions from particle data (dark matter and stars, also output by Enzo) and gridded hydrodynamic data. We also show results from isosurface studies, including surfaces in regions where adjacent patches differ by more than one refinement level. © 2011 IEEE.
Johnson B.R.,U.S. Environmental Protection Agency |
Haas A.,Computer Sciences Corp. |
Fritz K.M.,U.S. Environmental Protection Agency
Water Resources Research | Year: 2010
Regulatory agencies need methods to quantify the influence of headwater streams on downstream water quality as a result of litigation surrounding jurisdictional criteria and the influence of mountaintop removal coal mining activities. We collected comprehensive, spatially referenced physicochemical data (pH, dissolved oxygen, temperature, and specific conductance) from the partially mined Buckhorn Creek, KY, watershed in summer 2005 (n = 239 sites) and spring 2006 (n = 494 sites). We found conductivity was >10X higher in mined streams than in forested streams. Semivariograms, which quantify the degree of spatial dependence in chemistry values, indicated summer temperatures in both mined and unmined portions of the watershed had similar lag distances (approximately 5 km). Data for other parameters and seasons, however, violated model assumptions because of strong confluence effects in headwaters. We therefore developed a post hoc predictive model for water physicochemistry downstream of confluences using watershed areas as weighting factors. This weighted average model accurately predicted downstream conductivity (mean absolute error, MAE = 55.34 μS cm-1), pH (MAE = 0.16 units), and temperature (MAE = 0.41°C) for confluences in Buckhorn Creek and two additional watersheds with headwater disturbance in West Virginia and Ohio. Use of semivariograms or predictive confluence models can help regulatory agents identify downstream influence of headwater streams and presence of a "significant nexus" with downstream waters.