73,752 research outputs found
Development of Grid e-Infrastructure in South-Eastern Europe
Over the period of 6 years and three phases, the SEE-GRID programme has
established a strong regional human network in the area of distributed
scientific computing and has set up a powerful regional Grid infrastructure. It
attracted a number of user communities and applications from diverse fields
from countries throughout the South-Eastern Europe. From the infrastructure
point view, the first project phase has established a pilot Grid infrastructure
with more than 20 resource centers in 11 countries. During the subsequent two
phases of the project, the infrastructure has grown to currently 55 resource
centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16
participating countries. Inclusion of new resource centers to the existing
infrastructure, as well as a support to new user communities, has demanded
setup of regionally distributed core services, development of new monitoring
and operational tools, and close collaboration of all partner institution in
managing such a complex infrastructure. In this paper we give an overview of
the development and current status of SEE-GRID regional infrastructure and
describe its transition to the NGI-based Grid model in EGI, with the strong SEE
regional collaboration.Comment: 22 pages, 12 figures, 4 table
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Determining Utility System Value of Demand Flexibility From Grid-interactive Efficient Buildings
This report focuses on ways current methods and practices that establish the value to electric utility systems of distributed energy resource (DER) investments can be enhanced to determine the value of demand flexibility in grid-interactive efficient buildings that can provide grid services. The report introduces key valuation concepts that are applicable to demand flexibility that these buildings can provide and links to other documents that describe these concepts and their implementation in more detail.The scope of this report is limited to the valuation of economic benefits to the utility system. These are the foundational values on which other benefits (and costs) can be built. Establishing the economic value to the grid of demand flexibility provides the information needed to design programs, market rules, and rates that align the economic interest of utility customers with building owners and occupants. By nature, DERs directly impact customers and provide societal benefits external to the utility system. Jurisdictions can use utility system benefits and costs as the foundation of their economic analysis but align their primary cost-effectiveness metric with all applicable policy objectives, which may include customer and societal (non-utility system) impacts.This report suggests enhancements to current methods and practices that state and local policymakers, public utility commissions, state energy offices, utilities, state utility consumer representatives, and other stakeholders might support. These enhancements can improve the consistency and robustness of economic valuation of demand flexibility for grid services. The report concludes with a discussion of considerations for prioritizing implementation of these improvements
Management of an Urban Stormwater System Using Projected Future Scenarios of Climate Models: A Watershed-Based Modeling Approach
Anticipating a proper management needs for urban stormwater due to climate change is becoming a critical concern to water resources managers. In an effort to identify best management practices and understand the probable future climate scenarios, this study used high-resolution climate model data in conjunction with advanced statistical methods and computer simulation. Climate model data from the North American Regional Climate Change Assessment Program (NARCCAP) were used to calculate the design storm depths for the Gowan Watershed of Las Vegas Valley, Nevada. The Storm Water Management Model (SWMM), developed by the Environmental Protection Agency (EPA), was used for hydrological modeling. Two low-impact development techniques â Permeable Pavement and Green Roof â were implemented in the EPA SWMM hydrological modeling to attenuate excess surface runoff that was induced by climate change. The method adopted in this study was effective in mitigating the challenges in managing changes in urban stormwater amounts due to climate change
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of Californiaâs California Institute for Energy and the Environment, from 2003-2014
Improvement of the Fairbanks Atmospheric Carbon Monoxide Transport Model -- A Program for Calibration, Verification and Implementation
Completion Report Prepared for the Research Section, Alaska Department of Transportation and Public FacilitiesIn the early 70s, state, local and federal officials in Fairbanks,
Alaska, became concerned with the rising incidence of high carbon monoxide
episodes. Because of that concern, the Alaska Department of
Highways (forerunner of the Department of Transportation and Public
Facilities) and the Fairbanks North Star Borough requested that the
Institute of Water Resources undertake a study to develop a computer
model capability for understanding the transport of carbon monoxide and
other pollutants within the Fairbanks airshed. The work was completed
in June of 1976. Two publications (Carlson and Fox, 1976; Norton and
Carlson, 1976) describe the initial development, documentation and
implementation of the computer model. The model, ACOSP (Atmospheric
Carbon monOxide Simulation Program), describes the two-dimensional
behavior of pollutants in the atmosphere via solution of the convection-diffusion
equation using the finite element method of numerical analysis
Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes
To disentangle the complex non-stationary dependence structure of
precipitation extremes over the entire contiguous U.S., we propose a flexible
local approach based on factor copula models. Our sub-asymptotic spatial
modeling framework yields non-trivial tail dependence structures, with a
weakening dependence strength as events become more extreme, a feature commonly
observed with precipitation data but not accounted for in classical asymptotic
extreme-value models. To estimate the local extremal behavior, we fit the
proposed model in small regional neighborhoods to high threshold exceedances,
under the assumption of local stationarity, which allows us to gain in
flexibility. Adopting a local censored likelihood approach, inference is made
on a fine spatial grid, and local estimation is performed by taking advantage
of distributed computing resources and the embarrassingly parallel nature of
this estimation procedure. The local model is efficiently fitted at all grid
points, and uncertainty is measured using a block bootstrap procedure. An
extensive simulation study shows that our approach can adequately capture
complex, non-stationary dependencies, while our study of U.S. winter
precipitation data reveals interesting differences in local tail structures
over space, which has important implications on regional risk assessment of
extreme precipitation events
Source-Receptor Relationships for Ozone and Fine Particulates in the Eastern United States
A key question in developing effective mitigation strategies for ozone and particulate matter is identifying which source regions contribute to concentrations in receptor regions. Using a direct approach with a regional, multiscale three-dimensional model, we derive multiple source-receptor matrices (S-Rs) to show inter- and intrastate impacts of emissions on both ozone and PM2.5 over the eastern United States. Our results show that local (in-state) emissions generally account for about 23% of both local ozone concentrations and PM2.5 concentrations, while neighboring states contribute much of the rest. The relative impact of each state on others varies dramatically between episodes. In reducing fine particulate concentrations, we find that reducing SO2 emissions can be 10 times as effective as reducing NOx emissions. SO2 reductions can lead to some increase in nitrates, but this is relatively small. NOx reductions, however, lead to both ozone reductions and some reduction in nitrate and sulfate particulate matter.source-receptor, ozone, particulate matter, sensitivity analysis, air quality simulation, National Ambient Air Quality Standards
Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling
Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future
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Improving Precipitation Estimation Using Convolutional Neural Network
Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach
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