干旱区生态环境知识资源中心(Arid): Error Analysis of CM Data Products Sources of Uncertainty

Arid

DOI	10.2172/1343383
报告编号	SAND2017--1289R
来源ID	OSTI_ID: 1343383
	Error Analysis of CM Data Products Sources of Uncertainty
	Hunt, Brian D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)]; Eckert-Gallup, Aubrey Celia [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)]; Cochran, Lainy Dromgoole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)]; Kraus, Terrence D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)]; Allen, Mark B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)]; Beal, Bill [National Security Technologies, Joint Base Andrews, MD (United States)]; Okada, Colin [National Security Technologies, LLC. (NSTec), Las Vegas, NV (United States)]; Simpson, Mathew [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)]
英文摘要	This goal of this project is to address the current inability to assess the overall error and uncertainty of data products developed and distributed by DOE鈥檚 Consequence Management (CM) Program. This is a widely recognized shortfall, the resolution of which would provide a great deal of value and defensibility to the analysis results, data products, and the decision making process that follows this work. A global approach to this problem is necessary because multiple sources of error and uncertainty contribute to the ultimate production of CM data products. Therefore, this project will require collaboration with subject matter experts across a wide range of FRMAC skill sets in order to quantify the types of uncertainty that each area of the CM process might contain and to understand how variations in these uncertainty sources contribute to the aggregated uncertainty present in CM data products. The ultimate goal of this project is to quantify the confidence level of CM products to ensure that appropriate public and worker protections decisions are supported by defensible analysis.
出版年	2017
报告类型	Technical Report
语种	英语
国家	美国
来源学科分类	97 MATHEMATICS AND COMPUTING
URL	http://www.osti.gov/scitech/servlets/purl/1343383
资源类型	科技报告
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/271407
推荐引用方式 GB/T 7714	Hunt, Brian D. [Sandia National Lab. ,Eckert-Gallup, Aubrey Celia [Sandia National Lab. ,Cochran, Lainy Dromgoole [Sandia National Lab. ,et al. Error Analysis of CM Data Products Sources of Uncertainty,2017.