Integration of AMS and ERDS Measurement Data into NARAC Dispersion Models FY05 Technology Integration Project Final Report

Foster, K. ; Arnold, E. ; Bonner, D. ; Eme, B. ; Fischer, K. ; Gash, J. ; Nasstrom, J. ; Walker, H. ; Guber, A. ; Logan, C. ; Wasiolek, P. ; Fulton, J.

Lawrence Livermore National Laboratory

关键词: Modifications;    Lawrence Livermore National Laboratory;    Remote Sensing;    Implementation;    Processing;   

DOI  :  10.2172/878228
RP-ID  :  UCRL-TR-215614
RP-ID  :  W-7405-ENG-48
RP-ID  :  878228

美国|英语

来源: UNT Digital Library

【 摘 要 】

Staff from Lawrence Livermore National Laboratory (LLNL), Bechtel Nevada Remote Sensing Laboratory (RSL), and Sandia National Laboratory (SNL) completed the proposed work for the Technology Integration Project titled Integration of AMS and ERDS Measurement Data into NARAC Dispersion Models. The objectives of this project were to develop software to convert Aerial Measurement Survey (AMS) and Emergency Response Data System (ERDS) field measurement data into a standard electronic format for transmission to the National Atmospheric Release Advisory Center (NARAC), and to streamline aspects of the NARAC operational atmospheric dispersion modeling system to quickly process these data for use in generating consequence calculations based on refined, field measurement-based estimates of the source strength. Although NARAC continues to develop and maintain a state-of-the-art atmospheric dispersion modeling system, model predictions are constrained by the availability of information to properly characterize the source term. During an actual atmospheric release, very little may be known initially about the source material properties, amount, or release time and location. Downwind measurements often provide the best information about the scope and nature of the release. The timely integration of field measurement data with model calculations is an obvious approach toward improving the model consequence predictions. By optimizing these predictions a more accurate representation of the consequences may be provided to (a) predict contamination levels which may be below the detectable limit of sensors, but which may still pose a significant hazard, (b) determine contamination is areas where measurements have not yet been made, and (c) prioritize the locations of future measurement surveys. By automating and streamlining much of the related field measurement data processing, these optimized predictions may be provided within a significantly reduced period, and with a reduction in potential errors. The associated operational shortfalls were resolved by completing the following major tasks under this technology integration project: (1) The definition and implementation of a standard Extensible Markup Language (XML) measurement data transmission format, (2) Modification to the NARAC system Graphical User Interface (GUI) to streamline field measurement manipulation and processing within the NARAC system, (3) Modification to the NARAC graphical visualization capabilities to display, filter, and select target measurement data for further processing, (4) Improved access to statistically-oriented comparisons between model calculations and field measurement data aimed at developing a refined source term used to optimize model predictions, and (5) Rewriting of a limited legacy code which performs the calculations necessary to compare model results with field measurement data. Each of these tasks is discussed below in greater detail. The completed system was successfully used during the Dingo King exercise in August, 2005.