Assessing sensitivity of source term estimation |
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| Authors: | Kerrie J Long Sue Ellen Haupt George S Young |
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| Institution: | 1. Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, PA 16804, USA;2. Department of Meteorology, The Pennsylvania State University, 503 Walker Building, University Park, PA 16802, USA;1. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China;2. School of Chemical Engineering and Technology, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China;1. Aeris LLC, 1723 Madison CT, Louisville, CO 80027, USA;2. Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA;3. Sage Management Enterprise, LLC, 6731 Columbia Gateway Drive, Suite 150, Columbia, MD 21046, USA;4. Defense Threat Reduction Agency, 8725 John J. Kingman Rd., Ft. Belvoir, VA 22060-6201, USA;5. Colorado State University, Department of Atmospheric Science, 200 West Lake Street, Fort Collins, CO 80523-1371, USA;1. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang Province 310027, China;2. Institute of Zhejiang University-Quzhou, Quzhou, Zhejiang Province 324000, China;3. Zhejiang Ocean University, Zhoushan, Zhejiang Province 316022, China;1. Pacific Northwest National Laboratory, Risk and Decision Sciences Group, 902 Battelle Blvd, P.O. Box 999, MSIN K7-76, Richland, WA 99354, USA;2. The University of Texas at Austin, Austin, TX, USA;3. Radiation Protection Bureau, Health Canada, 775 Brookfield Rd., Ottawa, ON, USA;1. Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China;2. Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, 215021 Suzhou, China;1. Laboratory of Mechanics and Energy, Universite d''Evry-Val d''Essonne, 40 Rue Du Pelvoux, 91080 Courcouronnes, Evry Cedex, France;2. Centre for Atmospheric Sciences, Indian Institute of Technology Delhi 110016, India |
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| Abstract: | Source term estimation algorithms compute unknown atmospheric transport and dispersion modeling variables from concentration observations made by sensors in the field. Insufficient spatial and temporal resolution in the meteorological data as well as inherent uncertainty in the wind field data make source term estimation and the prediction of subsequent transport and dispersion extremely difficult. This work addresses the question: how many sensors are necessary in order to successfully estimate the source term and meteorological variables required for atmospheric transport and dispersion modeling?The source term estimation system presented here uses a robust optimization technique – a genetic algorithm (GA) – to find the combination of source location, source height, source strength, surface wind direction, surface wind speed, and time of release that produces a concentration field that best matches the sensor observations. The approach is validated using the Gaussian puff as the dispersion model in identical twin numerical experiments. The limits of the system are tested by incorporating additive and multiplicative noise into the synthetic data. The minimum requirements for data quantity and quality are determined by an extensive grid sensitivity analysis. Finally, a metric is developed for quantifying the minimum number of sensors necessary to accurately estimate the source term and to obtain the relevant wind information. |
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