Assessing sensitivity of source term estimation

Atmospheric Environment

Volumn 44, Issue 12, 2010, Pages 1558-1567

  Long, Kerrie J ^a   Haupt, Sue Ellen ^a   Young, George S ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Dispersion modeling; Gaussian puff model; Genetic algorithm; Source term estimation

Indexed keywords

ADDITIVE AND MULTIPLICATIVE NOISE; ATMOSPHERIC TRANSPORT; BEST MATCH; CONCENTRATION FIELDS; DATA QUANTITY; DISPERSION MODELING; DISPERSION MODELS; GAUSSIAN PUFF; GAUSSIAN PUFF MODEL; GRID SENSITIVITY; METEOROLOGICAL DATA; METEOROLOGICAL VARIABLES; NUMERICAL EXPERIMENTS; ROBUST OPTIMIZATION; SOURCE LOCATION; SOURCE STRENGTH; SOURCE TERM ESTIMATION; SOURCE TERMS; SURFACE WIND SPEED; SURFACE WINDS; SYNTHETIC DATA; TEMPORAL RESOLUTION; WIND FIELD;

DISPERSIONS; ESTIMATION; FLOW FIELDS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS; SENSORS;

ATMOSPHERIC MOVEMENTS;

ATMOSPHERIC POLLUTION; ATMOSPHERIC TRANSPORT; CONCENTRATION (COMPOSITION); DISPERSION; ESTIMATION METHOD; GAUSSIAN METHOD; GENETIC ALGORITHM; MODEL VALIDATION; NUMERICAL MODEL; OPTIMIZATION; PREDICTION; SENSITIVITY ANALYSIS; WIND DIRECTION; WIND VELOCITY;

AIR POLLUTION; ARTICLE; ATMOSPHERIC DISPERSION; ATMOSPHERIC TRANSPORT; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; GENETIC ALGORITHM; MATHEMATICAL ANALYSIS; METEOROLOGY; POLLUTION TRANSPORT; PREDICTION; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; WIND;

NEPTUNIA;

EID: 77649274007     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2010.01.003     Document Type: Article

References (31)

1. Allen C.T., Haupt S.E., and Young G.S. Source characterization with a genetic algorithm-coupled dispersion-backward model incorporating SCIPUFF. Journal of Applied Meteorology and Climatology 46 (2007) 273-287
2. Allen C.T., Young G.S., and Haupt S.E. Improving pollutant source characterization by optimizing meteorological data with a genetic algorithm. Atmospheric Environment 41 (2007) 2283-2289
3. Beychok M.R. Fundamentals of Gas Stack Dispersion. third ed. (1994), Milton Beychok, Irvine, CA pp. 193
4. Camelli F., and Lohner R. Assessing maximum possible damage for contaminant release events. Engineering Computations 21 (2004) 748-760
5. Cheng, Y., Reddy, K.V.U., Singh, T., Scott, P., 2007. CBRN data fusion using puff-based model and bar-reading sensor data. In: 10th International Conference on Information Fusion, Quebec City, Canada, July 9-12, 2007.
6. Chow F.K., Kosovic B., and Chan S. Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations. Journal of Applied Meteorology and Climatology 47 (2008) 1553-1572
7. Daley R. Atmospheric Data Analysis (1991), Cambridge University Press, New York pp. 457
8. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley, New York
9. Haupt S.E. A demonstration of coupled receptor/dispersion modeling with a genetic algorithm. Atmospheric Environment 39 Issue 37 (2005) 7181-7189
10. Haupt S.E., Young G.S., and Allen C.T. Validation of a receptor/dispersion model with a genetic algorithm using synthetic data. Journal of Applied Meteorology 45 (2006) 476-490
11. Haupt R.L., and Haupt S.E. Practical Genetic Algorithms. second ed. with CD (2004), John Wiley & Sons, New York, NY pp. 255
12. Haupt S.E., Young G.S., and Allen C.T. A genetic algorithm method to assimilate sensor data for a toxic contaminant release. Journal of Computers 2 Issue 6 (2007) 85-93
13. Haupt S.E., Haupt R.L., and Young G.S. A mixed integer genetic algorithm used in chem-bio defense applications. Journal of Soft Computing (2009) 10.1007/s00500-009-0516-z
14. Haupt S.E., Beyer-Lout A., Long K.J., and Young G.S. Assimilating concentration observations for transport and dispersion modeling in a meandering wind field. Atmospheric Environment 3 Issue 6 (2009) 1329-1338
15. In: Haupt S.E., Pasini A., and Marzban C. (Eds). Artificial Intelligence Methods in the Environmental Sciences (2009), Springer-Verlag, Berlin Heidelberg pp. 424
16. Holland J.H. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor
17. Krysta M., Bocquet M., Sportisse B., and Isnard O. Data assimilation for short-range dispersion of radionuclides: an application to wind tunnel data. Atmospheric Environment 40 (2006) 7267-7279
18. Lamb R.G. Air pollution models as descriptors of cause-effect relationships. Atmospheric Environment 18 (1984) 591-606
19. Lewellen W.S., and Sykes R.I. Meteorological data needs for modeling air quality uncertainties. Journal of Atmospheric and Ocean Technology 6 (1989) 759-768
20. Long, K.J., 2007. Improving Contaminant Source Characterization and Meteorological Data Forcing with a Genetic Algorithm. M.S. thesis. Dept. of Meteorology, The Pennsylvania State University, pp. 55.
21. Nelder J.A., and Mead R. A simplex method for function minimization. Computer Journal 7 (1965) 308-313
22. Neuman, S., Glascoe, L., Kosovic, B., Dyer, K., Hanley, W., Nitao, J., 2006. Event reconstruction for atmospheric releases employing urban puff model UDM with stochastic inversion methodology. In: Annual American Meteorological Society Meeting, Atlanta.
23. Oluwole, O.O., Albo, S.E., Miake-Lye, R.C., 2008. Source estimation using SCIPUFF tangent-linear or adjoint. In: 2008 Chemical and Biological Defense Physical Science & Technology Conference, New Orleans, LA, 17-21 November 2008.
24. Pasquill F. The estimation of the dispersion of windborne material. Meteorological Magazine 90 (1961) 33-49
25. Peltier, L.J., Haupt, S.E., Wyngaard, J.C., Stauffer, D.R., Deng, A.J., Lee, J., Long, K.J., Annunzio, A.J. Parameterization of NWP uncertainty for dispersion modeling. Journal of Applied Meteorology and Climatology, in revision.
26. Reddy, K.V.U., Cheng, Y., Singh, T., Scott, P., 2007. Data assimilation in variable dimension dispersion models using particle filters. In: 10th International Conference on Information Fusion, Quebec City, Canada, July 9-12, 2007.
27. Robins, P., Rapley, V., Thomas, P., 2005a. Non-linear Bayesian CBRN source term estimation, Dstl, Salisbury, UK. In: Proceedings of the 8th International FUSION2005 Conference, Philadelphia, PA.
28. Robins, P., Rapley, V., Thomas, P., 2005b. A probabilistic chemical sensor model for data fusion, Dstl, Salisbury, UK. In: Proceedings of the 8th International FUSION2005 Conference, Philadelphia, PA.
29. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal 27 (1948) 379-423 and 623-656
30. Storch R.B., Pimental L.C.G., and Orlande H.R.B. Identification of atmospheric boundary layer parameters by inverse problem. Atmospheric Environment 41 (2006) 1417-1425
31. Terejanu, G., Singh, T., Scott, P., 2007. Unscented Kalman filter/smoother for a CBRN puff-based dispersion model. In: 10th International Conference on Information Fusion, Quebec City, Canada, July 9-12, 2007.