Assimilation of observations of radiation level into an atmospheric transport model: A case study with the particle filter and the ETEX tracer dataset

Atmospheric Environment

Volumn 45, Issue 34, 2011, Pages 6149-6157

  Hiemstra, Paul H ^a   Karssenberg, Derek ^a   van Dijk, Arjan ^b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b NATIONAL INSTITUTE FOR PUBLIC HEALTH AND THE ENVIRONMENT   (Netherlands)

Author keywords

Atmospheric transport model; Data assimilation; Ensemble forecasting; ETEX dataset; Nuclear release; Particle filter

Indexed keywords

ATMOSPHERIC TRANSPORT MODEL; DATA ASSIMILATION; DATA SETS; ENSEMBLE FORECASTING; NUCLEAR RELEASE; PARTICLE FILTER;

AIR FILTERS; ATMOSPHERICS; DATA PROCESSING; MEAN SQUARE ERROR; MODELS; NONLINEAR FILTERING; RADIATION; RADIOACTIVITY; RADIOLOGY; WEATHER FORECASTING;

ATMOSPHERIC MOVEMENTS;

TRACER;

ATMOSPHERIC MODELING; ATMOSPHERIC POLLUTION; ATMOSPHERIC TRANSPORT; DATA ASSIMILATION; DATA SET; DISPERSION; ENSEMBLE FORECASTING; ERROR ANALYSIS; EXPERIMENTAL STUDY; FILTER; MONITORING SYSTEM; NUCLEAR POWER PLANT; NUMERICAL MODEL; RADIOACTIVE POLLUTION; RADIOACTIVITY; SPATIAL DISTRIBUTION; TRACER;

ARTICLE; ATMOSPHERIC RADIOACTIVITY; ATMOSPHERIC TRANSPORT; ENVIRONMENTAL MONITORING; ENVIRONMENTAL RELEASE; FORECASTING; MATHEMATICAL COMPUTING; MODEL; PRIORITY JOURNAL; RADIATION MEASUREMENT; TECHNIQUE;

NEPTUNIA;

EID: 80052627007     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2011.08.024     Document Type: Article

References (34)

1. Amodei M., Stein J. Deterministic and fuzzy verification methods for a hierarchy of numerical models. Meteorological Applications 2009, 16(2):191-203.
2. Beven K. Environmental Modelling: An Uncertain Future? 2009, Routledge.
3. Brandt J., Christensen J., Frohn L., Zlatev Z. Numerical modelling of transport, dispersion, and deposition - validation against ETEX-1, ETEX-2 and chernobyl. Environmental Modelling and Software 2000, 15(6-7 SPEC. ISS):521-531.
4. Brus D., Kempen B., Heuvelink G. Sampling for validation of digital soil maps. European Journal of Soil Science 2011, 62(3):394-407.
5. Davis L.S., Dacre H.F. Can dispersion model predictions be improved by increasing the temporal and spatial resolution of the meteorological input data?. Weather 2009, 64(9):232-237.
6. Doucet A., De Freitas N., Gordon N. Sequential Monte Carlo Methods in Practice 2001.
7. Ebert E. Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework. Meteorological Applications 2008, 15(1):51-64.
8. Eleveld H., Kok Y.S., Twenhofel C.J. Data assimilation, sensitivity and uncertainty analyses in the Dutch nuclear emergency management system: a pilot study. International Journal of Emergency Management 2007, 4(3):551-563.
9. The ETEX Project 1998, 108pp. EUR 181-43 EN. F. Girardi, G. Graziani, D. van Veltzen, S. Galmarini, S. Mosca, R. Bianconi, R. Bellasio, W. Klug (Eds.).
10. Galmarini S., Bianconi R., Addis R., Andronopoulos S., Astrup P., Bartzis J.C., Bellasio R., Buckley R., Champion H., Chino M., D'Amours R., Davakis E., Eleveld H., Glaab H., Manning A., Mikkelsen T., Pechinger U., Polreich E., Prodanova M., Slaper H., Syrakov D., Terada H., Van der Auwera L. Ensemble dispersion forecasting-part ii: application and evaluation. Atmospheric Environment 2004, 38(28):4619-4632. Sep.
11. Gering F. Correction of deposition predictions with data assimilation. Kerntechnik 2007, 72(4):222-225.
12. Gordon N., Salmond D., Smith A. Novel approach to nonlinear/non-gaussian bayesian state estimation. Radar and Signal Processing, IEE Proceedings F 1993, 140(2):107-113. Aug.
13. Haupt S., Beyer-Lout A., Long K., Young G. Assimilating concentration observations for transport and dispersion modeling in a meandering wind field. Atmospheric Environment 2009, 43(6):1329-1338.
14. Hiemstra, P.H., Karssenberg, D., van Dijk, A., de Jong, S.M., Using the particle filter for nuclear decision support. Submitted to Environmental Modelling and Software.
15. Iba Y. Population Monte Carlo algorithms. Transactions of the Japanese Society for Artificial Intelligence 2001, 16(2):279-286.
16. Karssenberg D., Schmitz O., Salamon P., de Jong K., Bierkens M. A software framework for construction of process-based stochastic spatio-temporal models and data assimilation. Environmental Modelling and Software 2010, 25(4):489-502.
17. Kitagawa G. Monte Carlo filter and smoother for non-gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 1996, 5(1):1-25.
18. Klaas M., de Freitas N., Doucet A. Towards practical N2 Monte Carlo: the marginal particle filter. Proceedings from the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI-05) 2005.
19. Kovalets I., Tsiouri V., Andronopoulos S., Bartzis J. Improvement of source and wind field input of atmospheric dispersion model by assimilation of concentration measurements: method and applications in idealized settings. Applied Mathematical Modelling 2009, 33(8):3511-3521.
20. Liu J., Chen R. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 1998, 93(443):1032-1044.
21. Musso C., Oudjane N., Grand F.L. Sequential Monte-Carlo Methods in Practice 2001, Springer-Verlag, Berlin, Ch. Improving regilarized particle filter pp. 247-271.
22. Nodop K., Connolly R., Girardi F. The field campaigns of the European tracer experiment (ETEX): overview and results. Atmospheric Environment 1998, 32(24):4095-4108. Dec.
23. Pelliccioni A., Tirabassi T. Air dispersion model and neural network: a new perspective for integrated models in the simulation of complex situations. Environmental Modelling and Software 2006, 21(4):539-546.
24. Pelliccioni A., Gariazzo C., Tirabassi T. A neural net-air dispersion model validation study using the Indianapolis urban data set. International Journal of Environmental Pollution 2010, 40(1-3):70-84.
25. Pitt M.K., Shephard N. Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association 1999, 94(446):590-599. http://www.jstor.org/stable/2670179.
26. Politis K., Robertson L. Bayesian updating of atmospheric dispersion after a nuclear accident. Journal of the Royal Statistical Society. Series C: Applied Statistics 2004, 53(4):583-600.
27. Risfic B., Arulampalam S., Gordon N. Beyond the Kalman Filter: Particle Filters for Tracking Applications 2004, Artech House, Norwell, Massachusetts.
28. Rojas-Palma C., Madsen H., Gering F., Puch R., Turcanu C., Astrup P., Müller H., Richter K., Zheleznyak M., Treebushny D., Kolomeev M., Kamaev D., Wynn H. Data assimilation in the decision support system rodos. Radiation Protection Dosimetry 2003, 104(1):31-40.
29. Simon D. Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches 2006, John Wiley & Sons.
30. van Leeuwen P. Particle filtering in geophysical systems. Monthly Weather Review 2009, 137(12):4089-4114.
31. Verver G., De Leeuw F. An operational puff dispersion model. Atmospheric Environment. Part A. General Topics 1992, 26(17):3179-3193. Dec.
32. Wendum D. Three long-range transport models compared to the ETEX experiment: a performance study. Atmospheric Environment 1998, 32(24):4297-4305.
33. Zheng D., Leung J., Lee B. Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter. Atmospheric Environment 2009, 43(12):2005-2011.
34. Zheng D., Leung J., Lee B. An ensemble Kalman filter for atmospheric data assimilation: application to wind tunnel data. Atmospheric Environment 2010, 44(13):1699-1705.