Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-5135-2018
https://doi.org/10.5194/gmd-11-5135-2018
Development and technical paper
 | 
14 Dec 2018
Development and technical paper |  | 14 Dec 2018

Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and Cross-Appalachian Tracer Experiment (CAPTEX) observations – effect of including model uncertainties on source term estimation

Tianfeng Chai, Ariel Stein, and Fong Ngan

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Cited articles

Achim, P., Monfort, M., Le Petit, G., Gross, P., Douysset, G., Taffary, T., Blanchard, X., and Moulin, C.: Analysis of Radionuclide Releases from the Fukushima Dai-ichi Nuclear Power Plant Accident Part II, Pure Appl. Geophys., 171, 645–667, https://doi.org/10.1007/s00024-012-0578-1, 2014. a
Angevine, W. M., Jiang, H., and Mauritsen, T.: Performance of an Eddy Diffusivity-Mass Flux Scheme for Shallow Cumulus Boundary Layers, Mon. Weather Rev., 138, 2895–2912, https://doi.org/10.1175/2010MWR3142.1, 2010. a
Bieringer, P. E., Young, G. S., Rodriguez, L. M., Annunzio, A. J., Vandenberghe, F., and Haupt, S. E.: Paradigms and commonalities in atmospheric source term estimation methods, Atmos. Environ., 156, 102–112, https://doi.org/10.1016/j.atmosenv.2017.02.011, 2017. a
Bocquet, M.: Reconstruction of an atmospheric tracer source using the principle of maximum entropy. II: Applications, Q. J. Roy. Meteor. Soc., 131, 2209–2223, https://doi.org/10.1256/qj.04.68, 2005. a
Bocquet, M.: High-resolution reconstruction of a tracer dispersion event: application to ETEX, Q. J. Roy. Meteor. Soc., 133, 1013–1026, https://doi.org/10.1002/qj.64, 2007. a
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Short summary
While model predictions depend on release parameters, model uncertainties in inverse modeling should also vary with the source terms. In this paper, model uncertainties that will change with the source terms are introduced in a weak-constraint inverse modeling system. Tests using HYSPLIT model and CAPTEX observations show that adding such model uncertainty terms improves release rate estimates. A cost function normalization scheme introduced to avoid spurious solutions proves to be effective.