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. A Hybrid Single-Particle Lagrangian Integrated Trajectory version 4(HYSPLIT-4) inverse system that is based on variational dataassimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) isevaluated using the Cross-Appalachian Tracer Experiment (CAPTEX) datacollected from six controlled releases. For simplicity, the initial tests areapplied to release 2, for which the HYSPLIT has the best performance. Beforeintroducing model uncertainty terms that will change with source estimates,the tests using concentration differences in the cost function result insevere underestimation, while those using logarithm concentration differencesresult in overestimation of the release rate. Adding model uncertainty termsimproves results for both choices of the metric variables in the costfunction. A cost function normalization scheme is later introduced to avoidspurious minimal source term solutions when using logarithm concentrationdifferences. The scheme is effective in eliminating the spurious solutionsand it also helps to improve the release estimates for both choices of themetric variables. The tests also show that calculating logarithmconcentration differences generally yields better results than calculatingconcentration differences, and the estimates are more robust for a reasonablerange of model uncertainty parameters. This is further confirmed with nineensemble HYSPLIT runs in which meteorological fields were generated withvarying planetary boundary layer (PBL) schemes. In addition, it is found thatthe emission estimate using a combined TCM by taking the average or medianvalues of the nine TCMs is similar to the median of the nine estimates usingeach of the TCMs individually. The inverse system is then applied to theother CAPTEX releases with a fixed set of observational and model uncertaintyparameters, and the largest relative error among the six releases is 53.3 %.At last, the system is tested for its capability to find a single sourcelocation as well as its source strength. In these tests, the location andstrength that yield the best match between the predicted and the observedconcentrations are considered as the inverse modeling results. The estimatedrelease rates are mostly not as good as the cases in which the exact releaselocations are assumed known, but they are all within a factor of 3 for allsix releases. However, the estimated location may have large errors.

geoscientific model development

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