HR: 10:35h
AN: A12B-02    [Abstracts]
TI: Residence Time Maps for Inverse Modeling in Complex Terrain
AU: * Ubl, S
EM: sandy.ubl@empa.ch
AF: Swiss Federal Laboratories for Materials Testing and Research, Ueberlandstrasse 129, Duebendorf, 8600 Switzerland
AU: Folini, D
EM: doris.folini@empa.ch
AF: Swiss Federal Laboratories for Materials Testing and Research, Ueberlandstrasse 129, Duebendorf, 8600 Switzerland
AU: Kaufmann, P
EM: pirmin.kaufmann@meteoswiss.ch
AF: MeteoSwiss, Kraehbuehlstrasse 58, Zuerich, 8044 Switzerland
AB: We will use inverse modeling to estimate the distribution and magnitude of European emissions of halogenated green house gases. Simulated annealing will be employed to combine measurement data with modeled residence time maps of Europe. The latter are obtained from a backward Lagrangian Particle Dispersion Model (LPDM). The measurements are taken at Jungfraujoch, a remote site in the northern part of the Swiss alps at 3580m asl. From a measurement point of view, the advantage to sample at Jungfraujoch lies in the possibility to sample both background air and air polluted by European emissions. Due to its remote and elevated position Jungfraujoch is not influenced by emissions nearby the station. From a modeling point of view, Jungfraujoch is demanding, since the measurement station is situated in a very rugged terrain. The quality of the modeled residence time maps is however, of central importance as they are an essential input to the inversion technique. In this context, several aspects have to be checked. One point is the quality of the used meteo fields. Despite the 7km x 7km grid spacing of the MeteoSwiss alpine model (aLMo) we use, the topography is still only approximately captured. The residence time maps also depend on the steering parameters of the LPDM, in particular the starting height, the wind field resolution, the model time step, the run time, and the number of released particles. We examine the sensitivity of the residence time maps on these input parameters. We link the resulting residence time maps with measurements to choose an optimized set of steering parameters.
DE: 3329 Mesoscale meteorology
DE: 3337 Numerical modeling and data assimilation
DE: 3379 Turbulence
DE: 3307 Boundary layer processes
DE: 0345 Pollution--urban and regional (0305)
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting