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| Titel |
Forecasting for a Lagrangian aircraft campaign |
| VerfasserIn |
A. Stohl, O. R. Cooper, R. Damoah, F. C. Fehsenfeld, C. Forster, E.-Y. Hsie, G. Hübler, D. D. Parrish, M. Trainer |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 4, no. 4 ; Nr. 4, no. 4 (2004-07-12), S.1113-1124 |
| Datensatznummer |
250001842
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| Publikation (Nr.) |
 copernicus.org/acp-4-1113-2004.pdf |
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| Zusammenfassung |
| A forecast system has been developed in preparation for an upcoming aircraft
measurement campaign, where the same air parcels polluted by emissions over
North America shall be sampled repeatedly as they leave the continent, during
transport over the Atlantic, and upon their arrival over Europe. This paper
describes the model system in advance of the campaign, in order to make the
flight planners familiar with the novel model output. The aim of a Lagrangian
strategy is to infer changes in the chemical composition and aerosol
distribution occurring en route by measured upwind/downwind differences.
However, guiding aircraft repeatedly into the same polluted air parcels
requires careful forecasting, for which no suitable model system exists to
date. This paper describes a procedure using both Eulerian-type (i.e.
concentration fields) and Lagrangian-type (i.e. trajectories) model output
from the Lagrangian particle dispersion model FLEXPART to predict the best
opportunities for a Lagrangian experiment. The best opportunities are defined
as being highly polluted air parcels which receive little or no emission
input after the first measurement, which experience relatively little mixing,
and which are reachable by as many aircraft as possible. For validation the
system was applied to the period of the NARE 97 campaign where approximately
the same air masses were sampled on different flights. Measured
upwind/downwind differences in carbon monoxide (CO) and ozone (O3)
decreased significantly as the threshold values used for accepting cases as
Lagrangian were tightened. This proves that the model system can successfully
identify Lagrangian opportunities. |
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