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| Titel |
Improving lidar-based mixing height measurements with radon-222 |
| VerfasserIn |
A. Griffiths, S. Chambers, S. Parkes, A. G. Williams, M. McCabe |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250060562
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| Zusammenfassung |
| We have found that near-surface hourly atmospheric radon-222 measurements can be
combined with elastic backscatter lidar data to obtain a higher quality time-series of mixing
height than is currently possible with lidar data alone.
The standard method of determining mixing heights from lidar observations relies on
algorithms which detect the contrast between relatively turbid aerosol-laden air within the
boundary layer and clear air above. However, this approach can be confounded by
meteorological conditions that lead to the formation of multiple aerosol layers within or
above the boundary layer, or when the contrast between boundary layer air and the overlying
air is weak. In such ambiguous circumstances, extra information would be helpful to choose
the appropriate mixing height.
Radon-222 has the properties—almost—of an ideal passive tracer emitted at a constant
rate from the surface. Assuming horizontal homogeneity, the near-surface concentration
time-series can be inverted to determine an effective mixing height, which is equal to the true
mixing height if the tracer is mixed uniformly throughout the boundary layer. A
time-series of effective mixing heights derived in this manner can then be used to choose
between lidar-derived candidates for mixing height in ambiguous meteorological
conditions.
This approach has the potential to extend the usefulness of lidar observations to
conditions where, at present, it is only marginally applicable, and to improve the performance
of automatic PBL height detection procedures. A time-series of mixing heights derived from
a combination of lidar and radon observations would have fewer gaps, and therefore be more
useful for applications such as model validation or pollution studies under a wider range of
meteorological conditions. |
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