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| Titel |
Empirical Downscaling of Windfields for Hydrodynamic Modeling of Lakes |
| VerfasserIn |
Dirk Schlabing, András Bárdossy |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250041889
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| Zusammenfassung |
| Aiming for the fine spatial and temporal discretization required for hydrodynamic modeling
of lakes, wind vectors from General Circulation Models (GCMs) are downscaled
stochastically. Instead of dynamic downscaling methods, empirical ones are used
here because they are more likely to reproduce the variability and the frequency of
extreme values existent in natural winds. Generally GCMs are good predictors of large
scale features. Their output is interpreted as the driving force for a process that
results in the observed measurements on surface. Using circulation patterns it is
possible to discern periods during which such a strong driving force is expected, from
those where large scale conditions are not likely to have a higher influence than
local phenomena. With the help of dynamical downscaling methods, intermediately
discretized wind fields can be generated and compared to the measured data. This
overall procedure leads to statistical transfer functions that link the GCM output to
observations and can therefore be used to downscale the output of GCMs run for climate
scenarios to get input for hydrodynamic modeling of lakes under changing climatic
conditions.
Determining the statistical relationships carries with it the necessity of homogenized datasets.
There are however significant differences between the results of the NCEP/NCAR and the
ERA-40 re-analysis model concerning the wind components in 850hPa height as well as
discontinuities within the ERA-40 time series. Whereas the latter ones can be attributed to
changes towards a higher abundance of vertically distinguishing measurements from 1979 on,
the former are related to different variances. The different variances in the wind
components lead to very different mean kinetic wind energies. It is shown that the
problem of differing distributions can be overcome by using quantile transformations. |
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