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| Titel |
Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland |
| VerfasserIn |
R. Schiemann, R. Erdin, M. Willi, C. Frei, M. Berenguer, D. Sempere-Torres |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 5 ; Nr. 15, no. 5 (2011-05-19), S.1515-1536 |
| Datensatznummer |
250012786
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| Publikation (Nr.) |
 copernicus.org/hess-15-1515-2011.pdf |
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| Zusammenfassung |
Modelling spatial covariance is an essential part of all
geostatistical methods. Traditionally, parametric semivariogram models are fit
from available data. More recently, it has been suggested to use nonparametric
correlograms obtained from spatially complete data fields.
Here, both estimation techniques are compared.
Nonparametric correlograms are shown to have a substantial
negative bias. Nonetheless, when combined with the sample variance of
the spatial field under consideration, they yield an estimate of the
semivariogram that is unbiased for small lag distances. This justifies
the use of this estimation technique in geostatistical applications.
Various formulations of geostatistical combination (Kriging) methods
are used here for the construction of hourly precipitation grids for
Switzerland based on data from a sparse realtime network of
raingauges and from a spatially complete radar composite.
Two variants of Ordinary Kriging (OK) are used to interpolate the
sparse gauge observations. In both OK variants, the radar data
are only used to determine the semivariogram model. One variant relies on
a traditional parametric semivariogram estimate, whereas the
other variant uses the nonparametric correlogram. The variants
are tested for three cases and the impact of the semivariogram
model on the Kriging prediction is illustrated.
For the three test cases, the method using nonparametric correlograms
performs equally well or better than the traditional method, and at
the same time offers great practical advantages.
Furthermore, two variants of Kriging with external drift (KED) are
tested, both of which use the radar data to estimate
nonparametric correlograms, and as the external drift variable.
The first KED variant has been used previously for geostatistical
radar-raingauge merging in Catalonia (Spain). The second variant
is newly proposed here and is an extension of the first. Both
variants are evaluated for the three test cases as well as an
extended evaluation period. It is found that both methods yield
merged fields of better quality than the original radar field
or fields obtained by OK of gauge data. The newly suggested KED
formulation is shown to be beneficial, in particular in
mountainous regions where the quality of the Swiss radar
composite is comparatively low.
An analysis of the Kriging variances shows that none of the
methods tested here provides a satisfactory uncertainty
estimate. A suitable variable transformation is expected to
improve this. |
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