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| Titel |
Sensitivity of Z-R relations to aggregation |
| VerfasserIn |
Maximiliano Sassi, Hidde Leijnse, Remko Uijlenhoet |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250086291
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| Publikation (Nr.) |
 EGU/EGU2014-125.pdf |
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| Zusammenfassung |
| Rain radars routinely rely on power functions to retrieve rain rates based on radar
reflectivities measured at widely ranging spatial and temporal resolutions. The nonlinear
nature of power functions may complicate the comparison of rainfall estimates employing
reflectivities measured at different scales, as transforming reflectivity Z into rain rate R using
relations that have been derived for other spatial and/or temporal scales results in a bias. We
investigate the sensitivity of such power functions, known as Z-R relations, to spatial and
temporal aggregation using high-resolution reflectivity fields measured with an
X-band radar, for five rainfall events. Existing Z-R relations were employed to
investigate the behavior of aggregated Z-R relations with scale, the aggregation
bias and the variability of the estimated rain rate. The prefactor and the exponent
of aggregated Z-R relations systematically diverge with scale, showing breaks
that are event-dependent in the temporal domain and nearly constant in the spatial
domain. The systematic behavior of prefactors and exponents with scale can be
described with prescribed functions, notably power, linear and exponential functions.
The systematic error associated with aggregation bias at a given scale can be of
the same order of magnitude as the corresponding random error associated with
intermittent sampling. The predictable bias can be constrained by including information
about the variability of Z within a certain scale of aggregation, and is captured by
simple functions of the coefficient of variation. Several descriptors of spatial and
temporal variability of the reflectivity field show strong links with aggregation bias.
Prefactors in Z-R relations can be related to multi-fractal properties of the rainfall
field whereas scale dependencies in the exponent may be interpreted as a spurious
artifact of the regression procedure. Shape factors of both bounded bilinear and
unbounded exponential variogram models are insensitive to aggregation for moments of
order higher than unity, however, the structural analysis of spatial rainfall reveals a
scaling break at spatial lags comparable with the maximum scale of aggregation
imposed by the limited spatial coverage of the radar dataset analyzed. Our results
support the good practice of attaching ranges of validity to nonlinear calibrations. |
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