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| Titel |
Optimising predictor domains for spatially coherent precipitation downscaling |
| VerfasserIn |
S. Radanovics, J.-P. Vidal, E. Sauquet, A. Ben Daoud, G. Bontron |
| 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 |
250062665
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| Zusammenfassung |
| Relationships between local precipitation (predictands) and large-scale circulation
(predictors) are used for statistical downscaling purposes in various contexts, from
medium-term forecasting to climate change impact studies. For hydrological purposes like
flood forecasting, the downscaled precipitation spatial fields have furthermore to be coherent
over possibly large basins. This thus first requires to know what predictor domain can be
associated to the precipitation over each part of the studied basin. This study addresses this
issue by identifying the optimum predictor domains over the whole of France, for a specific
downscaling method based on a analogue approach and developed by Ben Daoud et al.
(2011).
The downscaling method used here is based on analogies on different variables:
temperature, relative humidity, vertical velocity and geopotentials. The optimum
predictor domain has been found to consist of the nearest grid cell for all variables
except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have
been found to be sensitive to the target location by Obled et al. (2002), and the
present study thus focuses on optimizing the domains of this specific predictor over
France.
The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608
climatologically homogeneous zones in France using the ERA-40 reanalysis data for the
large-scale predictors and local precipitation from the Safran near-surface atmospheric
reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the
Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the
different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25
best analogue days found with the statistical downscaling method. Rectangular predictor
domains of different sizes, shapes and locations are tested, and the one that leads to
the smallest CRPS for the zone in question is retained. The resulting optimised
domains are analysed for defining regions where neighbouring zones have equal or
similar predictor domains and identifying which French river basins contain zones
associated with different predictor domains, i.e. are exposed to different meteorological
influences.
The above analysis will be used (1) to extend the statistical downscaling method of Ben
Daoud et al. (2011) to the whole of France and (2) to develop it further in order to
achieve spatially coherent forecasts while preserving the predictive skill on the local
scale.
Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011).
Precipitation forecasting through an analog sorting technique: a comparative
study. Advances in Geosciences, 29:103–107. doi: 10.5194/adgeo-29-103-2011
Obled, C., Bontron, G., and Garçon, R. (2002). Quantitative precipitation forecasts:
a statistical adaptation of model outputs through an analogues sorting approach.
Atmospheric Research, 63(3-4):303–324. doi: 10.1016/S0169-8095(02)00038-8
Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M.
(2010) A 50-year high-resolution atmospheric reanalysis over France with
the Safran system. International Journal of Climatology, 30:1627–1644. doi:
10.1002/joc.2003 |
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