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| Titel |
Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach |
| VerfasserIn |
L. Gerlitz, O. Conrad, J. Böhner |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 6, no. 1 ; Nr. 6, no. 1 (2015-02-27), S.61-81 |
| Datensatznummer |
250115413
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| Publikation (Nr.) |
 copernicus.org/esd-6-61-2015.pdf |
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| Zusammenfassung |
The heterogeneity of precipitation rates in high-mountain regions is not
sufficiently captured by state-of-the-art climate reanalysis products due to
their limited spatial resolution. Thus there exists a large gap between the
available data sets and the demands of climate impact studies. The presented
approach aims to generate spatially high resolution precipitation fields for
a target area in central Asia, covering the Tibetan Plateau and the adjacent
mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric
situations and modified by local-scale topographic characteristics, the
statistical downscaling approach estimates local-scale precipitation rates
as a function of large-scale atmospheric conditions, derived from the
ERA-Interim reanalysis and high-resolution terrain parameters. Since the
relationships of the predictor variables with local-scale observations are
rather unknown and highly nonlinear, an artificial neural network (ANN) was
utilized for the development of adequate transfer functions. Different
ANN architectures were evaluated with regard to their predictive performance.
The final downscaling model was used for the cellwise estimation of monthly
precipitation sums, the number of rainy days and the maximum daily
precipitation amount with a spatial resolution of 1 km2. The
model was found to sufficiently capture the temporal and spatial variations
in precipitation rates in the highly structured target area and allows for a
detailed analysis of the precipitation distribution. A concluding
sensitivity analysis of the ANN model reveals the effect of the atmospheric
and topographic predictor variables on the precipitation estimations in the
climatically diverse subregions. |
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