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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 1
Earth Syst. Dynam., 6, 61–81, 2015
https://doi.org/10.5194/esd-6-61-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Climate Change and Environmental Pressure: Adaptation and...

Earth Syst. Dynam., 6, 61–81, 2015
https://doi.org/10.5194/esd-6-61-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Feb 2015

Research article | 27 Feb 2015

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

L. Gerlitz, O. Conrad, and J. Böhner L. Gerlitz et al.
  • University of Hamburg, Institute of Geography, Bundesstraße 55, 20146 Hamburg, Germany

Abstract. 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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Short summary
In order to assess high-resolution precipitation fields for the Tibetan Plateau and the Himalayan Arc, a novel downscaling approach is presented which integrates traditional statistical downscaling and GIS-based terrain parameterization techniques. The approach enables a detailed analysis of the precipitation heterogeinity over the complex target area.
In order to assess high-resolution precipitation fields for the Tibetan Plateau and the...
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