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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 11 | Copyright
Nat. Hazards Earth Syst. Sci., 18, 2825-2840, 2018
https://doi.org/10.5194/nhess-18-2825-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Nov 2018

Research article | 02 Nov 2018

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

Silvia Terzago, Elisa Palazzi, and Jost von Hardenberg Silvia Terzago et al.
  • Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, Turin, Italy

Abstract. Stochastic rainfall downscaling methods usually do not take into account orographic effects or local precipitation features at spatial scales finer than those resolved by the large-scale input field. For this reason they may be less reliable in areas with complex topography or with sub-grid surface heterogeneities. Here we test a simple method to introduce realistic fine-scale precipitation patterns into the downscaled fields, with the objective of producing downscaled data more suitable for climatological and hydrological applications as well as for extreme event studies. The proposed method relies on the availability of a reference fine-scale precipitation climatology from which corrective weights for the downscaled fields are derived. We demonstrate the method by applying it to the Rainfall Filtered Autoregressive Model (RainFARM) stochastic rainfall downscaling algorithm.

The modified RainFARM method is tested focusing on an area of complex topography encompassing the Swiss Alps, first, in a perfect-model experiment in which high-resolution (4km) simulations performed with the Weather Research and Forecasting (WRF) regional model are aggregated to a coarser resolution (64km) and then downscaled back to 4km and compared with the original data. Second, the modified RainFARM is applied to the E-OBS gridded precipitation data (0.25° spatial resolution) over Switzerland, where high-quality gridded precipitation climatologies and accurate in situ observations are available for comparison with the downscaled data for the period 1981–2010.

The results of the perfect-model experiment confirm a clear improvement in the description of the precipitation distribution when the RainFARM stochastic downscaling is applied, either with or without the implemented orographic adjustment. When we separately analyze grid points with precipitation climatology higher or lower than the median calculated over the neighboring grid points, we find that the probability density function (PDF) of the real precipitation is better reproduced using the modified RainFARM rather than the standard RainFARM method. In fact, the modified method successfully assigns more precipitation to areas where precipitation is on average more abundant according to a reference long-term climatology.

The results of the E-OBS downscaling show that the modified RainFARM introduces improvements in the representation of precipitation amplitudes. While for low-precipitation areas the downscaled and the observed PDFs are in good agreement, for high-precipitation areas residual differences persist, mainly related to known E-OBS deficiencies in properly representing the correct range of precipitation values in the Alpine region. The downscaling method discussed is not intended to correct the bias which may be present in the coarse-scale data, so possible biases should be adjusted before applying the downscaling procedure.

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This study proposes a modification to a stochastic downscaling method for precipitation, RainFARM, to improve the representation of the statistics of the daily precipitation at fine scales (1 km) in mountain areas. This method has been demonstrated in the Alps and it has been found to reconstruct small-scale precipitation distribution. It can be employed in a number of applications, including the analysis of extreme events and their statistics and hydrometeorological hazards.
This study proposes a modification to a stochastic downscaling method for precipitation,...
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