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Statistical downscaling of atmospheric variables: for datasparse, glaciated mountain sites,Used
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How much information about localscale weather can be gained from largescale atmospheric models? This work investigates downscaling methods for temporally highresolution, normally distributed target variables. The performed procedures, based on model output statistics, are particularly appropriate when only shortterm observations (i.e., fewyears) are available. A case study is shown, with air temperature and humidity records from automatic weather stations in the Cordillera Blanca (Peru) representing the target variables, and reanalysis data from different institutions the predictors. The goal is to extend information available from the existing shortterm observations into the past, in order to provide a better understanding about highresolution atmospheric variability in the Cordillera Blanca. In principle, the downscaling techniques are portable to different sites or variables, as long as a few years of observational data are available for training. Yet we recommend their use primarily for skill assessment of largescale predictors, and the subsequent application of results in impact studies only if high skill is demonstrated.
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