Spatial interpolation of rain gauge data is important in forcing ofhydrological simulations or evaluation of weather predictions, forexample.This paper investigates the application of statisticaldistance, like one minus common variance of observation time series, between data sitesinstead of geographical distance in interpolation. Here, as a typicalrepresentative of interpolation methodsthe inverse distance weighting interpolation is applied and the test data is dailyprecipitation observed in Austria. Choosing statistical distanceinstead of geographical distance in interpolation of availablecoarse network observations to sites of adenser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinctperformance enhancement is in or close to mountainous terrain. Therefore,application ofstatistical distance in the inverse distance weighting interpolation or insimilar methods can parsimoniously densify the currently available observationnetwork.Additionally, the success further motivates search for conceptualrain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.
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