Distance in spatial interpolation of daily rain gauge data

By Bodo Ahrens. 2005

191 citations recorded by the source corpus.

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ddb411d6579c07cfec7e06253ff5feebef3ea33a

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Citations in the corpus

Analysis of stock market indices through multidimensional scaling (2011, methodology): “The Sammon criterion revealed good results and is adopted in this work [1,9,15].”
Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea (2010, methodology): “IDW method can be simply implemented within the GIS software and is useful for predicting a value at an unmeasured location directly from the measured values even if the data density is not sufficient (Coley and Clabburn 2005; Guo and Richard 2004; Ahrens 2006 ).”
Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach (2010, methodology): “Alternatively, the use of correlation coefficients between data series as weights has been investigated on a daily basis (Teegavarapu and Chandramouli, 2005; Ahrens, 2006), and generally found to outperform distance-based schemes.”