Climate Dynamics

, Volume 51, Issue 9–10, pp 3635–3652 | Cite as

Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

  • Katja ReinhardtEmail author
  • Cyrus Samimi


While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components \(\overrightarrow{u}\) and \(\overrightarrow{v}\) in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of \(\overrightarrow{u}\) and \(\overrightarrow{v}\). In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results.


Spatial interpolation Wind Central Asia Complex topography 



By the financial support of the University of Bayreuth, the University of Bayreuth Graduate school and DAAD it was possible to organize and finance field work in Central Asia. Thanks for grammar and spelling correction to Thomas Kolb and Daniela Kretz. The authors also would like to thank the two anonymous reviewers for their helpful comments and suggestions that significantly improved the quality of this paper.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeographyUniversity of BayreuthBayreuthGermany
  2. 2.Bayreuth Center of Ecology and Environmental Research (BAYCEER)University of BayreuthBayreuthGermany

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