Comparison of regression-based and combined versions of Inverse Distance Weighted methods for spatial interpolation of daily mean temperature data
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This paper focuses on the performance of two regression-based and one Inverse Distance Weighted (IDW) and two combined versions of IDW methods for interpolation of daily mean temperature at the Black Sea Region of Turkey. Simple linear regression (SLR) and multiple linear regression (MLR) are used as regression-based methods. Combinations of IDW with TLR (temperature lapse rate) and gradient plus inverse distance squared (GIDS) are used as combined versions of IDW. This study targets to compare five spatial interpolation methods based on RMSE (root-mean-square error) statistics of interpolation errors for daily mean temperatures from 1981 to 2012. In order to compare the interpolation errors of the five methods, the leave-one-out cross-validation method was applied over long periods of 32 years on 52 different sites. The algorithms of the five interpolation methods’ codes were written in MATLAB by the authors of the paper.
KeywordsInverse Distance Weighted Temperature lapse rate Combined version of IDW with TLR Gradient plus inverse distance squared (GIDS) Simple linear regression (SLR) Multiple linear regression (MLR)
The authors would like to thank the Turkish State Meteorological Service for providing the data for this study.
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