Spatial interpolation techniques: their applications in regionalizing climate-change series and associated accuracy evaluation in Northeast China

An accurate gridded climatological temperature data-set can be a reliable basis for studying the issues that concern climate change, natural disasters, among others. In this study, the climate standard value data and annual observed data collected by 104 meteorological sites in Northeast China from 1971 to 2000 are used to interpolate the annual mean air temperature of the region with different spatial interpolation techniques. Efforts have also been made to verify the accuracy of the results generated by each interpolation method with error indicator, temperature characteristic value and temperature variation curves. The results show at the spatial scale, the partial thin-plate smoothing spline (PTPSS) scheme generates the most desirable temperature interpolations with a root-mean-square error at 0.34 °C and an average standard error at 0.52 °C. At the temporal scale, the PTPSS-based 1971–2000 temperature curves agree the best with the gridded temperature curves, with the correlation coefficients of means, minimums and maximums between the two being larger than 0.9. Meanwhile, the PTPSS produce the smallest error approaching zero among all the interpolation schemes tested in the study. In this context, the PTPSS scheme stands out as the most desirable interpolation method for the annual mean temperature in Northeast China.

Abstract licence: CC BY-NC

1. 9.

   Multivariable geostatistics in S: the gstat package · 2004

   Not checked

25 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.