Influence of spatial sampling and interpolation on estimates of air temperature change

Abstract

Through graphical and statistical analysis, three spherically based interpolation methods - inverse-distance weighting, triangulated surface patches, and thin-plate splines - are evaluated and compared using air temperature anomaly data. Analysis of differences between the three interpolation methods suggests that similar spatial patterns are produced, but with some regional disparities. Mean absolute differences between interpolation methods can be over 0.4°C for some sparse station networks and as low as 0.1°C for dense station networks. Station networks from the late 1800s produce average interpolation errors of nearly 0.5°C, errors that are similar in magnitude to spatial standard deviations of air temperature anomalies. Denser station networks, typical of the 1950s and 1960s, produce average interpolation errors as low as 0.2°C. -from Author