Comparison of Six GIS-Based Spatial Interpolation Methods for Estimating Air Temperature in Western Saudi Arabia

Abstract

Six GIS-based spatial interpolation methods were compared to determine their suitability for estimating mean monthly air temperature (MMAT) surfaces, from data recorded at nearly 31 meteorological stations representing different climatic conditions in Western Saudi Arabia. The eventual purpose of producing such surfaces is to help making air temperature data be available for a wide variety of scientific uses. The interpolation techniques included four deterministic methods (Inverse Distance Weighted, Global Polynomial, Local Polynomial, and Radial Basis Function (Thin-Plate Spline) and two geostatistical methods (Ordinary Kriging, and Universal Kriging). Quantitative assessment of the continuous surfaces showed that there was a large difference between the accuracy of the six interpolation methods and that the geostatistical methods were superior to deterministic methods. This work also revealed systematic spatial and temporal variations of temperatures in western Saudi Arabia.

Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada

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Abundance and morphometry changes across the high‐mountain lake‐size gradient in the tropical Andes of Southern Ecuador

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A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

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