A spatial interpolation program for grid analysis and estimating missing meteorological data

Abstract

This study was geared towards developing a user-friendly spatial interpolation program for grid analysis and estimation of meteorological data. The methods of interpolation used include Lagrangian, Kriging and Inverse Distance Weighting (IDW). For IDW three different weighting factor was used namely: the Gaussian weighting factor, weighting with radius of influence and the inverse square-weighting factor. The program can be used as a tool: 1). to provide the observed data to grid points for the model input 2.) for comparing the model output with the observed data by interpolating the gridded data to station points 3.) to estimate missing meteorological data at station location usmg interpolation techniques Except Lagrangian the performance of 'the interpolation methods was evaluated usmg observed rainfall data over Ethiopia. The result showed that although some interpolation methods performed better than others, it was generally noted that all methods perform well in rainy season than dry season. For example Kriging was better than the other methods during the long rainy season while the Inverse Distance Weighting with radius of influence perform better during the short rainy season. It was concluded that an interpolation method be chosen for the specific task.