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.
Citation
Post Graduate Diploma in MeteorologySponsorhip
University of NairobiPublisher
Department of Meteorology University of Nairobi