Revised spatial weighting methods for estimation of missing rainfall data

Abstract

A complete daily rainfall dataset with no missing values is highly in demand for a variety of meteorological and hydrological purposes. In most situations, spatial interpolation techniques such as normal ratio and inverse distance methods are used for estimating missing rainfall values at a particular target station based on the available rainfall values recorded at the neighboring stations. Moreover, these two methods are found to be very useful in the case where the neighboring-stations are very close and highly correlated with the target stations. In this study, several modifications and improvements have been proposed to these methods in order to estimate the missing rainfall values at the target station using the information from the nearby stations. The methods have been tested with different percentages of missing rainfall values and also with a radius range of 75 km to 200 km. The result indicate that the performance of these modified methods improved the estimation of missing rainfall values at the target station based on the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).