Abstract
Climate data recorded by national meteorological agencies is either incomplete or faulty for some periods due to a number of reasons. Multi-functional utilization of climate data in complete form necessitates the filling of these gaps. In this study an inverse distance weighting (IDW) method was used to estimate rainfall utilizing neighbouring station data in the Free State Province of South Africa. Six weather stations evenly distributed across the province, and with data for 1950 to 2008, were used to evaluate this patching IDW approach at daily and dekadal time steps. Coefficient of determination (r 2 ), mean absolute error (MAE) and mean bias error (MBE) were the statistics used in the assessment. Firstly, the study conducted a sensitivity analysis of the IDW exponent (p) which showed that the best results are obtained when p is either 2 or 2.5. The estimated values at all six stations were highly correlated with the measured rainfall data with an overall r 2 value exceeding 0.70 for both daily and dekadal estimates. MAE showed low miscalculations with values with an average of 1 mm per day and 4.4 mm per dekad. MBE was very low for both daily and dekadal evaluations but the disaggregated data showed underestimation of the IDW mostly for daily rainfall exceeding 10 mm. Thus, IDW methodology proved to be an acceptable approach for estimating both daily and dekadal rainfall in the Free State Province. Keywords : estimation, missing data, neighbouring stations
Highlights
Measuring and archiving of different weather elements like rainfall, temperature or humidity is an important exercise
Results for each of the p values per target station were compared with observed rainfall data using coefficient of determination (r2), mean absolute error (MAE) and mean bias error (MBE)
Rankings for r2 are lower for exponents between 1 and 2.5 while high values are evident for exponents between 3 and 5
Summary
Measuring and archiving of different weather elements like rainfall, temperature or humidity is an important exercise. The widely-used patching methods include: closest station, simple arithmetic averaging, inverse distance weighting, multiple regression and normal ratio (Tang et al, 1996; Makhuvha et al, 1997; Xia et al, 1999; Xia et al, 2001). In utilizing the closest station method, the nearest weather station with data corresponding to the period of concern is identified and missing values are either replaced directly by the value at the neighbour station or adjusted by a factor from the ratio of long-term means between the two stations (Xia et al, 2001). The study aimed to evaluate the use of the inverse distance weighting (IDW) method of estimating rainfall as a possible means of patching missing or faulty rainfall measurements. Studies by Tang et al (1996) revealed that the IDW method has the potential of estimating missing rainfall values with minimum inaccuracy
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