Abstract
Incomplete climate records pose a major challenge to decision makers that utilize climate data as one of their main inputs. In this study, different climate data infilling methods (arithmetic averaging, inverse distance weighting, UK traditional, normal ratio and multiple regression) were evaluated against measured daily minimum and maximum temperatures. Eight target stations that are evenly distributed in Limpopo province, South Africa, were used. The objective was to recommend the best approach that results in lowest errors. The optimum number of buddy/neighboring weather stations required for best estimate for each of the approaches was determined. The evaluation indices employed in this study were the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), accuracy rate (AR) and mean bias error (MBE). The results showed high correlation (r > 0.92) for all the stations, different methods and varying number of neighboring stations utilised. The MAE [RMSE] for the best performing methods (multiple regression and UK traditional) of estimating daily minimum temperature and maximum temperature was less than 1.8 °C [2.3 °C] and 1.0 °C [1.6 °C], respectively. The AR technique showed the MR method as the best approach of estimating daily minimum and maximum temperatures. The other recommended methods are the UK traditional and normal ratio. The MBEs for the arithmetic averaging and inverse-distance weighing techniques are large, indicating either over- or underestimating of the air temperature in the province. Based on the low values for the error estimating statistics, these data infilling methods for daily minimum and maximum air temperatures using neighboring stations data can be utilised to complete the datasets that are used in various applications.
Highlights
Climate monitoring is a crucial exercise that is carried out by national meteorological and hydrological services, government agencies and international bodies [1]
The results of the analysis will be shown for all the methods per station, categorized according to the statistical indices used to compare measured data with estimated data
It depicted from the results that an increase in the number of stations used to estimate daily values gives rise to a slight increase of r in most stations
Summary
Climate monitoring is a crucial exercise that is carried out by national meteorological and hydrological services, government agencies and international bodies [1]. In agriculture, it can be used to delineate a portion of land that is suitable to plant a particular crop, the right timing of planting and harvesting, and crop yield estimation [3]. Climate data can be utilized to estimate the impact of climate hazards on agricultural production [4,5]. Climate data can be utilized to model both runoff and groundwater levels and assist in design of drainage systems [6,7,8], and historical data can be used to design drainage systems in civil engineering [9]. Historical climate data contain gaps, which usually increase with the length of the Climate 2019, 7, 86; doi:10.3390/cli7070086 www.mdpi.com/journal/climate
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