Data Scarcity or low Representativeness?: What hinders accuracy and precision of spatial interpolation of climate data?

Abstract

Data scarcity is a major scientific challenge for accuracy and precision of spatial interpolation of climatic fields, especially in climatestressed developing countries. Methodologies have been suggested for coping up with data scarcity but data have rarely been checked for their representativeness of corresponding climatic fields. Here, influences of number and representativeness of climate data on accuracy and precision of their spatial interpolation were investigated and compared. Two precipitation and temperature indices were computed for a long time series in Bangladesh, which is a data scarce region. The representativeness was quantified by dispersion in the data and the accuracy and precision of spatial interpolation were computed by four commonly used error statistics derived through cross-validation. The precipitation data showed very little and sometimes null representativeness whereas the temperature data showed very high representativeness of the corresponding fields. Consequently, interpolated precipitation surfaces showed little accuracy and precision whereas temperature surfaces showed high accuracy and precision despite the scarce data. The results indicate that representativeness of climate data, i.e. variability of climate phenomenon, is more crucial than the number of data for accuracy and precision of spatial interpolation and should be treated with higher importance.