Time-independent bias correction methods compared with gauge adjustment methods in improving radar-based precipitation estimates

Abstract

ABSTRACT Quantitative precipitation estimates obtained by weather radars are prone to errors. Gauge-based observations are known to be complementary data for mitigating radar-based estimation. This study investigates, implements, and validates four gauge adjustment and four time-independent bias correction methods over all the operating radars of Turkey during the years 2014–2019. The objective is to investigate the performance of methods over large regions using long time series, where such implementations are rarely done. The results provide detailed information regarding the performance of these methods in different spatiotemporal scenarios. Gauge adjustment methods can mitigate the mean error and/or the dispersion of the error in the original radar data. On average, gauge adjustment methods reduce the mean error from −0.81 to −0.05 mm/h, the root mean squared error from 2.63 to 1.50 mm/h, and the correlation coefficient from 0.53 to 0.83. Time-independent methods can improve the mean error from −0.81 to −0.08 mm/h.