Bias correction of ERA-Interim reanalysis temperature for the Qilian Mountains of China

Air temperature is the primary indicator of climate change. Reanalysis products are important datasets for estimates over high-elevation areas with few meteorological stations. However, they contain biases in observations, so a bias correction required to enhance accuracy modeling predictions. In this study, we used lapse-rate method correct ERA-Interim reanalysis-temperature data Qilian Mountains China from 1979 2017. These lapse rates were based on observations (Γ Obs ) and model internal vertical derived different pressure levels ERA ). The results showed that warm periods larger than those cold periods. Both original corrected can significantly capture warming trend exhibited by observations. general, rate was reliable correcting ERA-interim data. Although Γ performed best correction, it depends heavily density ground observation stations not appropriate remote low coverage. Correction methods shown be will especially applicable mountainous Our contribute improvement quality change effects risks environment human health.