Correction of daily precipitation data of ITPCAS dataset over the Qinghai-Tibetan Plateau with KNN model

As the meteorological stations in the Qinghai-Tibetan plateau (QTP) are scarcely and unevenly distributed, daily precipitation datasets generated from observation data and remote sensing inversion models are not accurate. The data accuracy can be improved by environmental and meteorological factors. This study selected k-Nearest Neighbor (KNN), a machine learning model, to correct the commonly used ITPCAS precipitation data over the QTP by establishing the relationship between daily precipitation and environmental (elevation, slope, aspect, vegetation) as well as meteorological factors (air temperature, humidity, wind speed). Error analysis shows that the KNN-corrected ITPCAS precipitation is more accurate than the original one. The spatial distribution of the corrected ITPCAS precipitation agrees well with the precipitation distribution pattern of the QTP. The error distribution of the corrected ITPCAS precipitation shows significant seasonal and regional characteristics.