STUDY ON CORRECTION OF DAILY PRECIPITATION DATA OF THE QINGHAI-TIBETAN PLATEAU WITH MACHINE LEARNING MODELS

The daily precipitation datasets of the Qinghai-Tibetan plateau (QTP) are mainly assimilated from remote sensing products and in-situ observations. The accuracy of those datasets needs further improvement with environmental and meteorological factors. This paper selected the related environmental and meteorological factors as input; k-Nearest Neighbor (KNN), Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Multinomial Log-linear Models (MLM) and Artificial Neural Networks (ANN) as correction models; 112 upscaled daily precipitation observations from the standard meteorological stations as ground truth to correct the commonly used ITPCAS and CMORPH daily precipitation of the QTP. Results show that the KNN model has the highest correction accuracy. The distribution of the corrected ITPCAS precipitation is nearer to the spatial pattern of the precipitation over the QTP than the corrected CMORPH precipitation. The correction accuracy is influenced by the precipitation distribution pattern of the original dataset.