 This research paper investigates the use of machine learning techniques to correct the underestimation of precipitation measurements in high-altitude regions. The authors selected the Yaku Station in Tibet as their study site, which has been found to experience a significant underestimation of precipitation compared to other locations. They used various meteorological and remote-sensing data sources to develop a machine learning model to correct the underestimation. Their results showed that the machine learning model outperformed the traditional statistical method in terms of accuracy metrics and frequency distributions. Additionally, they tested the transferability of the model by comparing it against two other sites in Norway and the US. The results indicated that the machine learning model was able to accurately predict precipitation levels in all three sites. Finally, the authors concluded that the use of remote-sensing data, such as GSMA-P, can be used to replace the role of in situ meteorological observations in precipitation correction. This article was authored by Hong Yi Li, Yang Zhang, Hua Jin Lei, and others. We are article.tv, links in the description below.