Evaluation and comparison of separated precipitation types from multisources data in the Chinese Tianshan mountainous region  

作　　者：YANG Chuanming LI Xuemei ZHANG Xu WU Jun LI Lanhai 

机构地区：[1]Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China [2]National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China [3]Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China [4]Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone,Urumqi 830011,China [5]Tianshan Snow cover and Avalanche Observation and Research Station of Xinjiang,Xinyuan 835800,China

出　　处：《Journal of Mountain Science》2025年第2期489-504,共16页山地科学学报(英文)

基　　金：financial support from the National Natural Sciences Foundation of China(42261026,and 42161025);the Open Foundation of Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone(XJYS0907-2023-01)。

摘　　要：Precipitation types primarily include rainfall,snowfall,and sleet,and the transformation of precipitation types has significant impacts on regional climate,ecosystems,and the land-atmosphere system.This study employs the Ding method to separate precipitation types from three datasets(CMFD,ERA5_Land,and CN05.1).Using data from 26meteorological observation stations in the Chinese Tianshan Mountains Region(CTMR)of China as the validation dataset,the precipitation type separation accuracy of three datasets was evaluated.Additionally,the impacts of relative humidity,precipitation amount,and air temperature on the accuracy of precipitation type separation were analyzed.The results indicate that the CMFD dataset provides the highest separation accuracy,followed by CN05.1,with ERA5_Land showing the poorest performance.Spatial correlation analysis reveals that CMFD outperforms the other two datasets at both annual and monthly scales.Root Mean Square Error(RMSE)and Mean Deviation(MD)values suggest that CMFD is more consistent with the station observational data.The analysis further demonstrates that relative humidity and precipitation amount significantly affect separation accuracy.After bias correction,the correlation coefficients between CMFD,ERA5_Land,and station observational data improved to 0.85-0.94,while the RMSE was controlled within 2 mm.The study also revealed that the overestimation of precipitation was positively correlated with the overestimation of rainfall days,negatively correlated with the overestimation of snowfall days,and that underestimated air temperatures led to an increase in the misclassification of snowfall days.This research provides a basis for selecting climate change datasets and managing water resources in alpine regions.

关 键 词：Multi-sources data Precipitation types Accuracy CTMR 

分 类 号：P426.6[天文地球—大气科学及气象学]