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An Assessment of the Applicability of Three Reanalysis Snow Density Datasets Over China Using Ground Observations
IEEE Geoscience and Remote Sensing Letters ( IF 4.8 ) Pub Date : 2022-09-16 , DOI: 10.1109/lgrs.2022.3202897 Shuo Gao, Zhen Li, Ping Zhang, Jiangyuan Zeng, Quan Chen, Changjun Zhao, Chang Liu, Zhipeng Wu, Haiwei Qiao
IEEE Geoscience and Remote Sensing Letters ( IF 4.8 ) Pub Date : 2022-09-16 , DOI: 10.1109/lgrs.2022.3202897 Shuo Gao, Zhen Li, Ping Zhang, Jiangyuan Zeng, Quan Chen, Changjun Zhao, Chang Liu, Zhipeng Wu, Haiwei Qiao
Snow density is an important variable in snowpack research. The comprehensive applicability evaluation of the snow density datasets is a prerequisite of these datasets for their applications in hydrology processes and climate change, as well as in snow equivalent water retrieval algorithms. In this letter, the applicability of three snow density datasets, including European ReAnalysis (ERA)-Interim, ERA5, and the newly released ERA5-Land datasets, was first assessed using two ground evaluation datasets with different land covers from seven snow survey courses and four densely sampled networks in China. The results show that the ERA-Interim dataset significantly overestimates snow density during the entire snow season, with an overall root mean square error (RMSE) larger than 112 kg/m3, and lacks temporal dynamics. The ERA5 and ERA5-Land datasets are generally in good agreement with the ground measurements in China. The averaged RMSEs of the ERA5 dataset are 56.2 kg/m3 against snow course sites and 28.3 kg/m3 versus the densely sampled measurements, and those of the ERA5-Land dataset are 56.6 and 28.4 kg/m3, respectively. However, the ERA5 and ERA5-Land datasets still underestimate snow density over time, especially for the middle and late snow seasons. These new findings are expected to provide valuable feedback to model developers to further enhance the accuracy of snow density datasets.
更新日期：2022-09-20
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