Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China

• Main Authors: Tao Chen, Jinmei Pan, Shunli Chang, Chuan Xiong, Jiancheng Shi, Mingyu Liu, Tao Che, Lifu Wang, Hongrui Liu
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Remote Sensing
• Subjects: snow; snow process model validation; snow depth; snow grain size; passive microwave; sntherm; memls
• Online Access: https://www.mdpi.com/2072-4292/12/3/507

Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (T_B), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008−2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2−4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed T_B with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered.