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

Tao Chen,Jiancheng Shi,Mingyu Liu,Hongrui Liu,Chuan Xiong,Lifu Wang,Shunli Chang,Jinmei Pan,Tao Che

doi:10.3390/rs12030507

Abstract

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 (TB), 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 TB with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered.

Highlights

The seasonal snow cover, as an important component of the cryosphere, plays a key role in the climate system [1,2]
We validated the use of the Snow Thermal Model (SNTHERM) combined with Microwave Emission Model of Layered Snowpacks (MEMLS) to simulate the snow depth, snow grain size, and brightness temperature in the Xinjiang, Qinghai, and Tibet provinces in China
Results showed that the SNTHERM has a good daily snow depth estimation accuracy, in the order of l–4 cm, when the accurate meteorological forcing is provided

Summary

Introduction

The seasonal snow cover, as an important component of the cryosphere, plays a key role in the climate system [1,2]. The high albedo of the snow surface, the latent heat generated by the internal ice/water phase transition, and the adiabatic effect of the snow layer significantly shift the energy balance and influence the energy exchange between the atmosphere, the snow cover, and the underlying soil medium. The snow is a natural reservoir of solid water resources. The snow-redistributed energy drives the circulation and redistribution of water. As a result of these reasons, the snow is an important parameter in the climate numerical model and the hydrological model predictions [3]. The snow feedback mechanisms on the climate change in long-term scale remains uncertain [4], which requires improvements in the snow-related property simulation

Objectives

Results

Conclusion