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

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.

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

“…Each sample is a layered snow profile over bare soil that contains the thickness, temperature, density, and SNTHERM-simulated grain size of each snow layer, and the temperature, water content, and unfrozen soil water content averaged over 0-5 cm of soil. The SNTHERM simulated snow depth, layered geometric grain size (D max ), snow density and snow temperature were validated using in situ snowpit measurements in [32]. In Figures 2-4, we further present a comparison of vertical profiles on three different days.…”

“…In this section, two datasets composed of multiple-layer snow profile samples are introduced as follows. [32] used the hourly meteorological forcing data observed at the station (including near-surface air temperature, air pressure, precipitation, wind speed, relative humidity, and solar radiation) to drive SNTHERM and obtained simulated snow profiles at the 1 hour step as shown in Figure 1. This dataset was used as the samples inputted into MEMLS to study the bulk T B calculation method.…”

“…T soil and unfrozen soil water content (mv soil ) from the SNTHERM simulations were used to calculate the soil dielectric constants using Zhang et al's (2003) model [36]. The rms was set the same as the simulation in Chen et al's [32] at Altay, which was 2 cm. Fixed values of downwelling sky T B (0 K) and rms (2 cm) were used to reduce the influence of nuisance parameters, when the MEMLS multiple-layer snow T B was compared only with the MEMLS bulk snow T B .…”

“…In previous studies, MEMLS have been extensively validated in Finland [19], US [19,29,30], Canada [19,31], and China [32] using a groundbased radiometer and concurrent snowpit measurements. The accuracy of MEMLS-IBA is approximately 5 K root mean squared error (RMSE) if the snow microstructure parameter is allowed to be scaled by an optimized multiplication factor [29,30,32], and the accuracy is about 10-15 K RMSE [19,31] if no optimization is performed. The snow microstructure parameter used in MEMLS is called the exponential correlation length (p ex ).…”

“…Third, we introduced the idea of penetration depth, which stands for the distance at which the radiation from the snow's surface is attenuated to a negligible fraction of its original value, to check whether the performance at high frequencies (36.5 and 89 GHz) can be further improved. Finally, the proposed method was tested using in situ snowpit observations from the Altay experiment [32].…”