Improved Parameterization of Snow Albedo in WRF+Noah:Methodology Based on a Severe Snow Event on the Tibetan Plateau

Snowfall and the subsequent evolution of the snowpack have a large effect on the surface energy balance and water cycle of the Tibetan Plateau(TP).The effects of snow cover can be represented by the WRF coupled with a land surface scheme.The widely used Noah scheme is computationally efficient,but its poor representation of albedo needs considerable improvement.In this study,an improved albedo scheme is developed using a satellite-retrieved albedo that takes snow depth and age into account.Numerical experiments were then conducted to simulate a severe snow event in March 2017.The performance of the coupled WRF/Noah model,which implemented the improved albedo scheme,is compared against the model’s performance using the default Noah albedo scheme and against the coupled WRF/CLM that applied CLM albedo scheme.When the improved albedo scheme is implemented,the albedo overestimation in the southeastern TP is reduced,reducing the RMSE of the air temperature by 0.7°C.The improved albedo scheme also attains the highest correlation between the satellite-derived and the model-estimated albedo,which provides for a realistic representation of both the snow water equivalent(SWE)spatial distribution in the heavy snowbelt(SWE>6 mm)and the maximum SWE in the eastern TP.The underestimated albedo in the coupled WRF/CLM leads to underestimating the regional maximum SWE and a consequent failure to estimate SWE in the heavy snowbelt accurately.Our study demonstrates the feasibility of improving the Noah albedo scheme and provides a theoretical reference for researchers aiming to improve albedo schemes further.