资源环境科技发展态势分析平台

Subgrid variability of snow is important in studying surface-atmosphere interactions as it affects grid-scale processes. However, this dynamic variability is currently not well-represented in most land-surface models (LSMs). A stochastic snow model using the Fokker-Planck Equation (FPE) has been developed specifically for representing subgrid variability of snow. In this study, this FPE snow model was coupled into the Rapid Update Cycle (RUC) LSM, which provides lower boundary conditions for the operational NOAA Rapid Refresh and High-Resolution Rapid Refresh weather prediction systems. This coupled land-surface model with subgrid snow processes is named the RUC-Stochastic Snow (SS) LSM. The performance of RUC-SS LSM and RUC LSM is analyzed in detail in initial offline single-column testing over two 13-km 13-km grid cells. The results show that RUC-SS LSM has a much better capability in estimating snow cover fraction than RUC LSM, especially during the late part of the snow-melting season. The simulated duration of the partially snow-covered period at the snow-melting stage is extended from a few days with RUC LSM to about one month with the RUC-SS LSM, thus improving prediction of surface energy budget components. For example, the latent and sensible heat fluxes and skin temperature increase gradually in the RUC-SS LSM during the transition from full coverage to snow-free conditions. In contrast, this transition happens too quickly in RUC LSM, and the energy-budget components change too abruptly. The results of RUC-SS demonstrate its capability to account for subgrid variability of snow cover and show that considering subgrid variability of snow in LSMs is important for simulating surface-atmosphere interactions.

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