Advancements in RUC Snow Model for Implementation in the Regional Application of the Unified Forecasting System (UFS)
Journal Article
Overview
abstract
RUC land surface model (LSM) was designed for short-range weather predictions with an emphasis on severe weather. The model has been operational at NCEP since 1998. Currently it is utilized in the operational WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) regional models. Being available to the world WRF community, RUC LSM is also used as a land-surface component in operational weather prediction models in Austria, New Zealand and Switzerland.At present time, RUC LSM is being tested in the regional application of the UFS-based Rapid Refresh FV3 Standalone (RRFS) model to replace operational RAP and HRRR at NCEP.RUC LSM has improved and matured over the years. The unique feature of this land-surface model is continuous evolution of soil/snow states within moderately coupled land data assimilation (MCLDA). To avoid possible drifts, this feature requires high skill from RUC LSM as well as accurate atmospheric forcing. Continuous snow cycling includes the following snow state variables: snow cover fraction, snow depth, snow water equivalent and snow temperature. To avoid possible inaccuracies in the location of cycled snow on the ground, snow depth is corrected daily using 4-km IMS snow cover information. Work is also underway to further improve RUC snow model for better surface predictions over snow-covered areas. RUC snow model uses “mosaic” approach to account for subgrid variability of snow cover. Within this approach, snow-covered and snow-free portions of the grid cells are treated separately in the solution of energy and moisture budgets. Thus, snow cover fraction becomes a critical parameter, and modifications to its computation have been developed and tested in the RRFS retrospective experiments. Results from these validation experiments will be presented at the meeting.
