Advances In RUC Lsm Snow Component To Address Cold Biases In Snow-covered Regions In RAP and HRRR

Abstract

RUC Land-Surface Model (LSM), a Weather Research and Forecast (WRF) LSM option, is used as a land surface component in the operational Rapid Refresh (RAP) over North America domain and in the High-Resolution Rapid Refresh (HRRR) over CONUS domain. It was also added to the land-surface model suite available in NASA Land Information System (LIS), and work has been started to implement it in the Next Generation Global Prediction System (NGGPS) as part of the RAP/HRRR physics suite. The RUC LSM performance has been evaluated for almost two decades within the real-time operational weather prediction systems focused on storm-scale predictions for severe weather and safer aviation. And in the recent couple of years it has been more and more extensively utilized by the WRF community in different parts of the world, including Arctic regions, and for different applications. Valuable feedback from the National Weather Prediction forecast offices and the WRF community has motivated further advances towards better representation of processes in snow-covered regions. The new treatment has been implemented for grid cells partially covered with snow. It considers snow-covered and non-snow-covered portions of a grid cell independently, and independently determined surface fluxes are aggregated to feed back into the surface-layer scheme at the end of each time step. This new “mosaic” approach removes the constraint of keeping skin temperature of partially covered with snow grid cells at or below the freezing point, and helps to reduce cold biases in these regions. Comparison results from experiments with the new and old approaches will be presented at the meeting. Also, techniques impemented in RAP/HRRR for optimal initialization of snow cover on the ground will be presented.