Dynamical downscaling details: the WRF model

Dynamical downscaling uses a physically based weather forecasting model to produce higher time and space resolution data from coarser General Circulation Model (GCM) data. SNAP uses the Weather Research and Forecasting (WRF) model to downscale GCM data. The internal physics engine of the WRF model is bounded by the input data. For projected data, it is bounded by GCM output, and for historical periods, it is bounded by reanalysis data.

Fewer models and scenarios are available because this is a very computationally expensive process. However, more than 50 variables are available at an hourly temporal resolution. We also provide daily and monthly versions. Spatial resolution is 20-km x 20-km. Example variables include:

  • Temperature
  • Rainfall
  • Snowfall
  • Precipitation type (convective and non-convective)
  • Wind speed and direction
  • Heat fluxes (radiative and turbulent)
  • Snow depth
  • Soil temperature and moisture
  • Upper level winds, heights, and temperatures

See Bieniek et al. (2016) for a more detailed description of the WRF model procedure and an evaluation against historical temperature and precipitation data.