A Downscaling Intercomparison Study: The Representation of Slope- and Ridge-Scale Processes in Models of Different Complexity - Kruyt, Bert; Mott, Rebecca; Fiddes, Joel; Gerber, Franziska; Sharma, Varun; Reynolds, Dylan (Frontiers in Earth Science, 2022)
Spatially distributed meteorological information at the slope scale is relevant for many processes in complex terrain, yet information at this sub-km spatial resolution is difficult to obtain. While downscaling to kilometer resolutions is well described in literature, moving beyond the kilometer scale is not. In this work, we present a methodical comparison of three downscaling methods of varying complexity, that are used to downscale data from the Numerical Weather Prediction model COSMO-1 at 1.1 km horizontal resolution to 250 m and 50 m over a domain of highly complex terrain in the Swiss Alps. We compare WRF, a dynamical atmospheric model; ICAR, a model of intermediate complexity; and TopoSCALE, an efficient topography-based downscaling scheme. Point-scale comparisons show similar results amongst all three models w.r.t. mean-error statistics, but underlying dynamics are quite different, for example w.r.t. elevation. Ridge-flow interactions show reasonable agreement between WRF and ICAR at 250 m model resolution, but at 50 m resolution differences become more pronounced. At this resolution, WRF is able to simulate complex flow patterns that ICAR cannot. Validation against Lidar data suggests that only WRF is able to capture preferential deposition of snow. In view of WRF's computational requirements, ICAR is deemed to be a cost efficient alternative at the 250 m resolution. TopoScale performs very well in point-scale comparison, but it is unclear how much of this can be attributed to the model itself vs the forcing data and observations assimilated therein. Further study is required to quantify this effect.