Impact of different microphysical parameterizations on extreme snowfall events in the Southern Andes

Abstract

This study evaluates the reliability of the Weather Research and Forecasting (WRF) model to simulate extreme snowfall events in the Southern Andes. The assessment includes comparison of seven microphysics parameterizations (MPs) schemes, using two different reanalysis datasets as boundary and initial conditions, namely NCEP-FNL and ERA-interim. Results demonstrate the feasibility of predicting extreme snow events with reasonable accuracy using WRF, but the accuracy level is dependent on the imposed initial conditions. In particular, by computing the RMSE turned out that the WSM6 under NCEP-FNL performed better as compared with the other schemes in the highly complex topography of the Andes. Conversely, Morrison and WDM5 ranked the worst as both simulated excessive snowfall. For ERA-interim initial conditions, Goddard (WDM6) scheme shows the best (weaker) performance. Despite these limitations, these modeling experiments demonstrate the feasibility of using the WRF to forecast the spatial and temporal distribution of snowfall and precipitation in this region of steep topography. Therefore, modeling experiments may reduce people losses by anticipating the weather threat for local communities, and provide decision makers with information on which to base future interventions for water supply hydrological hazards. Keywords: WRF, Cloud microphysical parameterizations, Snowfall, Andes