Sensitivity of Convection-Allowing Forecasts to Land Surface Model Perturbations and Implications for Ensemble Design

Abstract

Abstract In this exploratory study, a series of perturbations to the land surface model (LSM) component of the Weather Research and Forecasting (WRF) Model was developed to investigate the sensitivity of forecasts of severe thunderstorms and heavy precipitation at 4-km grid spacing and whether such perturbations could improve ensemble forecasts at this scale. The perturbations (generated using a combination of perturbing fixed parameters and using separate schemes, one of which—Noah-MP—is new among the WRF modeling community) were applied to a 10-member ensemble including other mixed physics parameterizations and compared against an identically configured ensemble that did not include the LSM perturbations to determine their impact on probabilistic forecasts. A third ensemble using only the LSM perturbations was also configured. The results from 14 (in total) 36-h ensemble forecasts suggested the LSM perturbations resulted in systematic improvement in ensemble dispersion and error characteristics. Lower-tropospheric temperature, moisture, and wind fields were all improved, as were probabilistic precipitation forecasts. Biases were not systematically altered, although some outlier members are present. Examination of near-surface temperature and mixing ratio fields, surface energy fluxes, and soil fields revealed tendencies caused by certain perturbations. A case study featuring tornadic supercells illustrated the physical causes of some of these tendencies. The results of this study suggest LSM perturbations can sample a dimension of model error not yet sampled systematically in most ensembles and should be included in convection-allowing ensembles.