Evaluation of Severe Weather Parameter Sensitivity to Soil Moisture during Strongly Forced Dynamic Weather Systems across the Central Plains

The use of ensembles in severe weather forecasting continues to increase, with initial condition and physics perturbations being the foundation for most of these systems. However, soil moisture, a less commonly considered variable, can also play a role in convective uncertainty. Observations of soil moisture are relatively sparse when compared to other near surface parameters such as temperature, dew point, and wind, and much of the input for operational models is based upon modelling of soil moisture. This would imply that, similar to other fields, there is uncertainty with the analysis of soil moisture used to initialize forecast models. Past modeling studies had shown that changing soil moisture impacted model quantitative precipitation forecast (QPF) and timing of precipitation.

Little research has been done on the impact modifying soil moisture can have on severe weather parameters prior to a severe weather outbreak. In this study, the WRF-ARW with 20 km grid spacing was used to simulate several past significant severe weather outbreaks in the central and northern Plains of the United States. The soil moisture was perturbed between -50% to +50% of the analyzed observed values from the Climate Forecast Reanalysis System (CFSR). We determined the impact these perturbations had on severe weather parameters, such as convective available potential energy (CAPE), lifted condensation level (LCL) heights, wind shear, the timing and location of convective initiation, and QPF values. For one event, a convection-allowing model (CAM) with 3-km grid spacing was also be run with different soil moisture. Differences in convective initiation as well as differences in storm-scale variables such as updraft helicity, maximum updraft and downdraft, maximum simulated reflectivity were evaluated. The coverage and duration of simulated convective cells were also compared.