Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale ensembles are initialized using background fields from either fixed physics or multiple physics mesoscale ensemble analyses. Radar observations from four operational WSR-88Ds are assimilated into convective-scale ensembles using ARPS model-based 3DVAR system and ensemble forecasts are launched. Results show that the ensemble with background fields from multiple physics ensemble provides more realistic forecasts of significant tornado parameter, dryline structure, and near surface variables than ensemble from fixed physics background fields. The probabilities of strong low-level updraft helicity from multiple physics ensemble correlate better with observed tornado and rotation tracks than probabilities from fixed physics ensemble. This suggests that incorporating physics diversity across the ensemble can be important to successful probabilistic convective-scale forecast of supercell thunderstorms, which is the main goal of NOAA’s Warn-on-Forecast initiative. 1. Introduction The development and evolution of severe thunderstorm events are strongly tied to the environment, and therefore incorporating mesoscale environmental variability and its uncertainty is crucial for successful convective-scale data assimilation and forecasts [1–3]. Several studies illustrate the importance of incorporating the influence of environmental variability and mesoscale forcing on the storm scale flows for accurate prediction of tornadic supercell thunderstorms ([4, 5]). In particular, when Stensrud and Gao [4] use a more realistic inhomogeneous mesoscale environment as initial and boundary conditions for their convective-scale three-dimensional variational (3DVAR) data assimilation and forecast system, substantial improvement in forecast accuracy is obtained over a similar convective-scale system using a homogeneous, single-sounding environment, which is typical of idealized storm modeling studies. Yussouf et al. [6] investigate the benefits of using a combined mesoscale-convective scale
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