Convection initiation (CI) refers to the physical process of the formation of deep, moist convection. To first order, CI is triggered by a convergence mechanism, whether surface-based or elevated in nature, in a moist environment that is positively buoyant to upward parcel displacements. CI is a classic scale interaction problem requiring the favorable superposition of the synoptic-, meso-, and micro- scales. The synoptic-scale and meso-α scale establish the large-scale thermodynamic and kinematic environment favorable for CI; the meso-β scale contributes to horizontal variability within this environment; and the meso-γ and microscales contribute to crucial local boundary layer lifting and moistening. Consequently, CI is highly sensitive to the atmospheric state in which it occurs, particularly within the boundary layer. As a result, accurately predicting the timing and location of CI events poses a formidable challenge for storm-scale numerical simulations.
Motivated by studies demonstrating the influence of the synoptic- and meso-α scales upon deep, moist convection, the Mesoscale Predictability Experiment (MPEX) hypothesizes that the collection of non-routine synoptic- and meso-α-scale observations in the upstream, pre-convective environment across the Intermountain West and their subsequent assimilation into convection-permitting numerical forecasts significantly improves forecasts of the timing, location, and mode of convection initiation and evolution downstream. Herein, utilizing perfect model and perfect observations approaches applied to the study of the initial CI event from three MPEX intensive observation periods, each characterized by a different prevailing synoptic-scale flow pattern, the respective influences of initial condition uncertainty and variability in the numerical representation of sub-grid-scale planetary boundary layer (PBL) processes upon the intrinsic predictability of convection initiation are examined.
In the context of the perfect model study approach, forecasts obtained from the thirty-member, MPEX observation-containing, Ensemble Kalman-initialized ensemble for each event are utilized to examine the control that initial condition uncertainty exerts upon the intrinsic predictability of initial CI timing and location. In the context of the perfect observations study approach, Ensemble Kalman filter-derived initial conditions from the thirty-member, MPEX observation-containing ensemble for each event are utilized to initialize four additional thirty-member ensembles of numerical simulations, each utilizing a different PBL parameterization. The forecast output from these ensembles is subsequently used to examine the control that variability in the numerical representation of sub-grid-scale PBL processes exerts upon the intrinsic predictability of initial CI and timing. Preliminary findings from one MPEX event will be presented.