We present work to determine the validity of linearity assumptions and ensemble sizes for two independent events that interact strongly with the mountainous environment: (a) fog over Salt Lake City, and (b) and severe downslope wind storms lee of the Colorado Rockies. Both cycling ensemble data assimilation experiments use the Data Assimilation and Research Testbed (DART) and up to 96 ensemble members. Nested domains of 36-12-4 km and 12-4-1.33 km for the Salt Lake City and Colorado downslope wind case, respectively, allow for comparison between the events and across scales.
Vastly different dynamics at similar scales provides basis for comparison. Approximating the analysis increments that would result from assimilating a perfect observation located at the maximum estimated sensitivity, then executing nonlinear ensemble forecasts, quantifies departures from linearity for each case. Convergence studies help identify ensemble sizes needed to obtain robust sensitivity estimates. For the downslope event, we analyze the roles of synoptic scales and local boundary layer thermal preconditioning in determining ground-level wind speeds.