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Abstract This study evaluates the impact of assimilating precipitable water vapor (PWV) within an observing system simulation experiment (OSSE) framework to improve forecasts of monsoonal mesoscale convective systems (MCSs) in Arizona. Two contrasting case studies differing in convective forcing, longevity, intensity, and coverage are analyzed using a 40‐member ensemble of 1.8‐km resolution Weather Research and Forecasting (WRF) convective‐permitting model (CPM) simulations including the Data Assimilation Research Testbed (DART) system. Synthetic PWV data are derived from a nature run (NR) and bias corrected using real GPS‐derived PWV observations from a campaign during the North American monsoon (NAM) season 2021. These synthetic PWV are assimilated in an inferior model simulation called the control run (CR) to avoid the identical twin problem. Horizontal GPS station spacing experiments (e.g., superobbed, 50 km, 100 km, and 200 km) are conducted to identify configurations that maximize forecast skills. Assimilating the synthetic PWV reduces mean errors (∼2 mm) and dry bias during the first 4–6 hr of the predictions using analyses improved with PWV data assimilation. The 100‐km GPS network optimally captures convective precipitation patterns, outperforming coarser (200‐km) and finer (50‐km) grids due to an improved representation of moisture and winds afforded by PWV data assimilation at the appropriate scales. Topography strongly influences moisture distribution, with elevation‐dependent biases, overestimation in low elevations (0–500 m), underestimation in midelevations (500–2,000 m), and systematic high‐elevation (>2,000 m) biases due to vertically integrated PWV constraints. This study provides actionable insights for optimizing GPS network design and improving convective‐scale modeling in arid/semiarid regions. 

Abstract The maximum upward vertical velocity at the leading edge of a density current is commonly <10 m s−1. Studies of the vertical velocity, however, are relatively few, in part owing to the dearth of high-spatiotemporal-resolution observations. During the Plains Elevated Convection At Night (PECAN) field project, a mobile Doppler lidar measured a maximum vertical velocity of 13 m s−1 at the leading edge of a density current created by a mesoscale convective system during the night of 15 July 2015. Two other vertically pointing instruments recorded 8 m s−1 vertical velocities at the leading edge of the density current on the same night. This study describes the structure of the density current and attempts to estimate the maximum vertical velocity at their leading edges using the following properties: the density current depth, the slope of its head, and its perturbation potential temperature. The method is then be applied to estimate the maximum vertical velocity at the leading edge of density currents using idealized numerical simulations conducted in neutral and stable atmospheres with resting base states and in neutral and stable atmospheres with vertical wind shear. After testing this method on idealized simulations, this method is then used to estimate the vertical velocity at the leading edge of density currents documented in several previous studies. It was found that the maximum vertical velocity can be estimated to within 10%–15% of the observed or simulated maximum vertical velocity and indirectly accounts for parameters including environmental wind shear and static stability.