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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 Over the course of his career, Fuqing Zhang drew vital new insights into the dynamics of meteorologically significant mesoscale gravity waves (MGWs), including their generation by unbalanced jet streaks, their interaction with fronts and organized precipitation, and their importance in midlatitude weather and predictability. Zhang was the first to deeply examine “spontaneous balance adjustment”—the process by which MGWs are continuously emitted as baroclinic growth drives the upper-level flow out of balance. Through his pioneering numerical model investigation of the large-amplitude MGW event of 4 January 1994, he additionally demonstrated the critical role of MGW–moist convection interaction in wave amplification. Zhang’s curiosity-turned-passion in atmospheric science covered a vast range of topics and led to the birth of new branches of research in mesoscale meteorology and numerical weather prediction. Yet, it was his earliest studies into midlatitude MGWs and their significant impacts on hazardous weather that first inspired him. Such MGWs serve as the focus of this review, wherein we seek to pay tribute to his groundbreaking contributions, review our current understanding, and highlight critical open science issues. Chief among such issues is the nature of MGW amplification through feedback with moist convection, which continues to elude a complete understanding. The pressing nature of this subject is underscored by the continued failure of operational numerical forecast models to adequately predict most large-amplitude MGW events. Further research into such issues therefore presents a valuable opportunity to improve the understanding and forecasting of this high-impact weather phenomenon, and in turn, to preserve the spirit of Zhang’s dedication to this subject.