Evaluating the representativeness of aircraft sampling strategies for cloud water content in LASSO simulations over the ARM Southern Great Plains (SGP) Site

Aircraft observations are widely used to evaluate cloud simulations; however, their inherently localized sampling may not fully represent the domain-scale cloud properties resolved by large-eddy simulations (LES). This study quantifies how aircraft-like sampling strategies influence the representativeness of in-cloud cloud water content (CWC) derived from LES output. Using LASSO simulations for the ARM Southern Great Plains site (SGP), we emulate aircraft trajectories by prescribing multiple horizontal flight patterns (circle, diagonal, straight, combo, and zigzag) and vertical profiles (level-leg, sine wave, staircase, and zigzag) under different speed categories. Results show that sampling geometry substantially influences the degree to which aircraft-like measurements capture the magnitude and variability of domain-scale CWC. Sampling representativeness depends jointly on flight speed and trajectory geometry. For the level-leg, sine wave, and staircase vertical profiles, increasing flight speed generally improves agreement with the layer-restricted in-cloud domain mean by increasing spatial coverage and reducing sensitivity to localized heterogeneity. In contrast, the zigzag pattern does not show systematic improvement with speed because its phase-locked vertical–horizontal coupling constrains effective spatial coverage. Cloud-boundary analysis further indicates the tendency to underestimate cloud-top height, while cloud-base height is more consistently captured, with higher speeds reducing both bias and variability. These findings establish a systematic framework for interpreting aircraft-based cloud measurements in the context of LES output and highlight the importance of sampling strategies in assessing representativeness in model–observation comparisons.