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With rising global temperatures, urban environments are increasingly vulnerable to heat stress, often exacerbated by the Urban Heat Island (UHI) effect. While most UHI research has focused on large metropolitan areas around the world, relatively smaller-sized cities (with a population 100 000–300 000) remain understudied despite their growing exposure to extreme heat and meteorological significance. In particular, urban heat advection (UHA), the transport of heat by mean winds, remains a key but underexplored mechanism in most modeling frameworks. High-resolution numerical weather prediction (NWP) models are essential tools for simulating urban hydrometeorological conditions, yet most prior evaluations have focused on retrospective reanalysis products rather than forecasts. In this study, we assess the performance of a widely used operational weather forecast model, the High-Resolution Rapid Refresh (HRRR), as a representative example of current NWP systems. We investigate its ability to predict spatial and temporal patterns of urban heat and UHA within and around Lubbock, Texas, a small-sized city located in a semi-arid environment in the southwestern US. Using data collected between 1 September 2023, and 31 August 2024 from the Urban Heat Island Experiment in Lubbock, Texas (U-HEAT) network and five West Texas Mesonet stations, we compare 18 h forecasts against in situ observations. HRRR forecasts exhibit a consistent nighttime cold bias at both urban and rural sites, a daytime warm bias at rural locations, and a pervasive dry bias across all seasons. The model also systematically overestimates near-surface wind speeds, further limiting its ability to accurately predict UHA. Although HRRR captures the expected slower nocturnal cooling in urban areas, it does not well capture advective heat transport under most wind regimes. Our findings reveal both systematic biases and urban representation limitations in current high-resolution NWP forecasts. Our forecast–observation comparisons underscore the need for improved urban parameterizations and evaluation frameworks focused on forecast skill, with important implications for heat-risk warning systems and forecasting in small and mid-sized cities. 

Abstract. Aerosol–cloud interactions (ACIs) are considered to be the most uncertaindriver of present-day radiative forcing due to human activities. Thenonlinearity of cloud-state changes to aerosol perturbations make itchallenging to attribute causality in observed relationships of aerosolradiative forcing. Using correlations to infer causality can be challengingwhen meteorological variability also drives both aerosol and cloud changesindependently. Natural and anthropogenic aerosol perturbations from well-defined sources provide “opportunistic experiments” (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.