The impact and severity of individual weather events can be shaped at the neighborhood scale by unique local environmental features such as buildings, tree cover, pavement or nearby bodies of water. These features influence temperature, humidity and wind, potentially amplifying weather effects and leading to localized extremes like strong winds, elevated surface temperatures, and poor air quality. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) award supports research investigating development of a new system that combines atmospheric measurements and simulation to deliver accurate and actionable weather forecasts at the neighborhood scale. The system looks to be designed to improve routine predictions by accounting for fine-scale environmental effects that are often unresolved in current weather forecasting models. Central to the measurement system are uncrewed aircraft systems (UAS), which offer a proven advantage in high-resolution sensing of atmospheric conditions. The UAS-based observations look to feed into a high-resolution nested numerical weather prediction model enhanced with model adaptation and machine learning. This approach should allow the model to continually adjust and minimize prediction errors, resulting in more accurate, fine-grained forecasts of localized weather variability. The project brings together a multidisciplinary team with expertise in fluid dynamics, computational science, machine learning, atmospheric