Hyper-Local Weather Predictions with the Enhanced General Urban Area Microclimate Predictions Tool

This paper presents enhancements to, and the demonstration of, General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models computational fluid dynamic (CFD) simulations. For further development of GUMP, Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local sensors provided data train ML models, CFD urban- suburban-like areas ERAU’s were created iterated through with wide assortment inlet wind speed direction combinations. sensor combined best-fit from database flow fields, providing flight operational fully expressed field. field defined risk map for uncrewed aircraft operators based on plans individual performance metrics. The potential applications GUMP are significant due immediate availability its ability easily extend arbitrary urban suburban locations.