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

Abstract

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

The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

“…This paper focuses on atmospheric sensing, but MoVE has already been used with ground, air, and pedestrian scenarios documented in. [3][4][5] 1.4 It takes scientists, engineers, and pilots Effectively exploring the atmosphere with uncrewed aircra using specialized sensors has both high science potential and high complexity. Data engineers providing live sensor telemetry data from airborne vehicles to atmospheric scientists is key and enables atmospheric scientists to provide guidance to pilots to y toward areas of interest.…”

“…[26][27][28] The General Urban area Microclimate Predictions (GUMP) tool forecasts urban ow and has been validated using simultaneous observations from multiple meteorologically instrumented UA. 3,29 Other mobile platforms include ship-based measurements, 30 crewed (manned) aircra measurements, 31 or dropping radiosondes or launching radiosondes, 32 tethered balloon systems, 33 or pedestrian measurements. 34 All these observation systems need to geo-tag and timestamp each atmospheric sample and aggregate the results into a coherent picture.…”

“…26–28 The General Urban area Microclimate Predictions (GUMP) tool forecasts urban flow and has been validated using simultaneous observations from multiple meteorologically instrumented UA. 3,29…”

“…This paper focuses on atmospheric sensing, but MoVE has already been used with ground, air, and pedestrian scenarios documented in. 3–5…”