Most convective storm losses occur in urban areas, raising the question of whether cities can intensify severe storms, such as supercells. We found that cities can weaken an approaching supercell by reducing low-level moisture, but can also trigger a new one downwind. More urban vegetation reduces city effects on storm evolution, while building height has little impact. This conceptual model improves understanding of supercell–city interactions and supports advances in early warning systems.

We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.

We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.