Despite a globally growing proportion of cities located in regions of complex terrain, interactions between urbanization and complex terrain and their meteorological impacts are not well understood. We utilize numerical model simulations and satellite data products to investigate such impacts over San Miguel de Tucumán, Argentina. Numerical modeling experiments show urbanization results in 20–30% less precipitation downwind of the city and an eastward shift in precipitation upwind. Our experiments show that changes in surface energy, boundary layer dynamics, and thermodynamics induced by urbanization interact synergistically with the persistent forcing of atmospheric flow by complex terrain. With urbanization increasing in mountainous regions, land‐atmosphere feedbacks can exaggerate meteorological forcings leading to weather impacts that require important considerations for sustainable development of urban regions within complex terrain.

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This paper presents a deep learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24 kt Root Mean Squared Error (RMSE). In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.

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