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Zach Baumer

Publications and source records attributed to Zach Baumer.

2 recordsLinked to original sources

CoolPath Tool: A Thermal Comfort Path Planning Tool for Urban Mobility

Extreme heat poses a growing challenge for active transportation in cities where conventional weather reporting (e.g. limited air temperature measurement for the whole city) fails to capture the large microclimate variations that pedestrians and cyclists experience. We present a novel walking and biking route planning tool (''CoolPath Tool'') that selects paths based on thermal comfort using the Universal Thermal Climate Index (UTCI) rather than just distance or travel time. This system combines high-resolution thermal modeling with real-time route mapping. We generate city-scale UTCI maps using GPU version of Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, to account for urban features (buildings, trees,) and weather conditions. The urban features are pre-mapped using satellite data products and the routes are the roadways. For any given origin and destination, our tool calculates the average UTCI along each possible route and recommends the ''coolest'' route, i.e. the path with the lowest heat stress (often the most shaded or otherwise thermally comfortable), while still being reasonably direct. We demonstrate this in a case study for Austin, Texas. The approach identifies routes that significantly reduce pedestrians' heat exposure (often recommending routes with a much larger proportion of shade). Such thermally-informed route planning has important public well-being, and economic implications: by helping people avoid dangerous heat hotspots and sun-exposed areas, it can reduce the risk of heat-related illness and make walking or biking a safer choice even on hot days. This tool is part of the Austin Digital Twin efforts developed as part the UT-City CoLab needs. The work while demonstrated for Austin, TX is scalable, and transferrable to other cities globally.

physics.soc-ph

Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA

Urban downscaling is a link to transfer the knowledge from coarser climate information to city scale assessments. These high-resolution assessments need multiyear climatology of past data and future projections, which are complex and computationally expensive to generate using traditional numerical weather prediction models. The city of Austin, Texas, USA has seen tremendous growth in the past decade. Systematic planning for the future requires the availability of fine resolution city-scale datasets. In this study, we demonstrate a novel approach generating a general purpose operator using deep learning to perform urban downscaling. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city of Austin, Texas, USA. We show the development of a high-resolution gridded precipitation product (300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP). High resolution gridded datasets of precipitation offer insights into the spatial distribution of heavy to low precipitation events in the past. The algorithm shows improvement in the mean peak-signal-to-noise-ratio and mutual information to generate high resolution gridded product of size 300 m X 300 m relative to the cubic interpolation baseline. Our results have implications for developing high-resolution gridded-precipitation urban datasets and the future planning of smart cities for other cities and other climatic variables.

physics.ao-ph