Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 5, 2025

Extreme summertime heat is an increasing challenge for cities, highlighting the need accurate, spatially meaningful methods to measure and map in ways that reflect human thermal experiences inform land management decisions. Mean radiant temperature (T_mrt) a key metric assessing urban at hyper-local scales, yet its measurement remains technically challenging. In this study, we apply six-directional, gold standard method measuring T_mrt with globe thermometer-based approaches across multiple levels of spatial aggregation develop novel machine learning model trained on field data. Data were collected semi-arid city Colorado, USA, over two summers. Using measurements from residential parcels, show aggregated thermometer data—collected using low-cost, accessible sensor—can capture patterns landscapes reasonable accuracy. Our findings also indicate learning, combining six-directional data, offers promising potential improving both accuracy efficiency. These are particularly relevant planners working scale where adaptation strategies commonly applied, especially insightful cities those increasingly experiencing arid summer conditions due climate change. This work advances practical integrating comfort into landscape planning climate-resilient design.

Язык: Английский