Surface heat assessment for developed environments: Probabilistic urban temperature modeling. (November 2017)

• Record Type: Journal Article
• Authors: Malings, Carl; Pozzi, Matteo; Klima, Kelly; Bergés, Mario; Bou-Zeid, Elie; Ramamurthy, Prathap
• Is Part Of: Computers, environment and urban systems. Volume 66(2017)
• Page Start: 53
• Page End: 64
• Publication Date: 2017-11
• Subjects: City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2017.07.006
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Extreme heat waves, exacerbated by the urban heat island effect, have major impacts on the lives and health of city residents. Projected future temperature increases for many urban areas of the United States will further exacerbate these impacts. Accurate predictions of the spatial and temporal distribution of risk associated with such heat waves can support the optimal implementation of strategies to mitigate these risks, such as the issuance of heat advisories and the activation of cooling centers. In this paper, we describe how fine resolution simulations of historic extreme heat events are generated and used to train a probabilistic spatio-temporal model of the temperature distribution in an urban area. We further demonstrate how this model can be used to combine temperature data from various sources and downscale regional predictions in order to provide accurate fine resolution temperature forecasts. Applications of this model are presented for two urban areas: New York City, NY and Pittsburgh, PA, USA. Based on simulated temperature data from fine resolution forecasting models, we find that this probabilistic method can improve the prediction accuracies of urban temperatures, locally and especially in the short-term, with respect to other temperature forecasting and interpolation methods, such as the use of average city-wide temperature predictions and estimates at discrete weather stations. Highlights: Assessing and mitigating risks of high surface temperatures in cities requires accurate fine resolution forecasting. Existing deterministic urban microclimate models are accurate but computationally demanding. A more efficient probabilistic model of urban temperatures is developed from fine resolution microclimate simulations. This model can be updated using temperature data to improve predictions. This model is validated on separate heat wave simulations for Pittsburgh and New York City.