Improved High Resolution Heat Exposure Assessment With Personal Weather Stations and Spatiotemporal Bayesian Models

Area of Science:

• Environmental Science
• Urban Climatology
• Geospatial Analysis

Background:

• The majority of the US population lives in urban areas, experiencing the urban heat island (UHI) effect.
• Existing gridded temperature products lack the resolution to capture intra-urban variability.

Purpose of the Study:

• To develop a method for estimating hourly air temperatures at a $0.01°\times 0.01°$ resolution.
• To improve exposure assessment for the US population regarding temperature extremes.

Main Methods:

• Utilized an extensive network of personal weather stations for intra-urban variability.
• Employed a spatiotemporal Bayesian model with Integrated Nested Laplace Approximation-Stochastic Partial Differential Equation.

Main Results:

• Achieved an overall root mean square error of $1.06°C$ across diverse US case studies.
• Identified previously undetected temperature hotspots with high granularity.
• Demonstrated model applicability across different climatic zones and urban landscapes.

Conclusions:

• The developed model offers high-resolution temperature estimates, enhancing exposure assessment for UHI effects.
• High population-density neighborhoods are more susceptible to elevated temperatures and prolonged hot nights.
• The model is suitable for epidemiological studies on heat and cold stress impacts on human health.