Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer

TL;DR

This paper introduces a hybrid experimental and numerical method using hyperspectral imaging and geospatial modeling to accurately predict surface temperatures and radiative heat transfer in New York City.

Contribution

It presents a novel approach combining hyperspectral imaging with a geospatial radiosity model for urban heat transfer analysis.

Findings

Model shows high accuracy in surface temperature predictions.

Method enables detailed analysis of urban radiative heat exchange.

Approach is scalable for large urban areas.

Abstract

Three decades into the research seeking to derive the urban energy budget, the dynamics of the thermal exchange between the densely built infrastructure and the environment are still not well understood. We present a novel hybrid experimental-numerical approach for the analysis of the radiative heat transfer in New York City. The aim of this work is to contribute to the calculation of the urban energy budget, in particular the stored energy. Improved understanding of urban thermodynamics incorporating the interaction of the various bodies will have implications on energy conservation at the building scale, as well as human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, and a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. The close comparison of temperature values derived from measurements and the computed surface temperatures (including streets and roads) implies that this geospatial, thermodynamic numerical model applied to urban structures, is promising for accurate and high resolution analysis of urban surface temperatures.