Extra surfacestations mission vindication: Robust UHI temperature biases confirmed in USA

More surfacestations project vindication: Strong UHI temperature biases confirmed in USA

WUWT readers may recall that NOAA conducted an experiment at the Oak Ridge National Laboratory that confirmed my findings about the effects of local urbanization on surface temperature measurements.

The urban heat island (UHI) effect is strongly influenced by changes in local land surfaces on an urban scale. Basically, the more asphalt, concrete, buildings etc. there are near a thermometer, the more the low night temperature is biased upwards due to the heat storage.

Climate monitoring thermometers are therefore biased upwards. This new UHI database confirms my results published in 2015 at the AGU autumn meeting. – Anthony

New Surface Urban Heat Island database for the US

ONE new study Published in the ISPRS Journal of Photogrammetry and Remote Sensing, UHI (SUHI) intensities in 497 urban areas in the United States shows the intensity of UHI intensities with clear skies by combining remote data products with multiple urban areas defined by the U.S. census.

The SUHI intensity is the difference in surface temperature between built and undeveloped pixels in an urban area.

The study reported that the time of day Summer SUHI was 1.91 ° ​​C higher and during the day Winter SUHI was 0.87 ° C higher.

The study also reports that SUHI intensity is lower in census areas with higher median incomes and higher proportions of white people. Unfortunately, the study didn't report how the UHI effect changes over time.

h / t to friends of science

The paper: https://www.sciencedirect.com/science/article/abs/pii/S0924271620302082#!

The urban heat island (UHI) effect is heavily modulated by changes in the aerodynamic, thermal and radiative properties of the earth's land surfaces on an urban scale. Interest in this phenomenon, both from a climatological and public health perspective, has led to hundreds of UHI studies, mostly conducted from city to city. However, these studies do not provide a complete picture of the UHI for administrative units using a consistent methodology. To fill this gap, we characterize the UHI intensities (SUHI) for all urban areas in the US using a modified SUE (Simplified Urban Extent) approach by combining a fusion of data products with remote sensing with multiple US censuses. defined administrative urban delimitations. We found the highest daytime SUHI intensities in summer (1.91 ± 0.97 ° C) for 418 of the 497 urban areas, while daytime SUHI intensities in winter (0.87 ± 0.45 ° C) in 439 cases the lowest is.

Because urban vegetation has often been cited as an effective means of mitigating UHI, we use NDVI, a satellite proxy for living green vegetation, and US Census Tract boundaries to characterize how vegetation density is between cities, inner-city, and interstate Regions modulated -seasonal variability of SUHI intensity. In addition, we examine how elevation and distance from the coast confuse the SUHI estimates. To further quantify the uncertainties in our estimates, we analyze and discuss some of the limitations of these satellite-based products across climate zones, particularly issues with using remotely controlled radiometric temperature and vegetation indices as proxies for urban warmth and vegetation cover. We demonstrate an application of this spatially explicit data set and show that SUHI intensity is lower in most urban areas in census areas with higher average incomes and higher proportions of white people. Our analysis also suggests that poor and non-white urban dwellers may suffer from the potential adverse effects of summer SUHI without taking advantage of the potential benefits (e.g. warmer temperatures) in winter, although future research will determine this using more extensive research Heat exposure metrics required. This study develops new methodological advances to characterize SUHI and its urban variability at aggregation levels in line with sources of other socio-economic information that may be relevant for future interdisciplinary research and as a possible screening tool for policy making.

The data set developed in this study is visualized at the following address: https://datadrivenlab.users.earthengine.app/view/usuhiapp

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