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Identifying and Mitigating the Effects of Urban Heat Islands in California.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifying and Mitigating the Effects of Urban Heat Islands in California./
Author:	Mohegh, Arash.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	116 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Meteorology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27787432
ISBN:	9781392755198

Identifying and Mitigating the Effects of Urban Heat Islands in California.
Mohegh, Arash.

Identifying and Mitigating the Effects of Urban Heat Islands in California. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 116 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--University of Southern California, 2018.

This item must not be sold to any third party vendors.

The urban heat island (UHI) effect describes a phenomenon whereby temperatures in cities are higher than their rural surroundings (Oke 1982) and is the result of land transformations associated with urbanization. Past studies using satellite data, ground observations, and numerical modeling have highlighted the importance of albedo and green vegetation fraction in determining temperature differences between urban regions and rural surroundings. In the following studies, we are focusing on investigating the causes of UHI and mitigation strategies. Our goal is to quantify the extent of UHIs, the effect of the contributing factors to UHI, and the effect of mitigation strategies to deal with UHI.In the first study, we create and evaluate a climate modeling framework using Weather Research and Forecasting (WRF) model over the domain of California. The model uses three nested domains, with spatial resolution of 36 km, 12 km, and 4km, from the most outer domain to the most inner domain. The outer domains inly provide boundary condition for the inner domain and their results are not analyzed. Using the WRF model, we simulated the current climate of California at 4 km spatial resolution, and output temporal resolution of one hour for all of our parameters for the 1 Sep 2001 to 31 Aug 2002, chosen to avoid strong El Nino or La Nina conditions. The simulations were done in a set of three ensembles and the simulation start dates for each ensemble-member per scenario were 1 March, 1 May, and 1 July 2001, respectively, to average out the effect of initial condition, and to have an understanding of the noise in the model. The coldest temperature in the urban areas of the domain was 0.28℃, and the hottest temperature in the urban areas were 34.97℃. Comparing this simulation to 105 weather stations in California suggested an overall mean bias (model minus observation) of −0.30℃, with higher biases in the coastal regions of Los Angeles, Bay area, and the arid regions of southeast California. The modeling framework set-up in this section provides a reference for the second part of the project as control scenario for solar reflective cool pavements, as well as other modeling studies performed by my colleagues in our group.In the second study we investigated the climate impacts of widespread deployment of cool pavements in California cities, using the modeling framework that was set-up in the last study. Solar reflective cool pavements have been proposed as a potential heat mitigation strategy for cities. However, previous research has not systematically investigated the extent to which cool pavements could reduce urban temperatures. Widespread pavement albedo increases of 0.1 and 0.4 in California cities were then simulated. Comparing temperature reductions for each scenario showed that the climate response to pavement albedo modification was nearly linear. Temperature reductions at 14:00 local standard time were found to be 0.32℃ per 0.1 increase in grid cell average albedo. Temperature reductions were found to peak in the late morning and evening when (a) boundary layer heights were low and (b) solar irradiance (late morning) and heat accumulation in the pavement (evening) was high. Temperature reductions in summer were found to exceed those in winter, as expected. After scaling the results using realistic data‐derived urban canyon morphologies and an off‐line urban canyon albedo model, annual average surface air temperature reductions from increasing pavement albedo by 0.4 ranged from 0.18℃ (Palm Springs) to 0.86℃ (San Jose). The variation among cities was due to differences in baseline climate, size of the city, urban fraction, and urban morphology.In the third study we investigated the effects of neighborhood-scale land cover and land use (LULC) properties on observed air temperatures. We focus on two regions within Los Angeles County: central Los Angeles and the San Fernando Valley (SFV). Each region has differing baseline climates and LULC property spatial distributions. LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations within the two regions. Thorough procedures were developed for removing outlier meteorological observations. Observed air temperatures were then related to the spatial mean of each LULC parameter within 500 m of each weather station. Relationships were computed for each hour of July 2015. We find that variability in neighborhood scale albedo is dominated by variability in roof albedo. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in afternoon. Air temperatures at 15:00 local time are reduced by 0.31℃ and 0.49℃ per 1 MW of daily average solar power reflected from roofs in SFV and central Los Angeles, respectively. These translate to air temperature reductions of 1.9℃ and 2.8℃ per 0.1 increase in neighborhood average roof albedo, and 7.3℃ and 9.8℃ per 0.1 increase in neighborhood albedo, for SFV and central Los Angeles, respectively. Sensitivities computed here are higher than reported by previous modeling studies that investigate air temperature reductions from hypothetical cool roof adoption scenarios. While roof albedo effects on air temperature seem to dominate over tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night.

ISBN: 9781392755198Subjects--Topical Terms:

542822
Meteorology.
Subjects--Index Terms:

Urban heat island

Identifying and Mitigating the Effects of Urban Heat Islands in California.
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520 $a The urban heat island (UHI) effect describes a phenomenon whereby temperatures in cities are higher than their rural surroundings (Oke 1982) and is the result of land transformations associated with urbanization. Past studies using satellite data, ground observations, and numerical modeling have highlighted the importance of albedo and green vegetation fraction in determining temperature differences between urban regions and rural surroundings. In the following studies, we are focusing on investigating the causes of UHI and mitigation strategies. Our goal is to quantify the extent of UHIs, the effect of the contributing factors to UHI, and the effect of mitigation strategies to deal with UHI.In the first study, we create and evaluate a climate modeling framework using Weather Research and Forecasting (WRF) model over the domain of California. The model uses three nested domains, with spatial resolution of 36 km, 12 km, and 4km, from the most outer domain to the most inner domain. The outer domains inly provide boundary condition for the inner domain and their results are not analyzed. Using the WRF model, we simulated the current climate of California at 4 km spatial resolution, and output temporal resolution of one hour for all of our parameters for the 1 Sep 2001 to 31 Aug 2002, chosen to avoid strong El Nino or La Nina conditions. The simulations were done in a set of three ensembles and the simulation start dates for each ensemble-member per scenario were 1 March, 1 May, and 1 July 2001, respectively, to average out the effect of initial condition, and to have an understanding of the noise in the model. The coldest temperature in the urban areas of the domain was 0.28℃, and the hottest temperature in the urban areas were 34.97℃. Comparing this simulation to 105 weather stations in California suggested an overall mean bias (model minus observation) of −0.30℃, with higher biases in the coastal regions of Los Angeles, Bay area, and the arid regions of southeast California. The modeling framework set-up in this section provides a reference for the second part of the project as control scenario for solar reflective cool pavements, as well as other modeling studies performed by my colleagues in our group.In the second study we investigated the climate impacts of widespread deployment of cool pavements in California cities, using the modeling framework that was set-up in the last study. Solar reflective cool pavements have been proposed as a potential heat mitigation strategy for cities. However, previous research has not systematically investigated the extent to which cool pavements could reduce urban temperatures. Widespread pavement albedo increases of 0.1 and 0.4 in California cities were then simulated. Comparing temperature reductions for each scenario showed that the climate response to pavement albedo modification was nearly linear. Temperature reductions at 14:00 local standard time were found to be 0.32℃ per 0.1 increase in grid cell average albedo. Temperature reductions were found to peak in the late morning and evening when (a) boundary layer heights were low and (b) solar irradiance (late morning) and heat accumulation in the pavement (evening) was high. Temperature reductions in summer were found to exceed those in winter, as expected. After scaling the results using realistic data‐derived urban canyon morphologies and an off‐line urban canyon albedo model, annual average surface air temperature reductions from increasing pavement albedo by 0.4 ranged from 0.18℃ (Palm Springs) to 0.86℃ (San Jose). The variation among cities was due to differences in baseline climate, size of the city, urban fraction, and urban morphology.In the third study we investigated the effects of neighborhood-scale land cover and land use (LULC) properties on observed air temperatures. We focus on two regions within Los Angeles County: central Los Angeles and the San Fernando Valley (SFV). Each region has differing baseline climates and LULC property spatial distributions. LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations within the two regions. Thorough procedures were developed for removing outlier meteorological observations. Observed air temperatures were then related to the spatial mean of each LULC parameter within 500 m of each weather station. Relationships were computed for each hour of July 2015. We find that variability in neighborhood scale albedo is dominated by variability in roof albedo. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in afternoon. Air temperatures at 15:00 local time are reduced by 0.31℃ and 0.49℃ per 1 MW of daily average solar power reflected from roofs in SFV and central Los Angeles, respectively. These translate to air temperature reductions of 1.9℃ and 2.8℃ per 0.1 increase in neighborhood average roof albedo, and 7.3℃ and 9.8℃ per 0.1 increase in neighborhood albedo, for SFV and central Los Angeles, respectively. Sensitivities computed here are higher than reported by previous modeling studies that investigate air temperature reductions from hypothetical cool roof adoption scenarios. While roof albedo effects on air temperature seem to dominate over tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night.
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