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Identifying and mitigating the effects of urban heat islands in California

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Identifying and mitigating the effects of urban heat islands in California

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Identifying and mitigating the effects of urban heat islands
in California
Doctoral dissertation
By
Arash Mohegh

Doctor of Philosophy (ENVIRONMENTAL ENGINEERING)
Department of Civil and Environmental Engineering
Faculty of the USC Graduate School
Viterbi School of Engineering
University of Southern California

Dec 2018

Dedication:
… to my parents…

2

Acknowledgements

I want to thank my parents for their selfless sacrifices for me and their continuous love through
my entire life

Thanks to my colleagues and friends Jiachen Zhang and Yun Li who have helped me in my studies
and have contributed to this dissertation.

Thank you to Professor Amy Childress for providing me with great advices on my thesis proposal
and my Thesis defense

Thank you to Professors Kelly Sanders, Yan Liu, and Jiu-Chiuan Chen for reviewing my thesis
proposal and providing me with instructive feedback

And I want to thank my advisor, George, who has been not just present in my academic life, but
has been a fatherly figure for me in the past five years, who has never stopped caring for me, who
has been a role model in my career and personal life for me, who has helped me grow into the
person who I am today without ever letting my past mistakes be a problem in my future. I can’t
thank you enough in words more than the words in this dissertation. Thank you George.

3

Contents
1 Overview ............................................................................................................................... 15
1.1 Background and motivation ........................................................................................... 16
2 Climate Modeling Framework Using Canopy Model in California ..................................... 25
2.1 Introduction .................................................................................................................... 26
2.2 Methodology .................................................................................................................. 27
2.2.1 Model and simulations ............................................................................................ 27
2.3 Results and discussion .................................................................................................... 30
2.3.1 Model evaluation .................................................................................................... 30
2.3.2 Diurnal cycles of urban areas .................................................................................. 31
2.4 Summary ........................................................................................................................ 32
2.5 Figures and Tables ......................................................................................................... 33
3 Modeling the climate impacts of deploying solar reflective cool pavements in California
cities .............................................................................................................................................. 36
3.1 Introduction .................................................................................................................... 36
3.2 Methodology .................................................................................................................. 38
3.2.1 Model and simulations ............................................................................................ 38
3.2.2 Albedo definitions ................................................................................................... 39
3.2.3 Deriving scaling factors to estimate changes in air temperature from adopting cool
pavements for more realistic urban morphologies ................................................................ 43
3.3 Results ............................................................................................................................ 49
4

3.3.1 Spatially resolved climate response to cool pavement adoption ............................ 49
3.3.2 Diurnal cycles ......................................................................................................... 50
3.4 Discussions ..................................................................................................................... 51
3.4.1 Sensitivity of air temperature change to urban fraction and linearity of simulations
51
3.4.2 Sensitivity of air temperature to grid cell albedo and comparison to other modeling
studies 52
3.4.3 Influence of assumed urban morphology on climate response of cool pavements . 55
3.4.4 Comparison to experimental studies ....................................................................... 57
3.4.5 Impacts on pedestrian thermal comfort ................................................................... 58
3.5 Summary ........................................................................................................................ 61
3.6 Figures and Tables ......................................................................................................... 62
4 Observational evidence of neighborhood scale reductions in air temperature associated with
increases in roof albedo ................................................................................................................ 74
4.1 Introduction .................................................................................................................... 75
4.2 Methodology .................................................................................................................. 75
4.2.1 Areas of analysis ..................................................................................................... 75
4.2.2 Defining aggregation areas ..................................................................................... 80
4.2.3 Deriving sensitivities of measured air temperature to LULC properties ................ 80
4.3 Results ............................................................................................................................ 82
5

4.3.1 Sensitivity of temperature to solar power reflected from roofs .............................. 82
4.3.2 Sensitivity of temperature to tree fraction .............................................................. 83
4.3.3 Roof versus non-roof surfaces as contributors to variability in solar power reflected
from neighborhoods .............................................................................................................. 85
4.4 Discussions ..................................................................................................................... 85
4.4.1 Comparison with literature ..................................................................................... 85
4.4.2 Dependence of reported temperature-landcover sensitivities to neighborhood
characteristics ........................................................................................................................ 87
4.4.3 Policy-relevant take-away points ............................................................................ 88
4.5 Summary ........................................................................................................................ 89
4.6 Figures and Tables ......................................................................................................... 90
5 Conclusions ........................................................................................................................... 98
6 Appendix ............................................................................................................................. 103
7 References ........................................................................................................................... 106

6

List of Figures

Figure 2-1.The three nested domains (d01, d02, d03) used in the simulations with corresponding
spatial resolutions of 36, 12, and 4 km. The innermost domain (do3) is the main area of interest
and contains the entire state of California. .................................................................................... 33
Figure 2-2. Average diurnal cycle 2m air temperature for a) entire domain, b) land areas in the
domain, c) urban areas in the domain. Spatial averages at each time steps are calculated for each
region. The red, black, and yellow bar shows Summer, yearly, and winter averages, respectively.
The red and blue area shows the ranges of diurnal temperature for corresponding season. ......... 34
Figure 2-3. Comparison of the control simulation to NCDC observations for daily mean
temperature (a,c) and weekly accumulated precipitation (b,d). Probability density functions are
shown separately for simulations and observations in (a,b). Spatial distributions of biases (model
minus observation) are shown in (c,d) for locations corresponding to weather stations.
Observations are from the National Climate Data Center (NCDC) Glocal Surface Summary of
the Day (GSOD). The purple lines (c,d) bound the model domain. ............................................ 35
Figure 3-1. Maps of (a) the three urban classifications extracted from the NLCD dataset at 30 m
spatial resolution, and (b) the resulting urban fraction for each model grid cell. ......................... 62
Figure 3-2. Maps of (a) grid cell albedo for the control scenario (CTRL) at the start of the
simulation (Sep 2001), (b) grid cell albedo increases after increasing pavement albedo by 0.1
(COOL_LOW – CTRL), and (c) grid cell albedo increases after increasing pavement albedo by
0.4 (COOL_HIGH – CTRL). ........................................................................................................ 63
Figure 3-3. Relationship of changes in urban albedo to pavement albedo for different assumed
urban canyon morphologies. The assumed building height to pavement width ratio is 0.60, 0.80,
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and 1.0 for low density, medium density, and high intensity NLCD urban types, respectively.
The dashed line represents the case of no urban canopy. ............................................................. 64
Figure 3-4. Average surface air temperature reductions for different California cities versus
average urban fraction. Each point represents the mean value of a city. Temperature reductions
are shown for both COOL_HIGH – CTRL and COOL_LOW – CTRL. Green indicates 24 hour
averages while black shows values for 14:00 LST. Best fit linear regressions are shown. Unlike
other figures, city means include all grid cells within the city boundary and are not weighted by
urban fraction ................................................................................................................................ 65
Figure 3-5. Temperature changes from cool pavement adoption (COOL_HIGH – CTRL) for (a)
summer average at 14:00 LST; (b) summer average at 20:00 LST; (c) winter average at 14:00
LST; and (d) winter average at 20:00 LST. Only differences that are significant at 99.5%
confidence interval are shown. ..................................................................................................... 66
Figure 3-6. Changes in net (upward – downward) all-wave (short-wave + long-wave) radiance at
the top of model (10000 Pa) for COOL_HIGH – CTRL for (a) summer average at 14:00 LST
and (b) winter average at 14:00 LST.. Only differences that are significant at 99.5% confidence
interval are shown. ........................................................................................................................ 67
Figure 3-7. Seasonal average changes in diurnal profiles for COOL_HIGH – CTRL of (a)
surface air temperature in Los Angeles, (b) same but for Sacramento, (c) planetary boundary
layer height (PBL height) for Los Angeles, and (d) same but for Sacramento. Spatial averages for
each city are weighted by grid cell values of urban fraction. Fall corresponds to September-
October-November, Winter is December-January-February, Spring is March-April-May, and
Summer is June-July-August. ....................................................................................................... 68
8

Figure 3-8. Diurnal cycle of changes in surface energy fluxes (COOL_HIGH - CTRL) averaged
spatially over the city of Los Angeles and temporally over summer. Net shortwave (SW)
radiation and ground heat flux (GRDFLX) is positive downward, while net longwave radiation
(LW), sensible heat (SH), and latent heat (LH) fluxes are positive upward. Spatial averages are
weighted by grid cell values of urban fraction. ............................................................................. 69
Figure 3-9. Surface air temperature differences versus changes in grid cell albedo for our study,
and Millstein and Menon (2011), which focused on cool roofs. Each point represents a different
city. Best fit linear regressions using least squares are also shown, suggesting surface air
temperatures are reduced by about 0.3 C per 0.1 increase in grid cell albedo. To make our study
more comparable to Millstein and Menon (2011), city means in this figure are not weighted by
urban fraction as in our other figures (besides Figure 12). ........................................................... 70
Figure 4-1. The location of personal weather stations in the Los Angeles basin. The stations are
color coded by “region” as defined in the legend. ........................................................................ 90
Figure 4-2. The aggregation area (green circle of radius 500 m) around an example weather
station (red dot) in SFV. The underlying imagery shows the building footprint dataset.............. 91
Figure 4-3. Afternoon (14:00-15:00 local time) temperature versus daily average reflected solar
power in central Los Angeles for each day during July 2015. Each subpanel represents one day
and each point represents a single weather station and associated LULC parameter. Slopes from
least squares regressions are used to obtain daily sensitivities of temperature to the LULC
parameter under investigation. The mean irradiance (W m-2) between 14:00-15:00 is shown
above each subpanel. Red circular points are removed as outlier locations. The red dotted
regression line corresponds to linear regressions using all points (including outliers) and the
9

black line corresponds to those using only the black square points. The size of each point is
proportional to its influence. ......................................................................................................... 92
Figure 4-4. Boxplot for diurnal cycle of sensitivity of temperature to (a,b) daily average solar
power reflected by roofs, and (c,d) tree fraction. Panels (a) and (c) are for central Los Angeles
and panels (b) and (d) are for SFV. Each box contains the sensitivities per hour for the entire
month (July 2015). The hours with statistically insignificant sensitivities (see Section 4.2.3 for
details) have red hatching. Boxes show the inner-quartile range, whiskers show [(Q1-1.5 IQR),
(Q3+1.5 IQR)], and the black line within the box represents the median. ................................... 93
Figure 4-5. Daily average solar power reflected by roofs vs tree fraction for each region. Each
point represents a different neighborhood. ................................................................................... 95
Figure 4-6. Comparison of daily average solar power reflected from (a) roof and (b) non-roof
surfaces versus daily average solar power reflected from all surfaces in each corresponding
neighborhood. Least squares linear regressions are also shown separately for the two areas (i.e.,
SFV and central Los Angeles). The higher coefficients of variation (R2) in panel (a) versus (b)
suggest that variations in roof albedo are responsible for the majority of variations in
neighborhood albedo ..................................................................................................................... 96
Figure 6-1. The figure shows areas with significant 24 h averaged temperature reductions
between CTRL scenario and COOL_HIGH scenario for different seasons. Showing (a) fall, (b)
winter, (c) spring, and (d) summer.............................................................................................. 104
Figure 6-2. The figure shows areas with significant 24 h averaged temperature reductions
between CTRL scenario and COOL_LOW scenario for different seasons. Showing (a) fall, (b)
winter, (c) spring, and (d) summer.............................................................................................. 105

10

List of T ables

Table 3-1. Scaling factors for different building climate zones in California by season. ............ 71
Table 3-2. Unscaled and scaled annual difference in surface air temperature from increasing
pavement albedo by 0.4 (COOL_HIGH – CTRL), along with the annual mean scaling factors
and building climate zone for each city. ....................................................................................... 72
Table 4-1. Statistics of observed temperatures per region ........................................................... 97
Table 4-2. The temperature reductions per roof and neighborhood average albedo increase. .... 97

11

Abstract

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 Niño or La Niña
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 °C, and the hottest temperature
in the urban areas were 34.97 °C. Comparing this simulation to 105 weather stations in California
12

suggested an overall mean bias (model minus observation) of −0.30°C, 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°C 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°C (Palm Springs) to 0.86°C
(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
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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°C
and 0.49°C 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°C and 2.8°C per 0.1
increase in neighborhood average roof albedo, and 7.3 °C and 9.8°C 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.

14

1 Overview

15

1.1 Background and motivation
Cities are generally hotter than rural surroundings due to a phenomenon known as the urban heat
island (UHI) effect (Oke 1982; Arnfield 2003). This effect occurs as the process of urbanization
replaces natural vegetative cover with dry impervious materials that are thermally massive and
have low albedo (Taha 1997; Grimmond 2007). The fraction of the world population that lives in
urban areas was 54% in 2014, and is expected to rise to 66% by the year 2050 (WHO), suggesting
that UHIs may affect more people in the future. The magnitude of the UHI generally intensifies
with the size of the city (Oke 1973) and the density of the urban area compared to the surrounding
rural area (Wong 2011), so bigger cities experience this phenomenon in higher extent and they
contain more people.
Compounding UHIs is the fact that cities are facing increased warming due to the local impacts of
global climate change; Sun et al. (2015) found that most areas in the greater Los Angeles region
could see an increase in 60 – 90 extremely hot days per year by end of century under a high
emissions scenarios (RCP8.5), posing threats to public and environmental health in addition to
straining energy resources. Although the main course of action to prevent climate change and
associated temperature rise is to address the main cause (rising emissions of greenhouse gases),
even with the unrealistic assumptions of decreasing emissions (which requires global cooperation),
we are expecting a rise in global temperature (IPCC 2014), and therefore, rise in the heat waves
(sun et al. 2015).
In order to make informed decisions regarding the rise of temperatures in urban areas, we need to
have adequate information identifying the root cause of the UHI, quantifying its impact, and
mitigating its effects. To optimally mitigate urban heat islands, we need to have a better
understanding of available options and the effectiveness of different strategies in the specific
16

region of interest. The study of the urban heat islands in this work has been separated into
identification, investigating the causes, and the mitigation of the effects of the urban heat islands.
We will address each of the above steps in the following chapters.
While warming from climate change requires global action to mitigate, the UHI is a city-specific
phenomenon with solutions that cities can implement locally. UHI countermeasures, such as cool
roofs and tree canopy cover, have been found in modeling studies to reduce urban temperatures
when implemented at city scale (Santamouris 2014). Therefore, there are actions that cities can
now take to mitigate UHIs, which will decrease the threat of future extreme heat dangers from
climate warming. To design and implement appropriate countermeasures, cities need to
characterize urban heat and its causes (Taha 2013; Sailor et al. 2016).
Urban heat islands (UHIs) are categorized as either (a) skin-surface UHIs (i.e., comparing urban
versus rural surface temperatures) or (b) air-temperature UHIs (i.e., comparing urban versus rural
air temperatures near the surface). Air-temperature UHIs are relevant to building energy use,
thermal comfort, public health, pollutant emissions and formation, and climate, and as such are the
focus of this study. While satellite observations can provide spatially extensive temperature
observations, they are able to characterize only surface temperature and not air temperature. (The
other limitation with remote sensing is the coarse resolution of images, order tens to hundreds of
meters, which results in homogenous measurements for small neighborhoods (Voogt & Oke 1998).
Thus, characterizing air temperatures relies on ground-based observations such as fixed weather
stations or mobile transects. Acquiring observations from fixed weather stations with sufficiently
high spatial density to be representative at neighborhood-to-city scale is a major challenge in
characterizing urban air temperatures. In addition, mobile transects can be useful for characterizing
air temperatures for a neighborhood on a particular day, but characterizing large areas (e.g., entire
17

cities) over temporal scales that are relevant for different meteorological regimes is prohibitive.
Studies on UHIs that rely on numerical weather prediction models can simulate both surface and
air temperatures. While these models continue to resolve an increasing number of processes
relevant to urban physics, it is critical to conduct studies focused on air temperature UHIs using
observations.
The UHI results in part from the transformation of natural land cover including trees and vegetation
to pavements, buildings, and other urban infrastructure (Pomerantz et al. 1999). Reductions in
vegetation coverage can lead to decreases in evaporative cooling, and thus increases in air
temperatures in urban regions (Peng et al., 2012). Man-made materials (e.g., asphalt concrete) used
in roads and buildings usually have low albedo and high thermal inertia, leading to (a) high
absorption of shortwave solar radiation, and (b) high storage of energy (Oke 1982). In addition,
street canyons between buildings usually have low sky view factors, which can lead to reductions
in longwave radiative losses from the city (Oke 1981). Other factors such as the size of the city
(Oke 1973), shading from buildings and trees (Kusaka et al. 2001), urban irrigation (Vahmani et
al. 2016), and changes in surface roughness length (Vahmani et al. 2016) and anthropogenic
heating (Peng et al. 2012) can also impact urban temperatures.
The UHI can be defined over a variety of spatial extents ranging from neighborhood to city scale
(Taha 1997). Most past studies have investigated UHIs at city scale by comparing temperatures of
urban regions to surrounding rural areas. Investigating the UHI in Los Angeles at city scale is
tricky, as this megapolitan region is surrounded by ocean and mountains, and thus there are no
obvious rural background for reference. Furthermore, there is a strong temperature gradient
resulting from the onshore sea breeze, which can make teasing out temperature - landuse
relationships difficult. Temperature variations within the Los Angeles region are the result of the
18

superposition of effects from multiple UHIs and the sea breeze (Taha, 2017). For this reason, this
study focuses on investigating relationships between air temperature and land cover properties for
clusters of neighborhoods within the Los Angeles area; the clusters cover a sufficiently small
region such that the effects of the sea breeze on temperatures is negligible.
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. Utilizing global scale satellite observations for 419
cities, Peng et al. (2012) found that the daytime surface urban heat island intensity (SUHII) is
associated with urban-rural differences in vegetation cover, while the nighttime SUHII is
associated with albedo and anthropogenic heat differences. Similarly, other satellite-based studies
highlighted the correlation of SUHII with albedo and vegetation fraction for Rotterdam in the
Netherlands (Klok et al., 2012) and 32 cities in China (Zhou et al., 2014). Sun et al. (2009) used
mobile transects and remote sensing to identify the important role of anthropogenic heat and
vegetation cover on UHI formation in Phoenix during the winter season. Yan et al. (2014) assessed
the correlation between air temperatures measured by mobile transects and different land cover
properties at neighborhood scale in Beijing and found that tree faction can explain most
summertime temperature variations at noon and night. Model simulations from numerous studies
have found that increasing albedo and vegetation cover can reduce urban peak surface and air
temperatures (e.g., Sailor, 1995; Taha, 1997). Therefore, increasing urban albedo and vegetation
fraction have been proposed as measures to reduce the urban heat island effect.
Solar reflective “cool” surfaces (i.e., cool roofs, walls, and pavements) that increase urban albedo
can decrease urban temperatures because they absorb less solar radiation than traditional dark
surfaces, maintain cooler skin temperatures, and therefore transfer less heat into the atmosphere.
19

Past modeling studies predict that adopting cool roofs can effectively reduce urban air
temperatures in Los Angeles (Zhang et al, 2018; Vahmani et al, 2016), Baltimore-Washington (Li
et al, 2014), New York (Lynn et al, 2009), and other cities in the United States (Georgescu et al,
2012; Taha et al, 1997; Millstein and Menon, 2011). Zhang et al. (2016) found an annual- and
global- average reduction of 0.40°C in the urban heat island effect due to increasing roof albedo
globally from 0.15 to 0.90. Note that nearly all past studies investigating the influence of cool
surface adoption on urban air temperatures are based on (1) results from computational models,
and (2) the assumption that cool surfaces are uniformly adopted citywide; in reality, cool surfaces
may be adopted in a patchwork fashion at neighborhood scale. There is a lack of research that uses
real-world observations to assess the influence of adopting cool surfaces on neighborhood
temperatures. Using satellite observations, Mackey et al (2011) found stronger land surface
temperature reductions induced by increasing urban albedo relative to increasing green vegetation
in Chicago. However, the effectiveness of cool roofs in reducing near-surface air temperatures (as
opposed to surface temperatures) still needs to be verified by observations in various cities.
To fill the aforementioned research gaps, in this study we investigate the influence of two
important land cover properties (i.e., albedo and tree coverage) on neighborhood-scale near-
surface air temperatures for two clusters of neighborhoods in the greater Los Angeles area. We use
high spatial density meteorological observations to derive the sensitivity of air temperatures
observed at each weather station to corresponding spatially aggregated albedo and tree coverage.
In this way, we compute associations between air temperatures versus albedo and tree fraction.
Various state (cite title 24) and city-level (cite LA requirement) policies have led to cool roof
adoption on some commercial and residential buildings in Los Angeles, allowing us to analyze air
temperature differences between neighborhoods with extensive use of cool roofs versus traditional
20

dark roofs. To our knowledge, our study for the first time investigates associations between
neighborhood-scale near-surface air temperatures and roof albedo.
With urbanization also comes increases in air conditioning use and traffic, both of which emit
waste heat to the environment. The heat island effect has been investigated using a variety of
methods including numerical modeling (e.g., Li and Bou-Zeid 2013; Li and Bou-Zeid 2014; Zhao
et al. 2014; Vahmani and Ban-Weiss 2016a; Vahmani et al. 2016) and ground and remote-sensing
based observations (e.g. Voogt and Oke 2003; Fast et al. 2005; Imhoff et al. 2010, Peng et al. 2012;
Mackey et al. 2012). Cities are also currently warming due to the local impacts of global climate
change (e.g. Garfin 2013).
Urban heat mitigation strategies are a set of approaches that modify the land surface of cities in an
effort to reduce urban temperatures. Previous research on heat mitigation strategies has mostly
focused on the urban climate impacts of solar reflective cool roofs, vegetative green roofs, and
street level vegetation. Solar reflective cool roofs absorb less radiation than standard dark roofs
and thus stay cooler in the sun, leading to cooling of surrounding air. Vegetative (“green”) roofs
and street level vegetation modify the surface energy balance in favor of increased latent and
decreased sensible heat fluxes, which also has the effect of cooling surrounding air (Santamouris
2014). Changes in vegetative cover can also modify soil moisture with corresponding changes in
soil thermal properties. Ground heat fluxes can be subsequently affected with consequences on
surface and air temperatures especially at night (Vahmani and Ban-Weiss, 2016a). Vegetation also
shades the surface, which generally decreases surface temperatures (Taleghani and Ban-Weiss
2016). Analogous to cool roofs, solar reflective cool pavements can reduce absorbed solar
radiation and stay cooler in the sun than standard low albedo pavements (e.g. asphalt concrete).
However, the climate impacts of cool pavements have not been widely studied in previous
21

research. Another form of heat mitigation strategy is deploying solar photovoltaic panels in urban
areas. The main function of these panels is to create energy from incoming solar radiation, but they
can potentially reduce urban heat island (Taha 2013a; Salamanca et al. 2016).
We summarize here previous studies that use mesoscale numerical weather prediction models to
investigate the urban climate impacts of increasing albedo. Sailor (1995) found that increasing the
albedo of the Los Angeles basin by an average of 0.08 reduced peak summer temperatures by up
to 1.5 °C. Rosenfeld et al. (1995) used a mesoscale meteorological model to simulate the effect of
increasing albedo and found a peak temperature reduction exceeding 3 °C in the afternoon over
Los Angeles after increasing the albedo in developed urban areas by 0.13 on average. Rosenfeld
et al. (1998) investigated the impacts of increasing the albedo of roofs and pavements by 0.35 and
0.25, respectively. They found that hourly maximum temperature decreased by 1.5 °C in Los
Angeles. Taha (2008a) found that increasing the albedo of urban surfaces in Sacramento, CA could
reduce temperatures by up to 3 °C. Taha (2008b) simulated many scenarios of surface albedo
perturbation and found that resulting changes in temperatures depend on the scenario assumed and
the city under investigation. Maximum spatial-mean temperature reductions for most cities
investigated were up to 1-2 °C. Taha (2008c) simulated the meteorology of Houston, Texas using
the MM5 mesoscale model and found temperature reductions of up to 3.5 °C in some locations
with decreases in albedo of 0.2, 0.18, and 0.05 for roofs, pavements and walls, respectively. Using
a modified mesoscale model, Lynn et al. (2009) investigated mitigation strategies in New York
City and found temperature decreases of about 1.5 °C and 2 °C at 12:00 and 18:00h local standard
time (LST), respectively, after increasing albedo of impervious surfaces by 0.35. The larger
nighttime cooling found by Lynn et al. was in contrast to some of the aforementioned previous
studies that had found maximum cooling from albedo increases during the day. Millstein et al.
22

(2011) modeled the climate of the entire United States and found city-mean afternoon temperature
reductions of 0.1 to 0.5 °C after increasing roof and pavement albedo by 0.25 and 0.15,
respectively. Georgescu et al. (2013) suggested that cool roof adoption could counter future
warming from urbanization in Arizona’s sun corridor by 50%. Georgescu et al. (2014) studied the
effects of heat mitigation scenarios and compared them with projected air temperature increases
caused by multiple scenarios of future urbanization over the United States. The results for
California showed a reduction in surface air temperature of 1.5 °C after increasing roof albedo to
0.88 from their assumed baseline roof albedo. Using an updated urban canopy model that can
resolve multiple ground and roof types per grid cell (Li and Bou-Zeid 2014), Li et al. (2014)
investigated the effects of cool (and green) roofs for the Baltimore-Washington region. They found
that increasing the albedo of all roofs to 0.7 from 0.3 reduced the city-mean surface and near
surface air temperature urban heat island by up to about 3 °C and 0.7 °C respectively; maximum
surface and surface air temperature reductions were simulated to be in afternoon and evening,
respectively. A review of past studies on cool materials (Santamouris 2014) suggested that urban
albedo increases of 0.1 can lead to a mean reduction in ambient air temperature of about 0.3 °C.
Zhang et al. (2016) investigated the climate impacts of adopting cool roofs around the globe and
found that the mean urban heat island effect was reduced by 0.4 °C.
As the study of heat mitigation strategies has evolved, so have the weather prediction models
implemented in these studies. Some of the aforementioned studies have used models that represent
urban areas as two-dimensional tiles, while more recent developments have included
parameterizations that account for the influence of urban canopies on radiation transfer (e.g.
shading of the ground by buildings) and wind flows (see a summary in Chen et al. 2011). For
example, Kusaka et al. (2001) present a single layer urban canopy model that parameterizes the
23

effects of urban canopies on incoming and outgoing shortwave and longwave radiation, resolving
the heat balance and surface temperatures of walls, pavements, and roofs. Kusaka et al. (2004)
coupled this UCM to a local circulation model to better capture the effects of the canopies in a
meteorological simulation. Modern weather prediction models, including the Weather Research
and Forecasting Model (WRF), include the option of using modified versions of this urban canopy
parameterization, among other more complex formulations (Chen et al. 2011). More recent
developments include accounting for different land cover types within canopies and on rooftops
(Li and Bou-Zeid 2014). We note that as urban models become more complex and require more
inputs to describe urban morphology and thermophysical properties, the need for observational
data describing these properties becomes increasingly important.
While past studies have investigated the urban climate impacts of increases in roof or urban-scale
albedo, few studies have studied solar reflective cool pavements, and even fewer (e.g. Taha 2013b,
Rosenfeld et al. 1998) have singled out the effects of cool pavements. Another study (Taleghani
et al. 2016) investigated the impacts of cool pavements on air temperatures and human thermal
comfort, but this study focused on micrometeorological changes within a neighborhood, and not
on mesoscale changes. Since pavements are at the bottom of urban canopies, the sensitivity of
climate (e.g. surface air temperature) to albedo is likely to be different for pavements versus roofs.
In this study, we use a numerical weather prediction model coupled to a land model that includes
urban canopy parameterizations to investigate the impacts of increasing pavement albedo on
California’s climate.

24

2 Climate Modeling Framework Using Canopy Model in California

25

2.1 Introduction
This study focuses on evaluating the climate modeling using WRF on the domain of California. In
order to evaluate the effect of implementing solar reflective cool pavement, which will be
described in the next chapter, we need to set-up a reliable modeling framework. In this study, I
simulated the state of California, using parameterization similar to Zhao et al. (2011), that
described a specification of physical properties in the model with the least amount of error, using
the same model and similar domain. The model uses three nested domains inside each other, with
resolutions of 36 km, 12 km, and 4km from outer nest to the most inner nest. We only analyze the
inner domain, as it is the highest resolution, and the other two only exist to provide boundary
conditions. The model uses additional inputs such as sea surface temperature, and high resolution
urban land cover datasets to simulate the dense urban areas such as Los Angeles with more detail.
After the simulations, we evaluate the results of the model using ground observations for
temperature and precipitation over the domain and use the final modeling framework in the next
part of this study, to understand the impacts of solar reflective pavements.

26

2.2 Methodology
2.2.1 Model and simulations
We employ the Weather Research and Forecasting (WRF) Model version 3.5.1 (Skamarock et al
2008). The WRF is a nonhydrostatic, compressible atmosphere model and is coupled to the
community Noah land surface model (LSM) (Chen et. al. 2001). WRF was set up using three
nested domains using two-way feedback mechanism between the domains, with corresponding
spatial resolutions of 36, 12 and 4 km (Figure 2-1 a). The inner model domain encapsulates the
entire state of California. We employ 30 layers in the vertical from the surface to 10 kPa.
The physics of the planetary boundary layer (PBL), cumulus clouds, and cloud microphysics are
parameterized using the Yonsei University (YSU) scheme (Hong et. al. 2006a), Grell scheme
(Grell 1993), and WRF Single Moment 6 class (WSM6) scheme (Hong and Lim 2006),
respectively. Note that the inner domain does not use the Grell scheme as cumulus clouds are
assumed to be resolved at 4 km resolution. A previous study (Zhao et al. 2011) using the same
model and spatial resolution over California tested multiple combinations of physics
parameterizations. They found that the aforementioned combination of parameterizations led to
the lowest root mean square error compared to observations. Longwave and shortwave radiative
transfer are simulated using the Rapid Radiative Transfer Model for GCMs (RRTMG) scheme, an
update to the Rapid Radiative Transfer Model (RRTM) (Mlawer et. al. 1997). RRTMG uses the
Monte Carlo Independent Column Approximation (MCICA) method of random cloud overlap, a
statistical method to resolve sub-grid scale cloud variability. The single layer Urban Canopy Model
(UCM) is used in order to capture the influence of urban canopies on surface-atmosphere
interactions. The UCM is described in detail in Chen et al. (2011), and is described briefly here.
27

In WRF, the single-layer UCM parameterizations are based on Kusaka et al. (2001) and Kusaka
and Kimura (2004). These parameterizations are employed in grid cells for which the dominant
land cover type is urban. The dominant land cover type for each cell is determined using the
MODIS 20 category land-use dataset in our study. Cells that are deemed urban are divided into
impervious and pervious portions. Heat and momentum fluxes are determined using the UCM in
the impervious portion of the cell, while the Noah LSM is used to determine these fluxes in the
pervious portion. The fraction of the grid cell that is impervious is determined using the “urban
fraction” parameter explained in the next paragraph. The influence of urban geometry on surface-
atmosphere exchange of shortwave and longwave radiation is taken into account assuming
infinitely-long street canyons. The model computes surface skin temperatures for each urban sub-
facet (roof, wall, and road) using surface energy balances. Monin-Obukhov similarity theory then
predicts sensible heat fluxes from each sub-facet. The sensible heat flux in the impervious portion
of the grid cell is subsequently combined with that of the pervious portion, and then transferred to
the atmosphere model. Momentum transfer uses stability functions to estimate the canyon drag
coefficient and friction velocity. The vertical profile of wind speed is assumed to be exponential.
The friction velocities over the impervious and pervious portions of the grid cell are then combined
and used in the atmosphere model’s boundary layer scheme (Chen et al. 2011).
The fraction of each grid cell that is covered with urban areas (urban fraction) was computed using
the United States Geological Survey (USGS) National Land Cover Database (NLCD) (Homer et
al. 2007) for 2001. The NLCD data characterizes land cover at 30 m resolution and includes four
different urban types: ‘developed open space’, ‘low intensity’, ‘medium intensity’, and ‘high
intensity’. Impervious surfaces account for less than 20%, 20% to 49%, 50% to 79%, and 80% to
100% of total cover, respectively. Figure 2-1b shows the urban regions in California that are
28

classified as low, medium, or high intensity and have corresponding urban fractions of 50, 90, and
95%, respectively (Chen et al. 2011). Details on urban morphology including building height, roof
width, road width, and others (see Table 1 in Chen et al. 2011) are obtained using observations
from the National Urban Data and Access Portal Tool (NUDAPT) where available (i.e., for
portions of Los Angeles, San Diego, Riverside, and Garden Grove). Where NUDAPT observations
are unavailable, urban canopy parameters that depend on urban morphology are assumed to be the
same as described in Chen et al. (2011). For example, low, medium, and high intensity urban types
assume building heights of 5, 7.5, and 10 m, respectively, with corresponding road widths of 8.3,
9.4, and 10 m. Roads are assumed to extend the full width of the urban canyon, with no “setbacks”
between roads and buildings. We were able to obtain additional data on urban morphology (for
regions without NUDAPT data) after the simulations were complete. Given the importance of
accurately describing urban morphology on quantifying the climate consequences of adopting cool
pavements, we carried out offline simulations using an urban canyon albedo model (Rosado et al.
2017) to scale our results for each city. See the Methods section 3.2.3 and Discussion section 3.4.3
for more details.
Roof and wall albedos were assumed to be 0.20. While roof albedo should have negligible effect
on the sensitivity of climate to pavement albedo, assumed wall albedo may play a role through
reflections between walls and pavements. The mechanics of calculating canyon albedo and the
solar heat gain of the urban canyon are detailed in the Methods section 3.2.3.
Boundary and initial conditions are based on the National Center for Environmental Prediction
(NCEP) North American Regional Reanalysis (NARR) data. These data have horizontal spatial
resolution of 32 km, close to that of the outer domain of the model, and 29 vertical levels ranging
29

from 100 to 10 kPa. Real-time global NCEP sea surface temperature (SST) data are used for the
portions of the domain over the Pacific Ocean.

2.3 Results and discussion
2.3.1 Model evaluation
We evaluate the control (CTRL) simulation by comparing modeled surface air temperature and
accumulated weekly precipitation to weather observations from the National Climatic Data Center
(NCDC) Global Surface Summary of the Day (GSOD). Data from 105 weather stations in
California were used in the evaluation. Histograms and Q-Q plots of simulated and observed daily
mean temperatures indicate that the model reproduces the observed temperatures well but with a
slightly narrower distribution (Figure 2-3a). Assessing the spatial variation in bias (model minus
observation) indicates that the model captures well the daily mean temperatures in the Sacramento
and San Joaquin Valley, and has larger biases in the coastal portions of Los Angeles and the San
Francisco Bay area, as well as the desert regions in the southeastern portion of California. The
overall mean bias for the entire state of California is -0.30 °C. Precipitation is quite well modeled
though weeks with < 5 mm of precipitation are more frequent than indicated by observations, and
weeks with  5 mm are less frequent than indicated by observations. Similar to daily mean
temperatures, biases in precipitation are low in most parts of the Sacramento and San Joaquin
Valley, while coastal regions show relatively larger biases. The overall mean normalized bias in
weekly accumulated precipitation during the wet season (November to March) was 150%.
30

2.3.2 Diurnal cycles of urban areas
Diurnal cycle of the entire inner domain, the land areas in inner domain, and the urban areas in
inner domain are calculated. For each diurnal cycle, near surface air temperature (T2) is spatially
averaged over the said area in each time step and then processed into daily time series. Figure 2-2
shows the mean diurnal cycles for summer, winter and yearly average.
The temperature gradients in urban areas (Figure 2-2c), are sharper in the late morning to noon,
compared to that of entire domain and land areas in the domain (Figure 2-2b&c). The temperature
in urban areas trends toward the local minima during the night quicker than other two aggregated
areas. This is to be expected from the aggregation of entire domain, as a fraction of the domain is
simulated over the oceans and water has higher heat capacity than any other material, but the fact
that urban areas lose their heat during evening faster than land areas are not expected.
The temperature ranges in the entire domain (Figure 2-2a), is less than other two aggregated area,
which is likely due to the normalization by high heat capacity of water. The maximum temperature
in land areas reaches to 36 °C, higher than the other two aggregation which is caused by the arid
desert regions in the east of California. The minimum mean daily temperature of the land areas in
the domain is lower than the other two aggregated area, which is representing the high elevation
mountains that are cover in snow during winter time.
The seasonal patterns are similar in all regions, with one exception that urban areas are the only
areas where the maximum range of winter temperature can exceed the minimum temperature of
the summer.
31

2.4 Summary
Availability of a climate modeling framework that can capture and predict meteorological
parameters, specially air temperature, is essential in understanding the effects of land cover
changes on the local climate. In this study we used WRF model to simulate the climate of
California with spatial resolution of 4 km. The physics of the planetary boundary layer (PBL),
cumulus clouds, and cloud microphysics are parameterized using the Yonsei University (YSU)
scheme, Grell scheme, and WRF Single Moment 6 class (WSM6) scheme, respectively. We
incorporated 30 m resolution NLCD dataset as land cover input for the model, to capture the
different settings of urban area, which is the focus of our study. The domain was simulated from
Sep 2001 to Aug 2002, to capture an entire year of simulation that are going to be used as control
scenario in the future studies. The simulations were done in set of three, to provide reference for
the noises of the model to be compared with results of perturbations in future studies. Each of the
simulations had different starting date, to average out the initial effect that can be carried for
months.
The modeling results were compared with ground measurements from weather stations across the
domain, and showed a mean bias of -0.3 C for near surface air temperature parameter. Precipitation
is quite well modeled though weeks with < 5 mm of precipitation are more frequent than indicated
by observations, and weeks with  5 mm are less frequent than indicated by observations.
Temperature and precipitation seems to have less bias in Sacramento and San Juaquin valley than
coastal and desert regions.
The diurnal cycle of urban areas shows spatial mean temperature ranging from 1 °C to 34 °C, and
comparison of the diurnal cycle in urban areas and non-urban areas shows higher temperature
gradients in the late morning in urban areas.
32

2.5 Figures and Tables

Figure 2-1.The three nested domains (d01, d02, d03) used in the simulations with corresponding spatial
resolutions of 36, 12, and 4 km. The innermost domain (do3) is the main area of interest and contains the
entire state of California.

33

Figure 2-2. Average diurnal cycle 2m air temperature for a) entire domain, b) land areas in the domain, c)
urban areas in the domain. Spatial averages at each time steps are calculated for each region. The red, black,
and yellow bar shows Summer, yearly, and winter averages, respectively. The red and blue area shows the
ranges of diurnal temperature for corresponding season.

34

Figure 2-3. Comparison of the control simulation to NCDC observations for daily mean temperature (a,c)
and weekly accumulated precipitation (b,d). Probability density functions are shown separately for
simulations and observations in (a,b). Spatial distributions of biases (model minus observation) are shown
in (c,d) for locations corresponding to weather stations. Observations are from the National Climate Data
Center (NCDC) Glocal Surface Summary of the Day (GSOD). The purple lines (c,d) bound the model
domain.

35

3 Modeling the climate impacts of deploying solar reflective cool
pavements in California cities

3.1 Introduction
This part of the study focuses on simulating the climate impacts of implementing the solar
reflective solar pavements in the domain of California, using the WRF modeling framework that
was set-up in the last part of the study. Parallel to the control scenario, we created two other
36

scenarios with implemented cool pavements. The simulations were done in three sets of ensembles,
and the ensembles were used to compare the changes in scenarios with the random noises in each
run of the model. The first scenario is the control scenario, with pavement albedo set to value of
0.2. This value is obtained from parallel observational studies that were part of the project, and is
typical albedo of California pavements. The other two scenarios are simulated with increased
pavement albedo of 0.2 and 0.5, to quantify the effects of cool pavements in different ranges of
albedo. We used t-distribution to extract significant effect from implementation of cool pavements
with 99.5% confidence level. The results of this study are used as the base for evaluating the
climate impacts of deploying pavements in California, which is under test implementation on
selected roadways in Los Angeles. This study is the first of its kind to investigate the effect of cool
pavements using a modeling framework.

37

3.2 Methodology
3.2.1 Model and simulations
In this study, we use the modeling framework explained in Chapter 2. The control scenario is used
from the results of the first study, and the perturbed scenarios are using the same physical
processes, but with increased albedo in pavements of urban areas.
To investigate the influence on California climate of the wide spread adoption of cool pavements,
three scenarios were simulated, each with a different pavement albedo. The control scenario
(‘CTRL’) assumed a baseline pavement albedo of 0.10. The first perturbation scenario
(‘COOL_LOW’) assumed that cool pavements with an “aged” albedo 0.20 were deployed in all
urban grid cells in California. This pavement albedo represents a case that is technologically
achievable in the near term. See Section 2.3.4 and Table 3 in a companion report, Gilbert et al.
(2017), for an extensive list of standard and cool pavement types with corresponding measured
albedos. The second perturbation scenario (‘COOL_HIGH’) assumed that cool pavements with
“aged” albedo 0.50 were deployed in all urban grid cells in California. This albedo represents a
high estimate that pushes the boundaries of what is likely to be technologically and economically
feasible. We include this high pavement albedo case because we strive to quantify a general
relationship describing the sensitivity of 2 m air temperature (referred to hereafter as surface air
temperature) to pavement albedo. By including this upper bound case, we can check the linearity
of the temperature-albedo relationship and ensure that results can be interpolated rather than
extrapolated to other pavement albedos of interest. “Aged” albedos refer to steady-state values
after weatherization and soiling has altered the albedo of newly installed pavement. Measurements
of albedo versus age for a variety of standard and cool pavement materials have shown that albedo
stabilizes at most four years after installation, and in many cases in a matter of months (see a
38

summary of pavement albedo measurements in sections 2.3.2 - 2.3.4 and 3.2.1 - 3.2.7 of Gilbert et
al. (2017)). In this study, we focus on bounding the maximum possible impacts of cool pavements.
Thus, most figures compare COOL_HIGH to CTRL, though select results comparing
COOL_LOW to CTRL are also presented. It is important to note that 2 m air temperature as
diagnosed by the single layer urban canopy model in WRF represents an air temperature near the
height of the urban canopy, and not the air temperature 2 m above the pavement (Li and Bou-Zeid
2014).
The main analysis period of focus for this study is 1 September 2001 to 31 August 2002, chosen
to represent the region without strong El Niño or La Niña conditions. To allow for characterizing
uncertainties, we ran three ensemble-members per scenario, for a total of nine simulations. The
simulation start dates for each ensemble-member per scenario were 1 March, 1 May, and 1 July
2001, respectively. Thus, for each ensemble-member, a minimum model spin-up time of two
months was ensured. The statistical significance of differences between scenarios was assessed
using Student’s t-test. Here the differences between ensemble-averaged scenarios were compared
to variations among each of the three ensemble members per scenario for each grid cell. All
presented difference maps omit results for cells that are not significant at the 99.5% confidence
level. Spatial averages over cities in California were created using U.S. Census 2000 Topologically
Integrated Geographic Encoding and Referencing (TIGER) data (U.S. Census 2002), which
provides urban boundaries in a shapefile.
3.2.2 Albedo definitions
In the single-layer urban canopy model in WRF 3.5.1, the surface temperature of each sub-facet is
derived using heat balance equations. Each heat balance includes absorption of shortwave
radiation, absorption and emission of longwave radiation, and convective heat fluxes. The urban
39

part of the grid cell is dry and thus does not include latent heat fluxes. Urban vegetation and
corresponding latent heat fluxes are modeled as part of the ‘natural’ pervious portion of the grid
cell.
We characterize albedo in four different ways. “Pavement albedo” (ρ p) describes all pavement
types with no distinction between sidewalks, roads, and parking lots. “Urban albedo” (ρ u) describes
the impervious portion of grid cells that are deemed urban. It represents the albedo above the urban
canopy and includes contributions from all urban sub-facets (i.e., roofs, pavements, and walls).
While we refer to “urban canopy” as the urban part of the grid cell including wall, roof, and
pavement, the term “urban canyon” refers to the U-shaped cavity formed by a ground surface and
its surrounding walls. The albedo of the canyon (including pavements and walls, but excluding
roofs) is denoted ρc. “Grid cell albedo” (ρg) describes the entire grid cell, including both impervious
and pervious portions. A detailed look at the default model code for the single-layer urban canopy
model used in WRF 3.5.1 shows that all downwelling sunlight is treated as purely diffuse in the
canopy (note that we discuss limitations to this assumption in the Methods section 3.2.3), and then
uses view factors to estimate exchanges of sunlight between the sky, wall, and ground (pavement)
sub-facets. (The model code used for tracking diffuse and direct radiation separately to calculate
building shadows as a function of sun angle (as described in Kusaka et al. 2001) is commented out
in the code. See line 728 in the “module_sf_urban.F” for WRF 3.5.1, or line 853 in WRF 3.7.) The
view factor FX→Y is the fraction of radiant power leaving surface X that is intercepted by surface
Y. However, to help explain the radiation equations in the urban canopy model, we show first that
the radiance (radiant power per unit area) incident on surface Y that originates from surface X is
proportional to FY→X, the view factor from surface Y to surface X.
40

Let A represent area, J denote outgoing radiance, and I represent incident radiance. If the radiance
leaving surface X is JX, the power leaving surface X is AX JX, and the power intercepted by
surface Y is FX→Y AX JX. Applying view factor reciprocity (AX FX→Y = AY FY→X), the
radiance incident on surface Y is
IY = (FX→Y AX JX) / AY = FY→X JX. (1)

The radiance absorbed by the canyon, Scanyon (absorbed power per unit sky area), is computed as a
weighted average of (a) Sground1, the irradiance from the sky absorbed by the ground; (b) Swall1, the
irradiance from the sky absorbed by the walls; (c) Sground2, the irradiance from wall reflection that
is absorbed by the ground; and (d) Swall2, the irradiance from ground reflection that is absorbed by
the walls. Treating the two walls that bound the canyon as a single surface (subscript “wall”),
Sground1 = Jsky Fground→sky (1 – ρground) (2)

Swall1 = Jsky Fwall→sky (1 – ρwall) (3)

Sground2 = Swall1 [ρwall / (1 – ρwall)] Fground→wall (1 – ρground) (4)

Swall2 = Sground1 [ρground / (1 – ρground)] Fwall→ground (1 – ρwall) (5)

41

Let H, W, and L represent the height, width, and (infinite) length of the canyon. Since the sky area
and ground area are each W×L, and the combined wall area is 2×H×L, radiant power absorbed in
the canyon per unit sky area is
Scanyon = [(W×L) (Sground1 + Sground2) + (2×H×L) (Swall1 + Swall2)] / (W×L) (6)
or
Scanyon = (Sground1 + Sground2) + (2×H/W) (Swall1 + Swall2) (7)

The albedo of the canyon is calculated as the fraction of solar irradiance entering the canyon that
exits the canyon. Neglecting secondary reflections within the canyon, the top of canyon albedo
(ρc) is estimated as
ρc = 1 – Scanyon / Jsky (8)

Canyon albedo is a diagnostic variable and, as it is explained later in the Discussion section, is
correlated to temperature changes from albedo modifications. It can be used as a criterion to
extrapolate results simulated here to other values of pavement or canyon albedo change (as further
explained in the Methods section 3.2.3).
Each grid cell consists of two major parts, urban (a.k.a. “impervious”) and vegetative (a.k.a.
(“pervious”). The albedo of the urban part of the gridcell (ρu), is the wighted average between the
pavement and the roof. In the default version of the ρ
u
model, the fraction of pavement in urban
parts are considered equal to the roof fraction of the urban part. The albedo of each grid cell is
computed as
42

ρg = ρu f + ρv (1 – f) (9)

where f is the urban fraction of the grid cell, and ρv is the albedo of the vegetative portion of the
grid cell.
Grid cell albedos (ρg) for the CTRL simulation are shown in Figure 3-1. In California, values of
grid cell albedo range from about 0.05 in the forested regions of the state to 0.28 in the desert
regions. Raising pavement albedo to 0.20 in COOL_LOW from 0.10 in CTRL increases grid-cell
albedo by up to about 0.02 in urban regions around California (Figure 3-1b). Raising pavement
albedo to 0.50 from 0.10 (COOL_HIGH – CTRL) increases grid-cell albedo by up to about 0.08
in the City of Los Angeles (Figure 3-1c). Spatial variations in grid cell albedo changes are caused
by associated variations in urban fraction and urban morphology. We note here that relationships
between pavement albedo change and urban albedo (and therefore grid cell albedo) change depend
on assumed canyon morphologies and calculations of radiative transfer within the canopy, as is
further explained in section 3.2.3 and the Discussion section.
3.2.3 Deriving scaling factors to estimate changes in air temperature from adopting cool
pavements for more realistic urban morphologies
As described in section 3.2.1and 3.2.2, the WRF simulations carried out in this study contain two
limitations for generalizing the effects of pavement albedo on city air temperatures: (1) the
assumed urban morphologies were not based on real-world data for most cities (i.e. they are based
on observations only where NUDAPT data are available), and (2) radiative transfer calculations
within the urban canopy assumed that all sunlight was purely diffuse and thus shadows cast by
buildings were not a function of solar elevation. In this section, we describe our method for
43

enhancing air temperature predictions using real-world data that describe urban morphologies.
(These data became available after the simulations were complete. We were unable to re-run our
simulations given the extreme computational cost of our climatological simulations described in
section 3.2.1.) Scaling factors were developed using an offline urban canyon albedo model
(UCAM) that tracked direct and diffuse solar radiation, allowing for improved calculations of
building shadows. The development of the UCAM and the corresponding offline simulations are
described in detail in a companion paper (Rosado et al. 2017), and briefly summarized here.
The single layer urban canopy parameterization in WRF that was employed in this study assumes
that canyons are infinitely long without any spacing between walls and pavements (see Figure 2
in Chen et al. (2011)). The pavement is assumed homogeneous with no distinction between
roadways and sidewalks. Building “setbacks” (gaps between streets and buildings) are not
resolved. Adding these details to the urban canopy model would change the relationship between
pavement albedo and canyon or urban albedo. Since we are ultimately interested in the influence
of changes in pavement (roadway) albedo on climate, the influence of building setbacks are also
included in the UCAM model (Rosado et al. 2017). We note that in the single layer urban canopy
parameterization employed here, urban albedo is not a function of canyon orientation since all
shortwave radiation is assumed diffuse. Even the model code that tracks direct solar radiation
(commented out in the default single-layer UCM) reports results that are independent of canyon
orientation; the model computes mean results after considering eight different typical canyon
orientations.
Since albedo modifications are implemented only in the urban part of the grid cell, we can write
Δρ
= 𝑓 ∗ Δρ
(10)
44

Changes in urban albedo and pavement albedo can also be related by
Δρ
Δρ
=
𝛫 𝛫 .
Δρ
Δρ

(11)

where K is the ratio of change in urban albedo to the pavement albedo change, and subscripts 1
and 2 denote estimates of this proportionality for either two different assumed urban canyon
morphologies, or two different modeling methods. Note that K is independent of urban fraction.
Figure 3-3 in the supplementary online material illustrates the relationship of urban albedo to
pavement albedo difference for the three canyon geometries (i.e. building heights and street
widths) associated with NLCD urban classifications (see the Methods section), and also the
limiting case assuming 2D urban surfaces with no canyon. These relationships were developed
using the default model code in the single-layer urban canopy model, which assumes that all
sunlight is diffuse, and assuming that pavements comprise 50% of impervious surfaces. The slopes
of each line represent values for K corresponding to each assumed morphology and assuming
sunlight is purely diffuse. Values of K for high, medium, and low intensity urban are 0.13, 0.16,
and 0.20, respectively. As the ratio of building height to street width increases (as occurs from low
to high intensity), the urban albedo increase attained from raising pavement albedo is diminished.
The upper bound for the impact of pavement albedo modification on urban albedo is set by
assuming there is no canyon. Combining equations (10) and (11), we attain,
Δρ
Δρ
=
𝛫 𝛫 .
Δρ
Δρ

(12)

which expresses the proportionality of grid cell albedo to pavement albedo.
45

As will be discussed in the Discussion section 3.4.1, comparing the slopes for COOL_LOW –
CTRL and COOL_HIGH – CTRL in Figure 3-5 indicates that surface air temperature change
versus pavement albedo change is approximately linear in each modeled city. Li et al. (2014)
similarly found that the magnitude of surface air temperature change is nearly linearly related to
the magnitude of city-wide roof albedo change. Thus, to generalize the sensitivity of surface air
temperature to pavement albedo for any urban canyon configuration of choice, we can compare K
values assumed in our WRF modeling (i.e. K1) to newly derived K values using various
morphologies and/or canopy radiative transfer assumptions (i.e. K2 in equation 11). Scaled
estimates of surface air temperature can then be attained for newly assumed canyon morphologies
by scaling city-mean surface air temperature differences derived using WRF by the ratio K2/K1.
Note that to attain city specific results, city-mean temperature-albedo sensitivities and K1 values
should be used. (Values of K1 can differ by city because of different relative amounts of low,
medium, and high intensity urban land cover, and incorporation of NUDAPT data where
available.) Note that we subsequently refer to K2/K1 as “scaling factors”.
In an attempt to provide more realistic estimates of surface air temperature changes from cool
pavement adoption, we explore in a related study (Rosado et al. 2017) the relationship of changes
in urban albedo to changes in pavement albedo (K) using (a) improved data-derived estimates of
urban canyon morphologies that also account for building setbacks, and (b) modeling of both direct
and diffuse components of sunlight in the urban canyon. Shadows cast on pavements by buildings
are computed as a function of sun angle and for both north-south and east-west facing canopies.
Values reported here represent averages of these two canopy orientations. City specific urban
canyon morphologies were estimated from building heights, road widths, and setback distances.
Building heights for each city were estimated using data from County Assessor’s offices in
46

California. These data are acquired by each county for all taxable properties and include building
location, floor area, property type, and age. Road widths were estimated using street design
standards for Sacramento (DOT Sacramento 2009), which we found to be representative of other
major cities in California including Los Angeles (Ryan Snyder Associates 2011) and San Jose
(DOT San Jose 2010). Setback distances were estimated by assuming they are comprised of front
yards, road verge (also known as planting strip, sidewalk buffer, or utility strip), and sidewalks.
The widths of the front yards were obtained from the Sacramento County Zoning Code (ZCSC
2015), while the widths of the sidewalk and the road verge were obtained from the Street Design
Standards for the City of Sacramento (DOT Sacramento 2009).
The data-derived urban morphologies were then used in the UCAM that we developed to derive
modified urban to pavement albedo relationships (i.e. K2). The ratio K2/K1 (i.e. scaling factors)
was then used as a criterion to scale WRF-simulated temperature reductions for each city as
previously described. The UCAM calculates the direct and diffuse solar radiation that enters
through the top of the canyon and the solar radiation that is reflected out of the canyon. It is suited
for exploring how the canyon albedo varies with the albedo or geometry of walls, setbacks, and/or
roads.
As an example of a scaling factor calculation, consider that WRF simulations were carried out for
a hypothetical city comprised of medium intensity urban (as defined by NLCD) to quantify
changes in surface air temperature caused by increases in pavement albedo. The ratio of the change
in urban albedo to pavement albedo change computed using the WRF single-layer UCM is K1 =
0.16. (This is equivalent to the slope of the red line in Figure 3-3.) Next, assume that we obtain
realistic canyon morphology data for the hypothetical city as described in the preceding two
paragraphs. Using these data with the UCAM, we find that the relationship between changes in
47

urban albedo and pavement albedo is K2 = 0.32. The ratio K2/K1 would therefore be 2, which
would serve as a factor to scale surface air temperature changes per pavement albedo increase
derived using the WRF simulations. We note here that scaling factors are relatively large in many
cases since the canyon width-to-height ratio assumed in the WRF simulations were smaller than
suggested by the data.
All results sections, discussion sections 3.4.1 and 3.4.2, and all figures in this paper report WRF
model results without including scaling. The discussion section 3.4.3, and Table 3-1 and
48

Table 3-2, report scaling factors per city as well as scaled and unscaled temperature changes as a
result of cool pavement adoption.
3.3 Results
3.3.1 Spatially resolved climate response to cool pavement adoption
The effect of increasing pavement albedo to 0.50 from 0.10 on surface air temperature is shown in
Figure 3-5. Temperature changes are shown for the hottest time of day (14:00 LST), and in the
evening (20:00 LST) when the magnitude of the air temperature heat island (urban temperature
minus surrounding rural temperature) is often observed to be at a maximum (Oke 1982, Vahmani
et al. 2016). Only differences that are significant at 99.5% confidence interval are shown.
Widespread deployment of cool pavements reduces surface air temperatures in the urban parts of
California including the Los Angeles Basin, San Diego, San Francisco Bay Area, and cities in the
Sacramento and San Joaquin Valleys. Temperature reductions of up to 0.32 C and 0.25 C are
simulated at 14:00 LST for summer and winter, respectively. (Summer and winter are defined as
June-July-August and December-January-February, respectively.) Corresponding temperature
reductions at 20:00 are 0.62 C and 0.32 C. The absolute values of temperature reductions are
smaller in winter versus summer, a consequence of the fact that insolation at the surface is
decreased in winter, and therefore changes in the albedo play a smaller role on temperatures. In
the Los Angeles basin, temperature reductions are stronger in the inland versus coastal regions.
This occurs because the effects of cool pavements accumulate as the dominant winds flow from
west to east.
Raising pavement albedo increases net (upward – downward) all-wave (short-wave + long-wave)
radiance at the top of the model in the urban parts of California by up to about 45 W m
-2
in summer
and 25 W m
-2
in winter (Figure 3-6). These increases in radiation are larger in summer than winter
49

since solar irradiance at the surface is increased during summer. While boosting the surface albedo
increases the reflected shortwave radiation at the surface, corresponding increases in total upward
radiation at the top of model can be muted for two reasons: (1) short-wave radiation that is reflected
from the surface can be scattered and absorbed by gases, aerosols, and clouds in the atmosphere
prior to reaching the top of model (Zhang et al. 2016); and (2) surface temperature reductions
caused by increasing pavement albedo lead to decreases in upward long-wave radiation.
3.3.2 Diurnal cycles
Diurnal cycles of seasonal mean temperature differences for (COOL_HIGH – CTRL) are shown
for two example cities (Los Angeles and Sacramento) in Figure 3-8a and b. For all seasons, the
largest temperature reductions are simulated in the late morning between about 9:00 and 11:00
LST, and in the early evening between about 17:00 and 19:00 LST. While one might expect the
largest temperature decreases to occur at noon when solar irradiance peaks, or around 14:00 LST
when absolute temperatures are highest, we find that the magnitudes of temperature decreases are
closely coupled to the height of the planetary boundary layer (PBL) (Figure 3-8c,d). This occurs
because convective heat transfer from the surface to atmosphere can have a more pronounced
impact on the temperature of the boundary layer when PBL height is decreased. Thus, the largest
temperature reductions occur in the (a) late morning when solar irradiance is relatively strong and
PBL height is relatively low (compared to diurnal maximum values that occur in the early
afternoon), and (b) early evening when accumulated solar heat gain reaches its daily maximum
and PBL heights are decreasing. The mentioned evening peak is qualitatively consistent with past
studies showing that the magnitude of the air temperature heat island effect reaches a maximum
after sunset (e.g. Oke 1982, Vahmani et al. 2016).
50

Diurnal cycles of changes in surface energy fluxes for (COOL_HIGH – CTRL) are shown for an
example city (Los Angeles) in Figure 3-9. Widespread adoption of cool pavements reduces net
(downward – upward) short-wave radiation due to increases in the reflected short-wave radiative
flux. The maximum reduction in net short-wave radiation of about 42 W m
-2
occurs at noon when
solar irradiance is highest. Sensible heat fluxes are reduced throughout the day with maximum
decreases of about 28 W m
-2
just after noon. Changes to latent heat fluxes are small relative to
other flux changes, as expected. Surface temperature reductions throughout the day are associated
with decreases in net (upward – downward) long-wave radiation. The daytime ground heat flux
(downward positive) is reduced during the day. The ground heat flux flows downward during the
day since surface temperatures are higher than subsurface temperatures; cool pavements reduce
surface temperatures more than subsurface temperatures during daylight hours, and thus reduce
the daytime downward heat flux. However, at night, when the surface is generally cooler than the
subsurface causing ground heat fluxes to be upward, this positive downward heat flux increases
(i.e. the upward ground heat flux decreases). This likely occurs because the surface temperature
differential for (COOL_HIGH – CTRL) is lower than the corresponding temperature differential
for the subsurface at night. The underlying mechanism is that subsurface temperatures are
dependent on ground heat accumulation throughout the entire day, whereas surface temperatures
are expected to be less dependent on albedo after the sun goes down. A similar diurnal cycle in
ground heat flux was reported for widespread deployment of cool roofs by Li et al. (2014).
3.4 Discussions
3.4.1 Sensitivity of air temperature change to urban fraction and linearity of simulations
City-mean temperature changes for (COOL_HIGH – CTRL) and (COOL_LOW – CTRL) are
plotted versus the city-mean urban fraction in Figure 3-4. In general, deployment of cool
51

pavements leads to larger air temperature reductions in cities with higher mean urban fractions, as
expected. Figure 3 also shows least-squares linear regressions of surface air temperature change
to urban fraction for (COOL_HIGH – CTRL) and (COOL_LOW – CTRL). These regressions
indicate that for (COOL_LOW – CTRL), the temperature reduction per 0.1 increase in urban
fraction is 0.0055 and 0.0043 C for 14:00 LST and daily means, respectively. Corresponding
temperature reductions for (COOL_HIGH – CTRL) are 0.022 C and 0.024. The fact that the
temperature reductions for (COOL_HIGH – CTRL) are about 4 times higher than those for
(COOL_LOW – CTRL) shows that the impacts of pavement albedo increases on air temperatures
are nearly linear, since the corresponding pavement albedo changes are 0.10 and 0.40. Scatter
about the trend lines represents variation in the sensitivity of air temperature change to albedo
change, and is caused by widely varying baseline climates for cities around California. For
example, sensitivity of air temperature to albedo is expected to be higher for cities with lower wind
speeds, less cloud cover, and larger boundary layer heights. Scatter is also caused by variation in
the size of each city. This occurs because the effects of cool pavements accumulate as winds flow
over the city, an effect that can amplify the sensitivity of air temperature to albedo change for
larger cities [Oke 1973].
3.4.2 Sensitivity of air temperature to grid cell albedo and comparison to other modeling
studies
Results reported here are compared to other relevant studies. Millstein and Menon [2011]
investigated the temperature reductions attainable from widespread deployment of cool roofs and
pavements in cities around the United States. They used WRF version 3.2.1 to simulate the entire
country at 25 km resolution for 12 years. An urban canopy model was not implemented and thus
each urban grid cell was represented as a 2D surface without including the effects of the urban
52

canopy on surface-atmosphere interactions. In Millstein and Menon, the urban albedo (ρ u) was
increased by 0.19, achieved by increasing roof albedo by 0.25 and pavement albedo by 0.15. The
grid-cell albedo change was calculated based on the increases in pavement and roof albedo, and
their fraction of grid-cell. The resulting grid-cell value was implemented in model afterwards.
Given the different treatments of urban grid cells in our study versus Millstein and Menon means
that we cannot directly compare the sensitivity of temperature difference to roof/pavement albedo
difference. Rather, we compare temperature difference to grid cell albedo (ρ g) difference. Figure
3-9 shows air temperature difference versus change in grid cell albedo for each city investigated
in our study and each city reported in Millstein and Menon (see their Table 1). Using results from
our investigation, linearly regressing temperature versus albedo change leads to a slope of 3.2,
implying surface air temperature reductions of 0.32 C per 0.10 increase in grid cell albedo. An
equivalent analysis of the results reported in Millstein and Menon suggests very similar
temperature reductions of 0.34 C per 0.10 increase in albedo. These are both consistent with that
reported in Santamouris (2012), who reviewed a number of past studies [Savio et al. (2006),
Synnefa et al. (2008), Sailor (1995), Rosenfeld et al. (1995), Rosenfeld et al. (1998), Millstein and
Menon (2011), Sailor et al. (2002), Zhou and Shepherd (2010), Taha (2008a), Lynn et al. (2009),
Taha (2008c)], and found that average ambient temperatures are expected to be reduced by 0.30
C per 0.10 increase in albedo (i.e. what we refer to as grid cell albedo). Note that Santamouris
(2012) does not include past studies that single out the urban temperature impacts of cool
pavements since no such studies existed.
Taha (2013b) is the only other study to our knowledge that has investigated the urban temperature
impacts of cool pavements. Taha simulated the impacts of increasing the albedo of driveways,
sidewalks, and parking lots by 0.10 and 0.20. Corresponding peak (i.e. both spatially and
53

temporally) temperature reductions in Los Angeles were about 0.25 C and 0.50 C, respectively.
These results are not directly comparable to our study since Taha reports peak temperature
reductions while we report spatial and temporal averages.
Another recent study (Taleghani et al. 2016) compared the impacts of adopting heat mitigation
strategies (i.e. cool roofs, cool pavements, green roofs, and street trees) on neighborhood scale
temperatures and pedestrian thermal comfort. This study implemented a micrometeorological
model with a spatial resolution of 3 m, orders of magnitude higher resolution than that of the
regional climate model used in our research. They found that increasing pavement albedo by 0.3
decreased surface air temperatures by up to about 2° C, which is larger than the temperature
reductions reported in our research. There are important differences in methodology between our
study and that of Taleghani et al. For example, Taleghani et al. simulated one extreme heat day,
while our longer simulations are “climatological” and thus represent a variety of meteorological
regimes. Also, due to model limitations on domain size, the high resolution simulations carried out
in Taleghani et al. could only be performed for a neighborhood, and not an entire city or state.
Thus, results reported in Taleghani et al. may be specific to the particular neighborhood they
investigated. The higher spatial resolution of the micrometeorological model used in Taleghani et
al. allowed for more detailed characterization of land cover compared to the current research. The
micrometeorological model also better resolves air flows between buildings than is capable by the
regional climate model used in our research, which parameterizes the urban canopy using only one
layer in the vertical. Thus, surface air temperatures reported in Taleghani et al. were obtained
directly from an atmospheric layer of the micrometeorological model, while those reported here
are diagnostic and represent air temperatures near the top of the urban canopy (Li and Bou-Zeid
2014). We suggest that the temperature reduction from cool pavements may be dependent on the
54

spatial scale of the model used. Note also that Taleghani et al. explored the impacts of heat
mitigation strategies on pedestrian thermal comfort, further discussed in Section 4.5.

3.4.3 Influence of assumed urban morphology on climate response of cool pavements
As described in the Methods section 3.2.3, we developed city-specific scaling factors using an
offline urban canyon albedo model (see our companion paper, Rosado et al. 2017) to provide
improved estimates of the impacts of pavement albedo increases on surface air temperatures. The
scaling factors were developed to incorporate improved characterization of urban canyon
morphologies based on real-world data, including building setbacks, and using improved
shortwave radiative transfer calculations in the urban canopy. These factors were developed
using the UCAM (see section 3.2.3) for representative cities in California Building Climate
Zones 3, 4, 7-10, and 12-15 (California Energy Commission, 2016). Representative cities per
climate zone are listed in
55

Table 3-2. For each city, 10 canyon geometries that each contain different building prototypes were
modeled including: single-family home, apartment building, large hotel, large office, medium
office, primary school, fast-food restaurant, retail stand-alone, retail strip mall, and sit-down
restaurant. The resulting scaling factors for each city, canyon geometry, and season, are shown in
the Table 3-1. The mean scaling factor for each building climate zone was then determined by
taking the weighted mean of the 10 canyon geometries per representative city, where the fraction
of each building type within the city obtained from the County Assessors Office data (see section
3.2.3) is used as the weighting factor. Annual average scaling factors range from 2.64 to 2.81.
Scaling factors are relatively high due to the fact that the urban morphologies used in the WRF
simulations (see Methods section 2.1) have substantially higher building height to canyon width
ratios than suggested by the urban morphology data (see section 3.1.3and Rosado et al. 2017).
Values vary slightly by season, as well as by the latitude and canyon morphology of the
representative city for each climate zone.
City-mean temperature reductions (summer, winter, and annual averages) attained by increasing
pavement albedo by 0.4 are shown in
56

Table 3-2. Spatial means are weighted by grid cell values of urban fraction in order to highlight
temperature changes over the urbanized portions of cities. (Some city boundaries include
significant undeveloped land areas that we did not want to include in spatial means.) Values that
are obtained directly from WRF simulations (unscaled) are shown, as are scaled temperature
differences computed using scaling factors from Table 3-1. (Scaling factors determined for each
representative city are applied to all cities per climate zone.) Scaled annual average surface air
temperature reductions at 14:00 LST range from 0.18 °C (Palm Springs) to 0.86 °C (San Jose).
Scaled summertime temperature differences are larger for most cities than wintertime differences.
Seasonal mean temperature reductions range from 0.19 to 0.87 and 0.16 to 0.64 during summer
and winter, respectively.
3.4.4 Comparison to experimental studies
Though we already presented an evaluation of simulated meteorology from our control case by
comparing to observations in Section 2.3.1, it is also desired to compare simulated versus observed
sensitivity of air temperature to pavement albedo. Numerous past studies have performed
experiments to investigate the effect of adopting cool pavements on surface temperatures (see
Yang et al. 2015 for a summary). Increasing pavement albedo has been found to reduce pavement
surface temperatures by about 12–15 °C (Li 2012, Synnefa et al. 2011). However, very few
previous studies have performed experiments to assess the effect of cool pavement adoption on air
temperature (Yang et al. 2015). The few studies that exist compared air temperatures above small
patches of standard and cool pavements (see next paragraph for details). We note that there is an
inherent spatial scale mismatch between simulations of city-wide adoption of cool pavements
versus experiments that assess air temperature impacts of small patches of high albedo pavements.
Nonetheless, we summarize previous experimental studies below.
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Li (2012) (also see Li et al. 2012 and Li et al. 2013) investigated surface and air temperatures
above 4 by 4 m sections of cement and asphalt concrete (albedo difference 0.2). They found that
air temperatures above cement concrete were decreased relative to those above asphalt concrete at
the lowest measured height of 5 cm. But air temperature differences above cement versus asphalt
concrete diminished up to the maximum measured height of 100 cm. Romeo and Zinzi (2013)
found that installing a cool roof on a school building had little impact on measured air temperature
above the roof. Yang et al. (2014) reported that measured air temperature at 1.5 m above ~4x4 m
asphalt and cement concrete test sections showed indistinguishable differences, stating that
turbulent mixing near the surface weakens the effect of surface albedo on air temperature.
While it appears that observations indicate that adopting cool pavements have negligible effects
on 2 m air temperature, we suggest that this is likely because of the small pavement test sections
investigated in these studies. Millstein and Levinson (in press) performed theoretical calculations
to estimate air temperatures at 2 m height for air traveling over a flat “plate” (i.e. analogous to
ground). When assuming that the plate was 10 K warmer than the air upwind of the plate, they
found the temperature rise at 2 m height to be 0.0 K after traveling over the plate a horizontal
distance of 10 m, 0.025K after 100 m, and 1.3 K at 1000 m. These results suggest that cool
pavement installations would need to be roughly 1 km in scale to observe appreciable 2 m air
temperature differences. To our knowledge there has never been an experimental study
investigating impacts of cool pavements at this spatial scale. Thus, there are no existing
experimental studies that are suitable for directly comparing to our mesoscale simulations.
3.4.5 Impacts on pedestrian thermal comfort
Despite the fact that widespread adoption of cool pavements could lower air temperatures,
increases in reflected solar radiation from high albedo pavements could be absorbed by
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pedestrians, contributing to reductions in thermal comfort. We do not quantify thermal comfort in
our study because of difficulties in assessing effective canyon air temperatures using mesoscale
meteorological models. Surface air temperatures as diagnosed by WRF coupled with the single
layer UCM are roughly representative of roof level air temperatures (Li and Bou-Zeid 2014), and
therefore would be inappropriate for using in calculations of pedestrian comfort. However, we
discuss the potential impacts of cool pavements on thermal comfort in the context of past research,
acknowledging that additional research on this topic should be performed.
Taleghani et al. (2016) quantified the effects of cool pavement adoption on pedestrian thermal
comfort using micrometeorological simulations. Changes in air temperatures, wind speed, relative
humidity, and mean radiant temperature were used to calculate cool pavement induced differences
in physiological equivalent temperature (PET), a widely used metric for estimating thermal
comfort. They found that increasing pavement albedo by 0.30 could reduce thermal comfort of
pedestrians (pedestrian albedo 0.30) that are walking on cool pavements during the day. This
occurred because the negative effects on PET of increasing mean radiant temperature outweighed
the positive effects of decreasing air temperature. However, daytime thermal comfort was
improved for pedestrians that were on average 5 m from cool pavements since the air temperature
reduction effects outweighed the mean radiant temperature effects on PET. In addition, thermal
comfort was improved in all studied locations after sunset since air temperature reductions from
cool pavements prevailed, while the radiation effects went away.
Another study (Lynn et al. 2009) that investigated heat mitigation strategies at the urban scale
using a mesoscale meteorological model suggested that increasing the albedo of impervious
surfaces by 0.35 could lead to reductions in thermal comfort for pedestrians (pedestrian albedo =
0.20) that are on these surfaces.
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Erell et al. (2013) modeled thermal comfort for pedestrians on a hot and dry summer day in Israel
and concluded that the reduction in longwave radiation from high-albedo pavements was
outweighed by an increase in reflected shortwave radiation. Despite decreased outdoor air
temperatures, they found a net increase in pedestrian radiant load (assuming pedestrian albedo =
0.35), which slightly increased thermal stress according to another metric, the Index of Thermal
Comfort (ITS).
Yet another study (Li 2012) measured this effect differently, noting that many comfort indices
(including ITS) were designed to gauge comfort in a uniform indoor environment and are therefore
not well-suited to outdoor conditions that vary in space and time. Accordingly, Li estimated
thermal comfort using PET as in Taleghani et al. Li found that higher-albedo pavement surfaces
raised the mean radiant temperature felt by pedestrians (pedestrian albedo = 0.30) and therefore
increased thermal stress.
We note that the impacts of cool pavements on thermal comfort are sensitive to the assumed albedo
of the pedestrian. Levinson (1997) pointed out that an increase in ground albedo could theoretically
decrease the temperature felt by a near-ground object (e.g., a pedestrian), as long as the albedo of
that object exceeds a critical value. This critical object albedo value in turn depends on wind speed
(which determines convective heat loss from surfaces), object geometry, and the height of the
atmospheric thermal boundary layer. Levinson determined that this critical value for humans
ranges from 0.15 to 0.37, depending on values of the aforementioned parameters.
In summary, adoption of cool pavements could in some situations cause decreases in thermal
comfort. This is most likely to occur on hot days, when solar elevation is large, for pedestrians that
are on or near cool pavements. In other situations, cool pavements could lead to improved thermal
comfort. This is most likely to occur after sunset, or during the day when sufficiently far from
60

pavements. Future research is needed to systemically investigate changes in human thermal
comfort due to cool pavements under a variety of real world situations.
3.5 Summary
The focus of this study is the effects of land cover changes on local climate in urban areas. In this
study, we investigated the effect of widely deployed solar reflective cool pavements on the air
temperature in urban areas in California. We use WRF model with the set-up explained in Chapter
2 as our control scenario with pavement albedo of 0.1, and used the same modeling framework
with perturbed pavement albedo values as our perturbation scenario. In order to understand the
cool pavement effect in different ranges of albedo, we created two perturbated scenarios: the first
scenario with an increase of 0.1 and another with an increase of 0.4. The simulations were done in
ensembles of three, and the results of the simulations from different scenarios were compared to
the ensembles to derive effect of cool pavements that are significantly higher than the noise of the
model. The results were only analyzed for the areas with 99.5% or higher confidence level. The
effect of canopies with realistic sizes that are representing the geometrical features of typical
canopies in each city were added to the results of the model, a feature that the model lacks and is
essential in capturing the effect of albedo increase.
We find that a widespread pavement albedo increase of 0.4 leads to reductions in annual average
surface air temperatures in selected California cities ranging from 0.18 to 0.86 °C. Highest daily
reductions occur around late morning and evening, which can be attributed to the low PBLH in
those hours, and thus higher sensitivities to amount of absorbed or reflected energy. The
temperature reductions at our two scenarios with different albedo perturbation seem to be linear,
which allows us to interpolate the effects at different scales. The derived sensitivities are consistent
61

with the previous modeling works on the similar domains, with a sensitivity of 0.32 air temperature
reduction per 0.1 increase in pavement albedo.
3.6 Figures and Tables

Figure 3-1. Maps of (a) the three urban classifications extracted from the NLCD dataset at 30 m spatial
resolution, and (b) the resulting urban fraction for each model grid cell.

62

Figure 3-2. Maps of (a) grid cell albedo for the control scenario (CTRL) at the start of the simulation (Sep
2001), (b) grid cell albedo increases after increasing pavement albedo by 0.1 (COOL_LOW – CTRL), and
(c) grid cell albedo increases after increasing pavement albedo by 0.4 (COOL_HIGH – CTRL).

63

Figure 3-3. Relationship of changes in urban albedo to pavement albedo for different assumed urban canyon
morphologies. The assumed building height to pavement width ratio is 0.60, 0.80, and 1.0 for low density,
medium density, and high intensity NLCD urban types, respectively. The dashed line represents the case
of no urban canopy.

64

Figure 3-4. Average surface air temperature reductions for different California cities versus average urban
fraction. Each point represents the mean value of a city. Temperature reductions are shown for both
COOL_HIGH – CTRL and COOL_LOW – CTRL. Green indicates 24 hour averages while black shows
values for 14:00 LST. Best fit linear regressions are shown. Unlike other figures, city means include all
grid cells within the city boundary and are not weighted by urban fraction

65

Figure 3-5. Temperature changes from cool pavement adoption (COOL_HIGH – CTRL) for (a) summer
average at 14:00 LST; (b) summer average at 20:00 LST; (c) winter average at 14:00 LST; and (d) winter
average at 20:00 LST. Only differences that are significant at 99.5% confidence interval are shown.
66

Figure 3-6. Changes in net (upward – downward) all-wave (short-wave + long-wave) radiance at the top of
model (10000 Pa) for COOL_HIGH – CTRL for (a) summer average at 14:00 LST and (b) winter average
at 14:00 LST.. Only differences that are significant at 99.5% confidence interval are shown.

67

Figure 3-7. Seasonal average changes in diurnal profiles for COOL_HIGH – CTRL of (a) surface air
temperature in Los Angeles, (b) same but for Sacramento, (c) planetary boundary layer height (PBL height)
for Los Angeles, and (d) same but for Sacramento. Spatial averages for each city are weighted by grid cell
values of urban fraction. Fall corresponds to September-October-November, Winter is December-January-
February, Spring is March-April-May, and Summer is June-July-August.

68

Figure 3-8. Diurnal cycle of changes in surface energy fluxes (COOL_HIGH - CTRL) averaged spatially
over the city of Los Angeles and temporally over summer. Net shortwave (SW) radiation and ground heat
flux (GRDFLX) is positive downward, while net longwave radiation (LW), sensible heat (SH), and latent
heat (LH) fluxes are positive upward. Spatial averages are weighted by grid cell values of urban fraction.

69

Figure 3-9. Surface air temperature differences versus changes in grid cell albedo for our study, and
Millstein and Menon (2011), which focused on cool roofs. Each point represents a different city. Best fit
linear regressions using least squares are also shown, suggesting surface air temperatures are reduced by
about 0.3 C per 0.1 increase in grid cell albedo. To make our study more comparable to Millstein and
Menon (2011), city means in this figure are not weighted by urban fraction as in our other figures (besides
Figure 12).

70

Table 3-1. Scaling factors for different building climate zones in California by season.
building climate
zone
Fall Winter Spring Summer Annual
3 2.81 2.56 2.79 2.79 2.74
4 2.82 2.55 2.80 2.80 2.74
7 2.81 2.62 2.80 2.79 2.76
8 2.82 2.62 2.79 2.80 2.76
9 2.82 2.61 2.79 2.79 2.75
10 2.85 2.65 2.82 2.82 2.79
12 2.84 2.65 2.81 2.81 2.78
13 2.85 2.69 2.82 2.82 2.79
14 2.87 2.68 2.84 2.83 2.81
15 2.74 2.40 2.72 2.71 2.64

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Table 3-2. Unscaled and scaled annual difference in surface air temperature from increasing pavement albedo by 0.4 (COOL_HIGH – CTRL), along
with the annual mean scaling factors and building climate zone for each city.
City
Building
climate
zone
Summer mean
temperature
difference at
14:00 LST
Summer mean
temperature
difference at
14:00 LST (K),
scaled for
realistic
canyon
Winter mean
temperature
difference at
14:00 LST
Winter mean
temperature
difference at 14:00
LST (K), scaled for
realistic canyon
Annual mean
temperature
difference at
14:00 LST
Annual mean
temperature difference
at 14:00 LST (K), scaled
for realistic canyon
LA 8 -0.20 -0.56 -0.18 -0.48 -0.19 -0.52
Sandiego 7 -0.13 -0.38 -0.11 -0.30 -0.12 -0.34
Irvine 8 -0.11 -0.31 -0.16 -0.42 -0.12 -0.33
Anaheim 8 -0.24 -0.68 -0.22 -0.58 -0.22 -0.61
Santa Ana 8 -0.19 -0.54 -0.19 -0.51 -0.18 -0.50
Garden
Grove
8 -0.22 -0.62 -0.21 -0.54 -0.20 -0.55
Riverside 10 -0.23 -0.64 -0.19 -0.50 -0.20 -0.55
Sacramento 12 -0.15 -0.43 -0.10 -0.27 -0.12 -0.32
Bakersfield 13 -0.09 -0.24 -0.12 -0.31 -0.08 -0.23
Fresno 13 -0.11 -0.30 -0.13 -0.36 -0.12 -0.34
Lancaster 14 -0.09 -0.26 -0.06 -0.17 -0.07 -0.19
Palm Spring 15 -0.07 -0.19 -0.07 -0.16 -0.06 -0.16
San Jose 4 -0.31 -0.87 -0.16 -0.41 -0.22 -0.60
Burbank 9 -0.25 -0.70 -0.21 -0.55 -0.23 -0.62
Glendale 9 -0.23 -0.65 -0.20 -0.53 -0.22 -0.60
Pasadena 9 -0.27 -0.75 -0.24 -0.64 -0.25 -0.70
Ontario 10 -0.24 -0.68 -0.19 -0.51 -0.21 -0.59
Rancho 10 -0.29 -0.81 -0.17 -0.45 -0.23 -0.65
72

Fontana 10 -0.25 -0.71 -0.17 -0.45 -0.21 -0.58
Rialto 10 -0.27 -0.77 -0.17 -0.44 -0.22 -0.63
San
Bernandino
10 -0.25 -0.72 -0.18 -0.49 -0.22 -0.61
Bloomington 10 -0.24 -0.67 -0.19 -0.51 -0.21 -0.59

a
Spatial means for each city are weighted by grid-cell values for urban fraction

73

4 Observational evidence of neighborhood scale reductions in air
temperature associated with increases in roof albedo

74

4.1 Introduction
This study is focused on associating ground observation of air temperature and land use and land
cover properties of neighborhoods. We gathered ground observation of near surface air
temperature from 76 weather stations from privately owned network of stations across the county
of Los Angeles. The study area is divided into two regions of Central Los Angeles (CLA) and San
Fernando Valley (SFV). This region selection allows us to avoid the effect of sea breeze from the
west coast and to investigate the isolated local climate of SFV. Los Angeles region is selected
parallel to the coast line, as the sea breeze effect causes a gradual increase in the temperature as
we go further away from the coast line (Taha, 2017). SFV region does not experience similar sea
breeze like the rest of the county, as the wind direction in that region is from north to south and
SFV is the downwind of other areas. The temperature measurements are then associated with the
properties of surrounding neighborhood of the weather station.
This study is part of California state’s effort to understand the complex heat island effect in the
metropolitan area of Los Angeles and reduce the extreme events such as heat waves, using the
most effective method of land cover change.
4.2 Methodology
4.2.1 Areas of analysis
The Los Angeles basin contains numerous microclimates leading to summer temperatures that
range from moderate (at the coast) to hot (at the interior of the basin). The onshore sea breeze and
therefore distance from the coast plays an important role in determining temperature variations
within the basin. Land use and land cover properties also vary widely across the basin, with some
regions consisting of primarily industrial/commercial land use, and others that are primarily
75

residential. In this study, we have chosen two “regions” of interest within the Los Angeles Basin.
Each region was chosen to fulfil two requirements: (1) it should be sufficiently small such that
distance from the coast does not dominate temperature variations; and (2) there should be sufficient
variation in land cover properties of interest (e.g., roof albedo) to enable discerning effects of land
cover on measured air temperatures. The first region encompasses an area of roughly 500 km
2
and
includes downtown Los Angeles; we refer to this region as central Los Angeles (CLA). The second
region encompasses roughly 160 km
2
and is located within the San Fernando Valley (SFV). These
two regions have distinctly different summertime baseline climates (Table 4-1). The monthly
averaged daily minimum, maximum, and mean temperature is 19.4, 30.5, and 24.3 C for SFV and
18.9, 27.7, and 22.5 C for central Los Angeles. Central Los Angeles typically experiences
afternoon sea breezes, while SFV is largely decoupled from the influence of coastal air because of
the Santa Monica mountains.
4.2.1.1 Meteorological data
The meteorological data comes from a network of personal weather stations from Weather
Underground (WunderGround API). We acquired all available meteorological parameters per
station, including near-surface outside air temperature (referred to as “temperature” from now on),
solar irradiance, air pressure, precipitation, relative humidity, wind speed, and wind direction for
July 2015. This month was chosen because it was the latest data available at the time of acquisition.
(June was intentionally avoided since Los Angeles generally experiences numerous cloudy days
during that month.) Data from 76 stations were gathered within the two regions, but not all stations
measured all meteorological parameters. Note that while we acquired all available parameters, we
focused our analysis on temperature and solar irradiance. Figure 4-1 shows the location of each
weather station grouped by region.
76

We developed a three-step data screening procedure to ensure that the meteorological data was of
high quality. In the first step, we removed values that were outside the range of observed minimum
and maximum temperatures from the historical record reported from NOAA weather stations
(NOAA) across the LA Basin; corresponding minimum and maximum temperatures were –17 °C
and 49 °C, determined using data from five to 130 years before current. Since the main focus of
this study is to investigate urban temperature variability, in the second step we removed stations
that were located in non-typical urban settings such as neighborhoods near golf courses or water
reservoirs, as the underlying effects on the local meteorology of these neighborhoods are different
than neighborhoods with more typical urban cover. In the third step, stations with unreasonable
diurnal cycles were removed. Examples of unreasonable diurnal cycles include having daily
maximum temperatures at night (i.e., indicating a problem with the weather station time stamp),
or diurnal temperature variations that were near zero (i.e., indicating a problem with the
temperature sensor).
4.2.1.2 Description and data sources for land use land cover properties
The land use land cover properties (referred to as “LULC” from this point on) are computed using
data from multiple sources for each neighborhood.
Roof fraction (froof) represents the ratio of building roof area (i.e., assumed to be equivalent to
building footprint area) to neighborhood area. Roof fraction is computed using the Los Angeles
Region Imagery Acquisition Consortium (LARIAC) dataset shapefiles for building footprints
(LARIAC 2008).
Tree fraction (ftree) is computed using a tree dataset from LARIAC with spatial resolution of 4
feet (1.2 m). This dataset is binary, indicating whether or not each pixel has tree cover. Analogous
to roof fraction, the tree fraction represents the ratio of tree covered area to neighborhood area.
77

Pavement fraction (fpavement) represents the area fraction of pavements per neighborhood, with
pavement area contributions from parking lots and paved roadways. Parking lot area is computed
using parking lot boundaries given by LARIAC dataset shapefiles. Paved roadway area is derived
using a street centerline dataset (CAMS 2011). The total roadway length is computed by summing
roadway length per neighborhood, and roadway area is then calculated by multiplying by an
assumed roadway width of 12.8 m. Note that this roadway width represents the average street plus
sidewalk width for Los Angeles, calculated using the weighted mean roadway width per building
type (CARB 2017), where the weighting factor is determined using the relative quantify of
different building types in LA.
Reflected solar power from roofs (Proof) represents the average daily solar power (W) reflected
from roofs within the neighborhood. This is computed as,

𝑃 = 𝐼 ∗ 𝛼 ∗ 𝑓 ∗ 𝐴
(1)

𝑤 here I is the average daily incoming solar power (W m
-2
), roof is the weighted average roof
albedo in the neighborhood, and A is the neighborhood area ( *500m
2
= 7.85×10
5
m
2
) (see the
next section for more information on the neighborhood areas). The average daily solar power of
the day includes all 24 hours, not just sunlit hours. The area-weighted mean roof albedo ( roof) is
determined using a dataset for seven California cities that reports building-specific roof albedos
using remote sensing data (Ban-Weiss et al. 2015); the mean roof albedo is computed for each
neighborhood using the roof’s area as the weighting factor. Overall, the metric Proof is used to
account for the influence of cool roofs, considering (a) the mean roof albedo of the neighborhood,
(b) the spatial coverage of roofs in the neighborhood, and (c) the daily solar irradiance. This avoids
78

biases that could occur when, for example, the mean roof albedo of a neighborhood may be high,
but spatial coverage of roofs is low.
Reflected solar power from neighborhood (Pneighborhood) represents the average daily solar power
(units [=] W) reflected from the entire neighborhood. This parameter is estimated as

𝑃 = 𝐼 ∗ 𝛼 ∗ 𝐴
( 2)

where neighborhood is the average albedo of the neighborhood. The average neighborhood albedo is
estimated using Eq. 3, assuming that the neighborhood is comprised of roofs, pavements, and trees,

𝛼 =
𝛼 ∗ 𝑓 + 𝛼 ∗ 𝑓 + 𝛼 ∗ 𝑓 𝑓 + 𝑓 + 𝑓
( 3)

Since spatial datasets describing pavement and tree albedos do not exist, we assume values of 0.10
and 0.15, respectively. Due to potential inaccuracies in the GIS datasets, and because
neighborhoods can consist of surface types other than roofs, pavements, and trees, 𝑓 + 𝑓 +
𝑓 generally does not equal 100%. Thus, the denominator of Eq. 3 ensures that
neighborhood-to-neighborhood variability in 𝑓 + 𝑓 + 𝑓 does not lead to variability
in neighborhood albedo. While this calculation provides a relatively crude estimate of reflected
solar power from the neighborhood, it is sufficient for the purposes of our study since this metric
is used only for supporting analysis.
Reflected solar power from non-roof surfaces (Pnon-roof) represents the average daily solar power
(units [=] W) reflected from surfaces other than roofs in the neighborhood. This parameter is
computed as the difference between Pneighborhood and Proof .
79

4.2.2 Defining aggregation areas
In this study we aim to relate observed temperatures to LULC properties. We aggregate LULC
properties (see previous section) within a 500 m radius of each weather station (Figure 4-2). We
expect that the LULC parameters in this aggregation area (referred to as “neighborhood” from now
on) can at least partially explain the variation in meteorological observations among the
neighborhoods within each region. We note here that we are particularly interested in roof albedo
as an LULC property to see whether observed temperatures are lower in neighborhoods that have
roofs with higher associated albedo.
4.2.3 Deriving sensitivities of measured air temperature to LULC properties
We compute sensitivity as the linear regression of temperature to LULC properties within a region
(i.e. SFV or central Los Angeles) for each hour of every day during July 2015. Thus, each
regression looks at temperature versus land cover variability from station to station within a given
region. Figure 4-3 shows how the sensitivity of temperature to a LULC property (i.e., reflected
solar power from roofs) is calculated for an example hour of day, 14:00 to 15:00 local time (LT).
Each point on the figure represents an hourly average value of temperature, and spatially
aggregated LULC property, associated with one weather station. After a multi-step outlier removal
process, the sensitivity of temperature to the LULC property is calculated. We perform these
regressions for each hour of the day and thus acquire hourly sensitivities for the entire month of
July 2015. Investigating sensitivities for each hour of the day can help hypothesize physical
processes that are driving the observed correlations. We only compute these regressions for sunny
days, defined as those with daily maximum solar irradiance > 700 W m
-2
.
To ensure that regression results are not dominated by a small number of weather stations, we take
the following steps to remove outlier data points:
80

1. We first detect and remove outlier weather stations for each hour. This is carried out by
first performing a standard least squares regression. The influence of each point in
determining the regression slope is then computed using leverage and residuals. Based on
the distribution of influences for each hour and region, data points that have influence that
is beyond 1.5 times the inner quartile range are removed. This effectively removes points
that have too much influence in determining the final regression statistics. After these
points are removed, another regression is carried out. This time, we use a robust regression
with a Huber-T objective function [Huber, 1981]. Regression using the Huber objective
function gives higher weights to points with lower residuals, whereas standard regression
using least-squares gives equal weights to each observation. The combination of outlier
removal and robust regression minimizes the role of observations with both high leverage
and residual. The sensitivity of temperature to the LULC parameter is then computed as
the slope of the robust regression (i.e.,
).
2. Next we determine whether the computed spatial sensitivity is statistically distinguishable
from zero. We do so by computing the probability (“p”) value of the aforementioned robust
regression. We deem the hourly sensitivity significant if the p-value < 0.1.
3. Lastly, for each hour of the day, we compute the number of days in July with statistically
distinguishable sensitivities for each land cover attribute. Those with > 10 significant days
are deemed as having significant relationships for that hour of day. Those with  10 are
deemed insignificant.

81

4.3 Results
4.3.1 Sensitivity of temperature to solar power reflected from roofs
Figure 4-4 a & b present sensitivities of temperature to daily average solar power reflected by roofs
for central Los Angeles and SFV, respectively. (Note that sensitivities for the early morning hours
between midnight and sunrise use the average solar power of the previous day in Eq. 1, since the
previous day’s solar power is more relevant in determining temperature of the neighborhood than
the average solar power of the upcoming day.) Each box and whisker set show the distribution of
values for sensitivity per hour over all sunny days (daily maximum solar irradiance > 700 W m
-2
)
in July 2015.
Median sensitivities for central Los Angeles (Figure 4-4 a) are negative at all times of day, with
most hours exhibiting statistically significant values. The largest negative sensitivity occurs at
15:00 LT, which is roughly consistent with the hottest time of day (i.e., 14:00 LT in this domain).
This matches our expectation based on the underlying physical processes involved since cooling
occurs via increased reflected solar radiation. While reflected solar radiation peaks at 13:00 LT,
there is an apparent lag between maximum cooling and maximum radiation. As the sun goes down
in the evening, sensitivities trend toward zero, and reach the lowest (negative) sensitivity right
before sun rise. This again matches our expectation based on physical mechanisms since the
apparent temperature reductions induced by reflected solar radiation from roofs are expected to
diminish after the sun goes down. The observed lag between peak temperature reduction and peak
solar irradiance, and the non-zero sensitivities at night, are likely caused by thermal inertia of
roofs. Note that in Los Angeles, the peak temperature occurs earlier (15:00 LT) than many other
cities that do not experience an afternoon sea breeze.
82

Sensitivities of temperature to daily average solar power reflected by roofs for SFV (Figure 4-4b)
show a similar diurnal shape as that of central Los Angeles. However, most hours of day have
sensitivity values that are not statistically significant in SFV. Sensitivities at 14:00 and 15:00 LT
are significant, however. While sensitivities for some hours of night are positive, indicating
counterintuitively that temperatures are positively correlated with increased daily reflected solar
power, these values are not statistically significant.
4.3.2 Sensitivity of temperature to tree fraction
Figure 4-4d present sensitivities of temperature to tree fraction for central Los Angeles and SFV,
respectively.
In central Los Angeles, these sensitivities are positive for most sunlit hours of the day and negative
at night. The maximum positive sensitivity is observed in the early afternoon (14:00 LT). The
apparent positive sensitivities during daytime are counter to expectation based on the underlying
physical mechanisms since increased tree cover should be associated with temperature reductions
through increased evaporative cooling and shading of surfaces. We suggest that these positive
daytime sensitivities are actually driven by co-variations between temperature, tree fraction, and
daily average reflected power from roofs.
To investigate this hypothesis, we present daily average solar power reflected from roofs versus
tree fraction for each neighborhood (Figure 4-5). Solar power reflected from roofs is anti-
correlated to tree fraction with a coefficient of determination of 0.36 for central Los Angeles. This
is consistent with our assertion that solar power reflected from roofs is driving temperature
reductions (and apparent positive sensitivities between temperature and tree fraction) since (a)
neighborhoods with lower tree fraction are associated with higher solar power reflected from roofs
and lower temperatures; and (b) the underlying physical mechanisms suggest that increases in solar
83

power reflected from roofs should lead to temperature reductions, while increases in tree fraction
should not lead to temperature increases. Additional evidence for this assertion is that the diurnal
cycles for sensitivity of temperature to (1) daily average solar power reflected from roofs (Figure
4-4a) versus (2) tree fraction (Figure 4-4b), nearly mirror each other. The largest negative and
positive sensitivity values occur in the early afternoon in both Figure 4-4 a & c, respectively. To
summarize, we assert that daily average solar power reflected from roofs is likely driving both
observed temperature reductions and the apparent positive association between temperature and
tree fraction in this region. This suggests that variations in solar power reflected from roofs (and
thus roof albedo) appear to dominate variations in observed air temperature relative to the effects
of tree fractions.
For SFV, sensitivities of temperature to tree fraction are mostly statistically insignificant, though
some values are significant during nighttime. These significant nighttime values are negative,
suggesting that increased tree fraction is associated with temperature reductions, as expected based
on the underlying physical mechanisms. Observations suggest a temperature reduction of up to
about 1.5 °C per 0.1 increase in tree fraction at night (hours 23:00-7:00 LT). We do not observe
significant positive sensitivity during the day in SFV as was observed for central Los Angeles. We
suggest that this is because less of the variance in solar power reflected by roofs is explained
through variations in tree fraction in SFV (R
2
= 0.09) relative to central Los Angeles (R
2
= 0.36).
The reduced coefficient of determination would lead to less co-variation among temperature, solar
power reflected from roofs, and tree fraction.
We note that the sensitivities reported above are likely specific to the region under investigation.
Other regions with different baseline (a) tree coverage, (b) tree physical properties, (c) soil
84

moisture, and (d) meteorology, among others, are likely to show different relationships between
air temperature and tree fraction.
4.3.3 Roof versus non-roof surfaces as contributors to variability in solar power reflected
from neighborhoods
In this section we aim to rule out the possibility that apparent temperature reductions associated
with increases in solar power reflected by roofs are driven by co-variations in solar power reflected
by non-roof surfaces, such as vegetation and pavement. To do so, we first present daily average
solar power reflected from roofs versus solar power reflected from the neighborhood (Figure 4-6
a). In this analysis, each point represents a different neighborhood. High values of coefficient of
determination (R
2
= 0.80 for SFV and 0.65 for central Los Angeles) indicate that a large proportion
of the variance in solar power reflected from the neighborhood is explainable through variations
in solar power reflected from roofs. In Figure 4-6 b, we present daily average reflected solar power
from non-roof surfaces versus reflected solar power reflected from the neighborhood. In this case,
coefficients of determination are much lower (R
2
= 0.07 and 0.10 for SFV and central Los Angeles,
respectively). This suggests that variations in daily average solar power reflected from the
neighborhood are dominated by variations in solar power reflected by roofs rather than non-roofs
surfaces. This provides additional evidence that observed temperature reductions are being driven
by increases in solar power reflected by roofs in these regions.
4.4 Discussions
4.4.1 Comparison with literature
Santamouris (2012) presented a review of past modeling work on urban heat mitigation strategies.
Most previous modeling studies have investigated the simulated temperature reductions attainable
through hypothetical city-wide adoption of reflective surfaces. In this section, we compare the
85

observational results from our study to the central results reported in Santamouris: daily average
temperature reduction of 0.32 °C per 0.1 increase in neighborhood albedo, and reduction in peak
temperature of 1 °C per 0.1 increase in neighborhood albedo. We focus on comparing reduction
in peak temperature since sensitivities derived from the current study are statistically insignificant
for many hours of day but significant during the hottest time of day for both regions (Figure
4-4a,b). Values for peak daily temperature reductions (i.e., at 15:00 LT) are 0.31 and 0.49 °C per
1 MW increase for SFV and central Los Angeles, respectively. To connect the results of the current
study to Santamouris (2012), we estimate an equivalence between albedo increase and increased
daily average reflected solar power. To derive this equivalence, we use the computed average roof
fraction of 26% and 28%, and daily average solar irradiance of 301 W m
-2
and 253 W m
-2
, for SFV
and central Los Angeles, respectively. Changes in reflected solar power can be expressed as

𝛥 𝑃 = 𝛥 𝛼 ∗ 𝐼 ∗ 𝐴
(4)

See Eq. 2 for symbol definitions. Since A is fixed and we are using average values for I per region,
Eq. 4 can be simplified to

𝛥 𝑃 = 𝛥 𝛼 ∗ 𝐼 ∗ 𝐴
(5)

Assuming that the only source of variability in neighborhood albedo comes from variability in roof
albedo, which is a reasonable assumption give Figure 4-6, Error! Reference source not found.
can be further simplified to
86

𝛥 𝑃 = 𝛥 𝛼 ∗ 𝑓 ∗ 𝐼 ∗ 𝐴
(6)

See Eq. 1 for additional symbol definitions. Based on Eq. 4-Eq. 6 we can estimate the observed
peak temperature reductions per 0.1 increase in average roof albedo or neighborhood albedo (Table
4-2). The latter can also be compared to Santamouris (2012). Computed air temperature reductions
are 1.89 °C and 2.76 °C per roof albedo increase of 0.1 for SFV and central Los Angeles,
respectively. These sensitivities translate to air temperature reductions of 7.26 °C and 9.75 °C per
0.1 increase in neighborhood albedo. These results are higher than the 1 °C per 0.1 increase in
neighborhood albedo reported by Santamouris. This could be the result of the difference between
the timeline and domain of the studies. We have focused on the sunny days in July, which are
among the hottest days of the year in this domain. This difference will result in variations in
sensitivities, since the studies summarized in Santamouris are reporting yearly aggregated
averages. Further, cities with different baseline meteorology and land cover properties would be
expected to have different sensitivities of temperature to land cover.
4.4.2 Dependence of reported temperature-landcover sensitivities to neighborhood
characteristics
It is important to note that sensitivities of air temperature to land cover properties reported here
are relevant to the neighborhoods under investigation and are not necessarily generalizable to other
neighborhoods and/or cities. We expect the sensitivities to vary by baseline land cover and
meteorology.
87

4.4.3 Policy-relevant take-away points
 Increases in reflected solar power from roofs are associated with reductions in observed air
temperatures. Temperature reductions are larger during the day than at night, and peak in
the afternoon. The peak effect is 0.31 °C and 0.49 °C reduction in afternoon air temperature
per MW increase in daily average reflected solar power from the neighborhoods in SFV
and central Los Angeles, respectively. To put this in more tangible terms, we can relate
increases in solar power reflected from roofs to increases in neighborhood or roof albedo
to compute temperature reductions per albedo increase (Table 4-2).
 In central Los Angeles, variations in solar power reflected from roofs (and thus roof albedo)
appear to dominate variations in observed air temperature relative to the effects of tree
fractions. Note that this is based on current neighborhood-to-neighborhood variability in
tree fraction in this region, and should not be interpreted as how future additional tree cover
would affect temperatures. For SFV, observations suggest a temperature reduction of up to
about 1.5 °C per 0.1 increase in tree fraction at night (hours 23:00-7:00 LT).
 As with any observational study, we are observing correlations among temperatures and
land use / land cover parameters. Thus, we cannot definitely make conclusions about
causation. However, we have hypothesized and provided evidence for appreciable
temperature reductions at neighborhood scale due to increasing reflected solar power
through roof albedo increases. To our knowledge, this is the first study to provide
observational evidence of roof albedo increases being associated with temperature
reductions.
88

4.5 Summary
This study investigates the effects of LULC properties on observed air temperatures for different
neighborhoods in two regions of the Los Angeles area: San Fernando Valley (SFV) and central
Los Angeles. Ground observations from a network of personal weather stations have been
analyzed for July 2015. LULC properties of particular focus include roof albedo (and solar power
reflected from roofs) and tree fraction. We find that sensitivities between air temperatures and
these LULC properties vary by region, likely due to their different baseline land cover and
meteorology. Increases in roof albedo are associated with observed air temperature reductions.
Peak sensitivities are 0.49 °C and 0.31 °C per MW reflected average daily solar power reflected
from neighborhoods in central Los Angeles and SFV, respectively. This translates to air
temperature reductions of 1.89 °C and 2.76 °C per roof albedo increase of 0.1 for SFV and central
Los Angeles, respectively. Observed sensitivities in air temperature to albedo are higher than
reported by previous modeling studies. In central Los Angeles, variations in solar power reflected
from roofs (and thus roof albedo) appear to dominate variations in observed air temperature
relative to the effects of tree fractions. Note that this is based on current neighborhood-to-
neighborhood variability in tree fraction in this region, and should not be interpreted as how future
additional tree cover would affect temperatures. For SFV, observations suggest a temperature
reduction of up to about 1.5 °C per 0.1 increase in tree fraction at night (hours 00:00-7:00 LT). To
our knowledge, this study is the first to report observational evidence that roof albedo increases
are associated with neighborhood scale temperature reductions.

89

4.6 Figures and Tables

Figure 4-1. The location of personal weather stations in the Los Angeles basin. The stations are color coded
by “region” as defined in the legend.

SFV
Central Los Angeles
90

Figure 4-2. The aggregation area (green circle of radius 500 m) around an example weather station (red
dot) in SFV. The underlying imagery shows the building footprint dataset.

91

Figure 4-3. Afternoon (14:00-15:00 local time) temperature versus daily average reflected solar power in
central Los Angeles for each day during July 2015. Each subpanel represents one day and each point
represents a single weather station and associated LULC parameter. Slopes from least squares regressions
are used to obtain daily sensitivities of temperature to the LULC parameter under investigation. The mean
irradiance (W m-2) between 14:00-15:00 is shown above each subpanel. Red circular points are removed
as outlier locations. The red dotted regression line corresponds to linear regressions using all points
(including outliers) and the black line corresponds to those using only the black square points. The size of
each point is proportional to its influence.
92

a)
b)
c)
d)

Figure 4-4. Boxplot for diurnal cycle of sensitivity of temperature to (a,b) daily average solar power reflected by roofs, and (c,d) tree fraction. Panels
(a) and (c) are for central Los Angeles and panels (b) and (d) are for SFV. Each box contains the sensitivities per hour for the entire month (July
93

2015). The hours with statistically insignificant sensitivities (see Section 4.2.3 for details) have red hatching. Boxes show the inner-quartile range,
whiskers show [(Q1-1.5 IQR), (Q3+1.5 IQR)], and the black line within the box represents the median.

94

Figure 4-5. Daily average solar power reflected by roofs vs tree fraction for each region. Each point
represents a different neighborhood.

95

a) b)

Figure 4-6. Comparison of daily average solar power reflected from (a) roof and (b) non-roof surfaces versus daily average solar power reflected
from all surfaces in each corresponding neighborhood. Least squares linear regressions are also shown separately for the two areas (i.e., SFV and
central Los Angeles). The higher coefficients of variation (R2) in panel (a) versus (b) suggest that variations in roof albedo are responsible for the
majority of variations in neighborhood albedo

96

Table 4-1. Statistics of observed temperatures per region
Region
Daily min
temperature
1

(°C)
Daily max
temperature
1

(°C)
Daily mean
temperature
1

(°C)
Diurnal
temperature
range
1
(°C)
SFV 19.4 30.5 24.3 11.1
central Los
Angeles
18.9 27.7 22.5 8.8

1
Values are averaged over July 2015

Table 4-2. The temperature reductions per roof and neighborhood average albedo increase.
Area Daily peak
temperature
reduction per 0.1
increase in roof
albedo (°C)
Daily peak temperature
reduction per 0.1 increase
in neighborhood albedo
(°C)
SFV 1.9 7.3
Central
Los
Angeles
2.8 9.8

97

5 Conclusions
In the first study, we set-up a climate modeling framework over the domain California using WRF
model. In order to understand the effect of land cover changes on the meteorology of the domain,
we need to correctly simulate the meteorology and evaluate the biases in the model. California is
an arid region with a diverse climate, bound by oceans on the west and high elevation on the east,
that create a unique climate with sharp temperature gradients.
Since our ultimate goal is to investigate the effect of land cover changes on near surface air
temperature, we need to minimize the model error in predicting temperature in urban areas. We
used high resolution land cover datasets such as NLCD to capture different urban settings and feed
the effect of these settings to model using urban canopy model (Chen et al. 2011), details that the
model cannot analyze due to its courser resolution.
The control simulation using the meteorological model employed here was evaluated, showing
good agreement to observations from 105 stations throughout California of surface air temperature
and weekly accumulated precipitation. The overall mean bias (model minus observation) in surface
air temperature for the entire state of California was -0.30 °. The model tends to predict towards
the average temperature and under-predict the 20-30 °C range, although captures the 30-40 °C
range better.
The results of the model show differences in diurnal cycle of temperature in urban areas, compared
to that of entire domain. The daily minimum of urban areas is higher, which is likely due to higher
heat capacity and anthropogenic heat. The daily peak temperature in urban areas are still lower
than that of entire domain, due to the desert region in the east of California. We can also observe
98

sharper temperature gradients in the late morning towards the noon in urban areas, compared to
domain overall average.
The focus of this study was to prepare a modeling framework for investigating the effect of land
cover changes on local climate. Further studies are required to increase the model accuracy over
the entire domain, specifically if investigation of heat islands and comparison between urban and
non-urban areas are required. Other areas that have room for improvements are to capture the urban
canopy shapes in different urban areas and simulate the urban areas with an instance of the model
that can model the anthropogenic heat.

The second study investigates the effects of increase in pavement albedo on the climate of
California. Perturbation simulations then modeled the climate impacts of widespread pavement
albedo increases of 0.1 and 0.4 in California cities. Our analysis is focused on the effects of
increasing pavement albedo by 0.4, while the increase of 0.1 was mostly used to check the linearity
of the climate response.
We find that a widespread pavement albedo increase of 0.4 leads to reductions in annual average
surface air temperatures in selected California cities ranging from 0.18 to 0.86 °C. These values
are scaled using an offline urban canyon albedo model (see our companion paper, Rosado et al.
2017) that incorporated improved data-derived urban morphologies and shortwave radiative
transfer calculations in the canyon relative to our meteorological model simulations. The variation
among cities is from differences in baseline climate (e.g. wind speed, cloud cover, planetary
boundary layer height, etc), size of the city, urban fraction, and urban morphology (e.g. building
height and roadway width). We find that afternoon (14:00 LST) and 24-hour average surface air
99

temperature reductions are nearly linearly related to pavement, urban, and grid cell average albedo
changes. This provides a basis for exploring the sensitivity of assumed urban morphology (e.g.
building heights, street widths) on air temperature decreases from cool pavements (see the
Discussion section).
Closer examination of the climate impacts of cool pavements in select cities (Sacramento and Los
Angeles) shows that temperature reductions from increasing pavement albedo are largest in the
late morning and early evening. The late morning peak occurs because solar irradiance is
approaching its daily maximum while boundary layer heights are still relatively low, enhancing
the impacts of changes to the surface energy balance on atmospheric temperatures. Similarly, the
early evening peak occurs when ground solar heat gain has accumulated throughout the day and
boundary layer heights are decreasing as the sun goes down. The evening peak in surface air
temperature reduction from cool pavements coincides with the time of day when air temperature
urban heat islands are generally at a maximum. Temperature reductions are also found to be larger
in summer versus winter, as expected.
The second study focuses on the temperature impacts of widespread adoption of cool pavements.
Further research should explore potential impacts of cool pavements on other parts of the climate
system including the hydrological cycle. Changes to air quality from altering radiation fluxes and
meteorology should also be explored. It is of paramount importance that any deployment of cool
pavements avoid increasing the UV reflectance of the land surface. Increasing UV reflectance
could have deleterious health consequences (e.g. potentially increasing skin cancer risk). It could
also increase the actinic flux, which is the driving force for producing photochemical air pollution.
Lastly, as cool pavements alter both temperatures and the surface radiation balance, it is of interest
to further explore their impacts on pedestrian thermal comfort.
100

In the third study we investigated the effect of LULC parameters of neighborhoods on the
temperature measurements from weather station across county of Los Angeles. We focused on two
regions of SFV and CLA, to capture the unique local climate of the region and isolate the unwanted
effect of sea breeze in the domain. Data from 76 stations were gathered from Weather
Underground, a network of personal weather stations. An area in shape of a circle with radius of
500 m was introduced around each weather station, and LULC parameters in the neighborhood
were aggregated and associated with the measurements of the station. Daily average solar power
reflected from the neighborhood and rooftops in neighborhoods were calculated based on the roof
fraction of neighborhood and average incoming solar radiation in the region.
The data from weather stations were cleaned in a comprehensive multi-step process that removed
the faulty stations, records with unreliable data, null values, and outliers. The spatial sensitivity of
the temperatures to aggregated LULC parameters were calculated for each hour of each day in
Month of July 2015. The calculated sensitivities were subjected to a significance test of
determining whether there are more than 10 days of regression between temperature and LULC
properties with p-values smaller than 0.1. The sensitivity of temperature to average daily solar
power reflected from neighborhoods has hourly peaks of 0.49 °C and 0.31 °C per MW central Los
Angeles and SFV, respectively. These values are translated into 2.76 °C and 1.89 °C per 0.1 roof
albedo in Los Angeles and SFV, respectively.
101

The results show higher sensitivity of temperature to LULC properties compared to similar studies.
These differences can be attributed to the date of the measurements, which is selected to be in a
heatwave of 2015 in the middle of summer in Los Angeles.
Further studies are required to improve our understanding of sensitivity of temperature to LULC
properties. The number of stations inside each region of interest needs to be higher than ~30 to
perform reliable multi regression of all of the available parameters. Use of mobile measurements
of temperature can be helpful in capturing the spatial component of the sensitivity, but it needs to
be coupled with fixed references to determine the effect of temporal changes in temperature.

102

6 Appendix
103

Figure 6-1. The figure shows areas with significant 24 h averaged temperature reductions between CTRL
scenario and COOL_HIGH scenario for different seasons. Showing (a) fall, (b) winter, (c) spring, and (d)
summer.

104

Figure 6-2. The figure shows areas with significant 24 h averaged temperature reductions between CTRL
scenario and COOL_LOW scenario for different seasons. Showing (a) fall, (b) winter, (c) spring, and (d)
summer.

105

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115

Abstract (if available)

Abstract 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 Niño or La Niña 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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Asset Metadata

Creator Mohegh, Mohammadhassan (Arash) (author)

Core Title Identifying and mitigating the effects of urban heat islands in California

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Publication Date 10/30/2018

Defense Date 08/06/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag albedo,climate modeling,local climate,OAI-PMH Harvest,urban heat island,weather stations,WRF

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ban-Weiss, George (committee chair), Childress, Amy (committee member), Dilkina, Bistra (committee member)

Creator Email mohegh@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-98756

Unique identifier UC11676676

Identifier etd-MoheghMoha-6916.pdf (filename),usctheses-c89-98756 (legacy record id)

Legacy Identifier etd-MoheghMoha-6916.pdf

Dmrecord 98756

Document Type Dissertation

Format application/pdf (imt)

Rights Mohegh, Mohammadhassan (Arash)

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA