# Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Areas of Analysis

^{2}and includes downtown Los Angeles; we refer to this region as Central Los Angeles. 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 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 unaffected by the influence of coastal air because of the Santa Monica mountains.

#### 2.2. Defining Aggregation Areas

#### 2.3. Meteorological Data

#### 2.4. Description and Data Sources for Land Use Land Cover Properties

**Roof fraction**(${f}_{\mathrm{roof}}$) represents the ratio of building roof area (assumed equal 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 [34].

**Tree fraction**(${f}_{\mathrm{tree}}$) is computed using a tree dataset from LARIAC with a spatial resolution of 4′ (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.

**Pavement fraction**(${f}_{\mathrm{pavement}}$) represents the area fraction of pavement 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 street area is derived using a street centerline dataset [35]. 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 [36], where the weighting factor is determined using the relative quantity of different building types in LA.

**Reflected solar power from roofs**(${P}_{\mathrm{roof}}$) represents the average daily solar power (W) reflected from roofs within the neighborhood. This is computed as:

^{−2}), ${\alpha}_{\mathrm{roof}}$ is the weighted average roof albedo in the neighborhood, and A is the neighborhood area (π × (500)

^{2}m

^{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 (${\alpha}_{\mathrm{roof}}$) is determined using a dataset for seven California cities that reports building-specific roof albedos using remote sensing data [37]; the mean roof albedo is computed for each neighborhood using the roof’s area as the weighting factor. Overall, the metric ${P}_{\mathrm{roof}}$ 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 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**(${P}_{\mathrm{neighborhood}}$) represents the average daily solar power (W) reflected from the entire neighborhood. This parameter is estimated as:

**Reflected solar power from non-roof surfaces**(${P}_{\mathrm{non}-\mathrm{roof}}$) represents the average daily solar power (W) reflected from surfaces other than roofs in the neighborhood. This parameter is computed as ${P}_{\mathrm{neighborhood}}-{P}_{\mathrm{roof}}$.

**Other LULC properties**: Several other LULC properties were found to have insignificant associations with neighborhood scale temperatures. Thus, they are described only briefly here and are addressed further in the Supplemental Material.

**Impervious fraction**is calculated as the sum of roof and pavement fraction.

**Building height**is the mean height (weighted by footprint area) of buildings in a neighborhood and is acquired from the LARIAC dataset.

**Overall albedo**(i.e., albedo accounting for all surfaces) is calculated as presented in Equation (3).

#### 2.5. Deriving Sensitivities of Measured Air Temperature to LULC Properties

^{−2}.

- We first detect and remove outlier weather stations for each hour. This is carried out by first performing a standard least squares linear 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 beyond 1.5 times the inner quartile range of the distribution are removed. This tends to eliminate 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 linear regression with a Huber-T objective function [38]. 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 high leverage, high residuals, or both. The sensitivity of temperature to the LULC parameter $x$ is then computed as the slope of the robust regression (i.e., $\frac{\Delta T}{\Delta x}$).
- 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 is less than 0.1. The choice of this value was rather subjective.
- Lastly, for each hour of the day, we compute the number of days in July with statistically distinguishable sensitivities for each land cover property. Those sensitivities with >10 significant days are deemed as having significant relationships for that hour of the day. Those with ≤10 are deemed insignificant. This threshold was chosen subjectively, but roughly corresponds to half the number of sunny days for this month.

## 3. Results

#### 3.1. The Sensitivity of Temperature to Solar Power Reflected from Roofs

#### 3.2. The Sensitivity of Temperature to Tree Fraction

#### 3.3. Roof versus Non-Roof Surfaces as Contributors to Variability in Solar Power Reflected from Neighborhoods

^{2}= 0.80 in SFV and 0.65 in 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 6b, we present the daily average reflected solar power from non-roof surfaces versus solar power reflected from the neighborhood. In this case, coefficients of determination are much lower (R

^{2}= 0.07 in SFV and 0.10 in Central Los Angeles). 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-roof surfaces. This provides additional evidence that observed temperature reductions are driven by increases in solar power reflected by roofs in these regions.

## 4. Discussion

#### 4.1. Comparison with Literature

#### 4.2. Dependence of Reported Temperature-Landcover Sensitivities to Neighborhood Characteristics

#### 4.3. Policy-Relevant Take-Away Points

- Observed air temperature reductions are associated with increases in reflected solar power from roofs. 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 solar power reflected from the neighborhoods in SFV and Central Los Angeles, respectively. To put this in more tangible terms, we can report temperature reductions per roof or overall albedo increase (Table 2). The average daily temperature reductions are 0.25 °C and 1.84 °C per 0.1 increase in roof albedo, which translates to 0.96 °C and 6.56 °C reduction per overall albedo increase of 0.1 for SFV and Central Los Angeles, respectively.
- 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 an overnight (00:00–07:00 LDT) temperature reduction of up to about 1.5 °C per 0.1 increase in tree fraction.
- As with any observational studies, we are correlating temperature and land use/land cover parameters. Thus, we cannot make definite conclusions about causation. However, we have hypothesized and provided evidence for appreciable temperature reductions at the 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.

## 5. Summary

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The aggregation area, or “neighborhood” (green circle of radius 500 m) around an example weather station (red dot) in SFV. The underlying imagery shows the building footprint dataset.

**Figure 2.**Location of selected personal weather stations in the Los Angeles basin. Stations are color coded to identify study regions. Note that the dots are drawn to scale to indicate the size of each 500 m radius neighborhood.

**Figure 3.**Afternoon (14:00–15:00 LDT) temperature versus daily average reflected solar power from roofs per neighborhood (500 m radius circle around each station) 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 the temperature to the LULC parameter under investigation. The mean irradiance (W/m

^{2}) at 14:00–15:00 LDT is shown above each subpanel. Red dots are removed from regressions as outliers. 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 squares (non-outliers). The size of each point (area) is proportional to its influence.

**Figure 4.**Boxplots for the diurnal cycle of sensitivity of temperature to (

**a**,

**b**) daily average solar power reflected by roofs, and (

**c**,

**d**) tree fraction. Panels (

**a**,

**c**) are for Central Los Angeles, and panels (

**b**,

**d**) are for San Fernando Valley (SFV). Each box contains the sensitivities per hour for the entire month (July 2015). The hours with statistically insignificant sensitivities (see Methodology section for details) have red hatching. Boxes show the inner-quartile range (IQR); whiskers show [(Q1 − 1.5 IQR), (Q3 + 1.5 IQR)], and the black line within the box represents the median. Hour of day 1 = 00:00 to 01:00 LDT.

**Figure 5.**Daily average solar power reflected by roofs vs. tree fraction for each region. Each point represents a different neighborhood (500 m radius around a weather station). Least squares linear regressions are also shown for SFV (black line) and Central Los Angeles (red line).

**Figure 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 determination (R

^{2}) in panel (

**a**) versus (

**b**) suggest that variations in roof albedo are responsible for the majority of variations in neighborhood albedo.

**Table 1.**Statistics of observed temperatures and land use and land cover (LULC) properties under investigation per region. All values are averaged over July 2015.

Daily Minimum Temperature (°C) | Daily Maximum Temperature (°C) | Daily Mean Temperature (°C) | Diurnal Temperature Range (°C) | Mean (Standard Deviation) Building Height (m) | Mean (Standard Deviation) Tree Fraction | Mean (Standard Deviation) Roof Fraction | |
---|---|---|---|---|---|---|---|

SFV | 19.4 | 30.5 | 24.3 | 11.1 | 5.33 (0.49) | 0.12 (0.037) | 0.26 (0.06) |

Central Los Angeles | 18.9 | 27.7 | 22.5 | 8.8 | 5.20 (2.72) | 0.12 (0.075) | 0.25 (0.09) |

Area | Daily Average Temperature Reduction per 0.1 Increase in Roof Albedo (°C) | Daily Average Temperature Reduction per 0.1 Increase in Neighborhood Albedo (°C) | Afternoon ^{a} Temperature Reduction per 0.1 Increase in Roof Albedo (°C) | Afternoon Temperature Reduction per 0.1 Increase in Neighborhood Albedo (°C) | Notes | |
---|---|---|---|---|---|---|

Current study | SFV | 0.25 ^{b} | 0.96 ^{b} | 0.05 | 0.19 | |

Current study | Central Los Angeles | 1.84 | 6.56 | 5.52 | 19.7 | Values from a network of weather stations. Afternoon = 14:00–15:00 LDT |

Santamouris [3] | Various | 0.2 ^{c} | 0.3 ^{d} | 0.4 ^{c} | 0.9 ^{d} | Values come from a meta-analysis of previous climate modeling studies. Time of “peak” afternoon temperature reductions vary by study |

Vahmani et al. [12] | Southern California | 0.35 | 1.84 | Values from climate modeling using the default near-surface air temperature model output. Afternoon = 15:00 LDT | ||

Zhang et al. [22] | Southern California | 0.06 | 0.34 | 0.09 | 1.03 | Values from climate modeling. Temperatures represent “canyon air temperature” rather than the default near-surface temperature model output. Afternoon = 15:00 LDT |

Taha et al. [39] | Downtown Los Angeles | 1.0–9.2 | Values from mobile measurements taken at various times of day, with the highest sensitivity derived from 11:00–14:00 LDT. Note that sensitivity values for measurements taken at night fall within the reported range. |

^{a}The definition of “afternoon” varies by study (see the Notes column).

^{b}Values are statistically insignificant.

^{c}Values are from two previous studies reported in Table 1 of Santamouris [3].

^{d}Values are from several previous studies reported in Figure 1 and Figure 2 of Santamouris [3].

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## Share and Cite

**MDPI and ACS Style**

Mohegh, A.; Levinson, R.; Taha, H.; Gilbert, H.; Zhang, J.; Li, Y.; Tang, T.; Ban-Weiss, G.A. Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo. *Climate* **2018**, *6*, 98.
https://doi.org/10.3390/cli6040098

**AMA Style**

Mohegh A, Levinson R, Taha H, Gilbert H, Zhang J, Li Y, Tang T, Ban-Weiss GA. Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo. *Climate*. 2018; 6(4):98.
https://doi.org/10.3390/cli6040098

**Chicago/Turabian Style**

Mohegh, Arash, Ronnen Levinson, Haider Taha, Haley Gilbert, Jiachen Zhang, Yun Li, Tianbo Tang, and George A. Ban-Weiss. 2018. "Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo" *Climate* 6, no. 4: 98.
https://doi.org/10.3390/cli6040098