# Response of Near-Surface Meteorological Conditions to Advection under Impact of the Green Roof

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{net}represents the net radiation at the surface, SH is the surface sensible heat flux, LH is the surface latent heat flux, and G is the storage heat flux. The net radiation is expressed as

_{in}and SW

_{out}are the incoming and outgoing shortwave radiation, and LW

_{in}and LW

_{out}are the incoming and outgoing longwave radiation. Unlike other strategies that change the albedo and thus reduce the net radiation, the nature of the green roof strategy is to enhance latent heat for given net radiation, thereby reducing the sensible heat flux and resulting in a reduction in atmospheric heating and heat storage over urban areas.

## 2. Methodology

#### 2.1. Model

#### 2.2. Simulations

#### 2.3. Data and Method

#### 2.4. Green Roof Modeling

_{2}is determined by the urban surface temperature and the total sensible heat flux (H

_{total}) from the impervious surface and the vegetated surface:

_{2}is the 2-m wind speed, and C

_{h}

_{2}is the turbulent transfer coefficient. The calculation of T2 can be used as a representative temperature that human beings can feel [64]. In the WRF, the grid cells where the major land use category is one of the three urban categories (low-intensity residential, LIR; high-intensity residential, HIR; and commercial/industrial, COI) are considered as the urban grid cells.

## 3. Results

#### 3.1. Near-Surface Temperature and Winds

#### 3.2. Surface Heat Flux

_{,}and TSK shows the sensitivity of the results due to the use of different PBL schemes. The reduction in TSK in GR is much larger over HIR than over LIR. The implementation of a green roof leads to increased net shortwave radiation (Figure 5i). For net surface longwave radiation, there is also a moderate increase in GR compared to CTL (Figure 5l). The difference in storage heat flux (Figure 5m–o, [67]) between the CTL and GR varies (−20 to 20 W m

^{−2}) over the day and nighttime, with larger storage heat flux in GR during the daytime. Apart from the analysis of surface heat flux components, the extent to which the temperature advection modulates the near-surface temperature is examined in the next section.

#### 3.3. Role of Advection

#### 3.3.1. Temperature Advection

#### 3.3.2. Momentum Advection

#### 3.3.3. Moisture Advection

_{h}is the horizontal wind vector, $\omega $ is the vertical velocity, E means the evaporation, P represents the precipitation, and R is the residual. The symbols < > stand for the mass-weighted vertical integral from 1000 hPa to 100 hPa. The term on the left-hand side is the moisture tendency. The first two terms on the right-hand side are the horizontal (HADV) and vertical (VADV) moisture advection. This equation is used extensively to understand the variability of atmospheric moisture and precipitation [76] that is important for water–climate–society research [77]. Figure 10 presents the terms in the moisture budget with a small residual that provides confidence regarding the estimates of the terms and their interpretations. Since there was no precipitation, we have not shown this in Figure 10. Over the LIR, the evaporation is compensated mostly by VADV in both experiments. In the COI/HIR area, the increase in evaporation in the presence of GR (see Figure 6) is compensated by an increase in HADV and VADV.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Seto, K.C.; Fragkias, M.; Guneralp, B.; Reilly, M.K. A meta-analysis of global urban land expansion. PLoS ONE
**2011**, 6, e23777. [Google Scholar] [CrossRef] [PubMed] - Arnfield, A.J. Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol.
**2003**, 23, 1–26. [Google Scholar] [CrossRef] - Yang, J.; Wang, Z.H.; Chen, F.; Miao, S.; Tewari, M.; Voogt, J.; Myint, S. Enhancing hydrologic modelling in the coupled Weather Research and Forecasting-urban modelling system. Bound. Layer Meteorol.
**2015**, 155, 87–109. [Google Scholar] [CrossRef] - Burian, S.J.; Shepherd, J.M. Effect of urbanization on the diurnal rainfall pattern in Houston. Hydrol. Process.
**2005**, 19, 1089–1103. [Google Scholar] [CrossRef] - Oleson, K.W.; Bonan, G.B.; Feddema, J.; Jackson, T. An examination of urban heat island characteristics in a global climate model. Int. J. Climatol.
**2011**, 31, 1848–1865. [Google Scholar] [CrossRef] - Unkasevic, M.; Jovanovic, O.; Popovic, T. Urban-suburban/rural vapour pressure and relative humidity differences at fixed hours over the area of Belgrade city. Theor. Appl. Climatol.
**2001**, 68, 67–73. [Google Scholar] [CrossRef] - Georgescu, M.; Mahalov, A.; Moustaoui, M. Seasonal hydroclimatic impacts of Sun Corridor expansion. Environ. Res. Lett.
**2012**, 7, 034026. [Google Scholar] [CrossRef] - Bornstein, R.D.; Johnson, D.S. Urban-rural wind velocity differences. Atmos. Environ.
**1977**, 11, 597–604. [Google Scholar] [CrossRef] - Fernando, H.J.S. Fluid dynamics of urban atmospheres in complex terrain. Annu. Rev. Fluid Mech.
**2010**, 42, 365–389. [Google Scholar] [CrossRef] - Landsberg, H.E. The Urban Climate; Academic Press: New York, NY, USA, 1981; 275p. [Google Scholar]
- Oke, T.R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc.
**1982**, 108, 1–24. [Google Scholar] [CrossRef] - Hassid, S.; Santamouris, M.; Papanikolaou, N.; Linardi, A.; Klitsikas, N.; Georgakis, C.; Assimakopoulos, D.N. The effect of the Athens heat island on air conditioning load. Energy Build.
**2000**, 32, 131–141. [Google Scholar] [CrossRef] - Cartalis, C.; Synodinou, A.; Proedrou, M.; Tsangrasoulis, A.; Santamouris, M. Modification in energy demand in urban areas as a result of climate changes: An assessment for the Southeast Mediterranean region. Energy Convers. Manag.
**2001**, 42, 1647–1656. [Google Scholar] [CrossRef] - Santamouris, M.; Papanikolaou, N.; Livada, I.; Koronakis, I.; Georgakis, C.; Argiriou, A.; Assimakopoulos, D.N. On the impact of urban climate to the energy consumption of buildings. Sol. Energy
**2001**, 70, 201–216. [Google Scholar] [CrossRef] - Grimmond, S. Urbanization and global environmental change: Local effects of urban warming. Cities Glob. Environ. Chang.
**2007**, 173, 83–88. [Google Scholar] [CrossRef] - Rosenfeld, A.H.; Akbari, H.; Bretz, S.; Fishman, B.L.; Kurn, D.M.; Sailor, D.; Taha, H. Mitigation of urban heat islands: Material, utility programs, updates. Energy Build.
**1995**, 22, 255–265. [Google Scholar] [CrossRef] - Akbari, H.; Pomerantz, M.; Taha, H. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Sol. Energy
**2001**, 70, 295–310. [Google Scholar] [CrossRef] - Akbari, H.; Levinson, R. Evolution of cool-roof standards in the U.S. Adv. Build. Energy Res.
**2008**, 2, 1–32. [Google Scholar] [CrossRef] - Theodosiou, T. Green roofs in buildings: Thermal and environmental behavior. Adv. Build. Energy Res.
**2009**, 3, 271–288. [Google Scholar] [CrossRef] - Sfakianaki, A.; Pagalou, E.; Pavlou, K.; Santamouris, M.; Assimakopoulos, M. Theoretical and experimental analysis of the thermal behavior of a green roof system installed in two residential buildings in Athens, Greece. Int. J. Energy Res.
**2009**, 33, 1059–1069. [Google Scholar] [CrossRef] - Zinzi, M. Cool materials and cool roofs: Potentialities in Mediterranean buildings. Adv. Build. Energy Res.
**2010**, 4, 201–266. [Google Scholar] [CrossRef] - Gaitani, N.; Spanou, A.; Saliari, M.; Synnefa, A.; Vassilakopoulou, K.; Papadopoulou, K.; Pavlou, K.; Santamouris, M.; Papaioannou, M.; Lagoudaki, A. Improving the microclimate in urban areas: A case study in the centre of Athens. J. Build. Serv. Eng.
**2011**, 32, 53–71. [Google Scholar] [CrossRef] - Chow, W.T.L.; Chuang, W.C.; Gober, P. Vulnerability to extreme heat in metropolitan Phoenix: Spatial, temporal, and demographic dimensions. Prof. Geogr.
**2012**, 64, 286–302. [Google Scholar] [CrossRef] - Takebayashi, H.; Moriyama, M. Study of a simple evaluation method of urban heat island mitigation technology using upper-air data. J. Heat Isl. Inst. Int.
**2012**, 7, 102–110. [Google Scholar] - Santamouris, M. On the energy impact of urban heat island and global warming on buildings. Energy Build.
**2014**, 82, 100–113. [Google Scholar] [CrossRef] - Akbari, H.; Rose, L.S. Urban surfaces and heat island mitigation potential. J. Hum. Environ. Syst.
**2007**, 11, 85–101. [Google Scholar] [CrossRef] - Georgescu, M. Challenges associated with adaptation to future urban expansion. J. Clim.
**2015**, 28, 2544–2563. [Google Scholar] [CrossRef] - Yang, J.; Yu, Q.; Gong, P. Quantifying air pollution removal by green roofs in Chicago. Atmos. Environ.
**2008**, 42, 7266–7273. [Google Scholar] [CrossRef] - Skamarock, W.C.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Barker, D.M.; Duda, M.G.; Huang, X.-Y.; Wang, W.; Powers, J.G. A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR. NCAR Tech. Notes
**2008**. [Google Scholar] [CrossRef] - Kusaka, H.; Kondo, H.; Kikegawa, Y.; Kimura, F. A simple single-layer urban canopy model for atmospheric models: Comparison with multilayer and slab models. Bound. Layer Meteorol.
**2001**, 101, 329–358. [Google Scholar] [CrossRef] - Wang, Z.H.; Bou-Zeid, E.; Au, S.K.; Smith, J.A. Analyzing the sensitivity of WRF’s single-layer urban canopy model to parameter uncertainty using advanced Monte Carlo simulation. J. Appl. Meteorol. Climatol.
**2011**, 50, 1795–1814. [Google Scholar] [CrossRef] - Wang, Z.H.; Smith, J.A. A spatially-analytical scheme for surface temperatures and conductive heat fluxes in urban canopy models. Bound. Layer Meteorol.
**2011**, 138, 171–193. [Google Scholar] [CrossRef] - Grimmond, C.S.B.; Blacketta, M.; Bestb, M.J.; Barlowc, J.; Baikd, J.-J.; Belcherc, S.E.; Bohnenstengelc, S.I.; Calmete, I.; Chenf, F.; Dandoug, A.; et al. The International Urban Energy Balance Models Comparison Project: First results from phase 1. J. Appl. Meteorol. Climatol.
**2010**, 49, 1268–1292. [Google Scholar] [CrossRef] - Lee, S.H.; Kim, S.W.; Angevine, W.M.; Bianco, L.; McKeen, S.A.; Senff, C.J.; Trainer, M.; Tucker, S.C.; Zamora, R.J. Evaluation of urban surface parameterization in WRF model using measurements during the Texas Air Quality Study 2006 field campaign. Atmos. Chem. Phys.
**2011**, 11, 2127–2143. [Google Scholar] [CrossRef][Green Version] - Wang, Z.H.; Bou-Zeid, E.; Smith, J.A. A coupled energy transport and hydrological model for urban canopies evaluated using a wireless sensor network. Q. J. Meteorol. Soc.
**2013**, 139, 1643–1657. [Google Scholar] [CrossRef] - Miao, S.; Chen, F. Formation of horizontal convective rolls in urban areas. Atmos. Res.
**2008**, 89, 298–304. [Google Scholar] [CrossRef] - Brownlee, J.; Ray, P.; Tewari, M.; Tan, H. Relative role of turbulent and radiative flux on the near-surface temperature in a single-layer urban canopy model over Houston. J. Appl. Meteorol. Climatol.
**2017**, 56, 2173–2187. [Google Scholar] [CrossRef] - Li, D.; Bou-Zeid, E. Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts. J. Appl. Meteorol. Climatol.
**2013**, 52, 2051–2064. [Google Scholar] [CrossRef][Green Version] - Streutker, D.R. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens. Environ.
**2003**, 85, 282–289. [Google Scholar] [CrossRef] - Salamanca, F.; Martilli, A.; Tewari, M.; Chen, F. A study of the urban boundary layer using different urban parameterizations and high-resolution urban canopy parameters with WRF. J. Appl. Meteorol. Climatol.
**2011**, 50, 1107–1128. [Google Scholar] [CrossRef] - Smith, K.R.; Roebber, P. Green roof mitigation potential for a proxy future climate scenario in Chicago, Illinois. J. Appl. Meteorol. Climatol.
**2011**, 50, 507–522. [Google Scholar] [CrossRef] - Sun, T.; Grimmond, C.S.B.; Ni, G.H. How do green roofs mitigate urban thermal stress under heat waves? J. Geophy. Res. Atmos.
**2016**, 121, 5320–5335. [Google Scholar] [CrossRef] - Yang, J.; Wang, Z.H.; Georgescu, M.; Chen, F.; Tewari, M. Assessing the impact of enhanced hydrological processes on urban hydrometeorology with application to two cities in contrasting climates. J. Hydrometeorol.
**2016**, 17, 1031–1047. [Google Scholar] [CrossRef] - Sharma, A.; Conry, P.; Fernando, H.J.S.; Hamlet, A.F.; Hellmann, J.J.; Chen, F. Green and cool roofs to mitigate urban heat island effects in the Chicago metropolitan area: Evaluation with a regional climate model. Environ. Res. Lett.
**2016**, 11, 064004. [Google Scholar] [CrossRef] - Tewari, M.; Yang, J.; Kusaka, H.; Salamanca, F.; Watson, C.; Treinish, L. Interaction of urban heat islands and heat waves under current and future climate conditions and their mitigation using green and cool roofs in New York City and Phoenix, Arizona. Environ. Res. Lett.
**2019**, 14, 034002. [Google Scholar] [CrossRef] - De Munck, C.; Lemonsu, A.; Bouzouidja, R.; Masson, V.; Claverie, R. The GREENROOF module (v7.3) for modelling green roof hydrological and performances within TED. Geosci. Model Dev.
**2013**, 6, 1941–1960. [Google Scholar] [CrossRef][Green Version] - Xie, X.M.; Nielsen-Gammon, J.W.; Zhang, F. Evaluation of three planetary boundary layer schemes in the WRF model. J. Appl. Meteorol. Climatol.
**2010**, 49, 1831–1844. [Google Scholar] - Xie, B.; Fung, J.C.H.; Chan, A.; Lau, A. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model. J. Geophys. Res.
**2012**, 117, D12103. [Google Scholar] [CrossRef] - Cohen, A.E.; Cavallo, S.M.; Coniglio, M.C.; Brooks, H.E. A review of planetary boundary layer parameterization schemes and their sensitivity in simulating southeastern U.S. cold season severe weather environments. Weather Forecast.
**2015**, 30, 591–612. [Google Scholar] [CrossRef] - Miao, S.; Chen, F. Enhanced modeling of latent heat flux from urbansurfaces in the Noah/single-layer urban canopy coupled model. Sci. China Earth Sci.
**2014**, 57, 2408–2416. [Google Scholar] [CrossRef] - Nielsen-Gammon, J.W. Evaluation and Comparison of Preliminary Meteorological Modeling for the August 2000 Houston–Galveston Ozone Episode. TNRCC Report. 2002. Available online: https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/mm/EvalComp_Preliminary_MM5_Modeling_2000Aug.pdf (accessed on 5 February 2002).
- Cheng, F.Y.; Byun, D.W. Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston metropolitan area, part I: Meteorological simulation results. Atmos. Environ.
**2008**, 42, 7795–7811. [Google Scholar] [CrossRef] - Hong, S.-Y.; Noh, Y.; Dudhia, J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev.
**2006**, 134, 2318–2341. [Google Scholar] [CrossRef][Green Version] - Janjic, Z.I. The step-mountain eta coordinate model: Further developments of the convection, viscous sub layer, and turbulence closure schemes. Mon. Weather Rev.
**1994**, 122, 927–945. [Google Scholar] [CrossRef][Green Version] - Nakanishi, M.; Niino, H. An improved Mellor–Yamada level 3 model: Its numerical stability and application to a regional prediction of advecting fog. Bound. Layer Meteorol.
**2006**, 119, 397–407. [Google Scholar] [CrossRef] - Bougeault, P.; Lacarrere, P. Parameterization of Orography–Induced Turbulence in a Mesobeta––Scale Model. Mon. Weather Rev.
**1989**, 117, 1872–1890. [Google Scholar] [CrossRef] - Hong, S.Y.; Dudhia, J.; Chen, S.H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev.
**2004**, 132, 103–120. [Google Scholar] [CrossRef] - Chen, F.; Dudhia, J. Coupling an advanced land surface—Hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Weather Rev.
**2001**, 129, 569–585. [Google Scholar] [CrossRef][Green Version] - Mlawer, E.J.; Taubman, S.J.; Brown, P.D.; Iacono, M.J.; Clough, S.A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophy. Res.
**1992**, 102, 16663–16682. [Google Scholar] [CrossRef][Green Version] - Dudhia, J. Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two—Dimensional model. J. Atmos. Sci.
**1989**, 46, 3077–3107. [Google Scholar] [CrossRef] - Kain, J.S. The Kain—Fritsch convective parameterization: An update. J. Appl. Meteorol.
**2004**, 43, 170–181. [Google Scholar] [CrossRef][Green Version] - Berg, L.K.; Zhong, S. Sensitivity of MM5-simulated boundary layer characteristics to turbulence parameterizations. J. Appl. Meteorol.
**2005**, 44, 1467–1483. [Google Scholar] [CrossRef] - Banks, R.F.; Baldasano, J.M. Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain. Sci. Total Environ.
**2016**, 572, 98–113. [Google Scholar] [CrossRef][Green Version] - Li, D.; Bou-Zeid, E. Quality and sensitivity of high-resolution numerical simulation of urban heat islands. Environ. Res. Lett.
**2014**, 9, 055001. [Google Scholar] [CrossRef] - Shimada, S.; Ohsawa, T.; Chikaoka, S.; Kozai, K. Accuracy of the wind speed profile in the lower PBL as simulated by the WRF model. SOLA
**2011**, 7, 109–112. [Google Scholar] [CrossRef][Green Version] - Niachou, A.; Papakonstantinou, K.; Santamouris, M.; Tsangrassoulis, A.; Mihalakakou, G. Analysis of the green roof thermal properties and investigation of its energy performance. Energy Build.
**2001**, 33, 719–729. [Google Scholar] [CrossRef] - Moriwaki, R.; Kanda, M. Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area. J. Appl. Meteorol.
**2004**, 43, 1700–1710. [Google Scholar] [CrossRef] - Oke, T.R.; Maxwell, G.B. Urban heat island dybamics in Montreal and Vancouver. Atmos. Environ.
**1975**, 9, 191–200. [Google Scholar] [CrossRef] - Large, W.G.; Pond, S. Open Ocean Momentum Flux Measurements in Moderate to Strong Winds. J. Phys. Oceanogr.
**1981**, 11, 324–336. [Google Scholar] [CrossRef][Green Version] - Gregory, D.; Kershaw, R.; Inness, P.M. Parametrization of momentum transport by convection. II: Tests in single-column and general circulation models. Q. J. R. Meteorol. Soc.
**1997**, 123, 1153–1183. [Google Scholar] [CrossRef] - Carr, M.T.; Bretherton, C.S. Convective momentum transport over the tropical Pacific: Budget estimates. J. Atmos. Sci.
**2001**, 58, 1673–1693. [Google Scholar] [CrossRef] - Richter, J.H.; Rasch, P.J. Effects of Convective Momentum Transport on the Atmospheric Circulation in the Community Atmosphere Model, Version 3. J. Clim.
**2008**, 21, 1487–1499. [Google Scholar] [CrossRef] - Ray, P.; Zhang, C. A case study of the mechanics of extratropical influence on the initiation of the Madden-Julian oscillation. J. Atmos. Sci.
**2010**, 67, 515–528. [Google Scholar] [CrossRef] - Ray, P.; Li, T. Relative roles of the circumnavigating waves and the extratropics on the MJO and its relationship with the mean state. J. Atmos. Sci.
**2013**, 70, 876–893. [Google Scholar] [CrossRef] - Tan, H.; Ray, P.; Barrett, B.S.; Tewari, M.; Moncrieff, M.W. Role of topography on the MJO in the Maritime Continent: A numerical case study. Clim. Dyn.
**2018**. [Google Scholar] [CrossRef] - Ray, P.; Zhang, C.; Dudhia, J.; Li, T.; Moncrieff, M.W. Tropical Channel Model. In Climate Models; Druyan, L.M., Ed.; InTech Open Access Publisher: Rijeka, Croatia, 2012; pp. 3–18. ISBN 978-953-308-181-6. [Google Scholar]
- McNeely, S.; Tessendorf, A.S.; Lazrus, H.; Heikkila, T.; Ferguson, M.I.; Arrigo, S.J.; Attari, A.Z.; Cianfrani, C.M.; Dilling, L.; Gurdak, J.J.; et al. Catalyzing frontiers in water-climate-society research: A view from early career scientists and junior faculty. Bull. Am. Meterol. Soc.
**2012**, 93, 477–484. [Google Scholar] [CrossRef]

**Figure 1.**(

**a**) Weather Research and Forecasting (WRF) domain configuration. The outer domain (DO3), the middle domain (DO2) and the inner domain (DO3) have 9 km, 3 km, and 1 km horizontal resolutions, respectively. (

**b**) Urban land use categories (shaded) indicating low-intensity residential (LIR, yellow), high-intensity residential (HIR, red), commercial/industrial urban land use (COI, green), rural (white) and water (blue).

**Figure 2.**Schematic representation of urban grid cell used in WRF for green roof modeling. The grid cell has two parts: the impervious fraction and the pervious fraction. H

_{a}is the height of the first level in the atmospheric model, H

_{r}is the height of the building roof-top, and H

_{c}is the street canyon height. In our simulations, there are conventional roofs and green roofs. The ground surface is composed of tar road, concrete and grass (50%, 30%, and 20%). The SH and LH are the sensible and latent heat flux. The subscript (g) stands for ground, (veg) for vegetated fraction, (w) for building wall, (r) for roof, (CR) for conventional roof, and (GR) for green roof. G

_{g}is the storage heat flux. T

_{a,}T

_{c,}and T

_{veg}denote the temperatures at the first level of an atmospheric model, at the street canyon, and at the vegetated fraction, respectively. Out of the total roof area, 50% of the roof was considered as GR, and the other 50% was the conventional roof.

**Figure 3.**(top) The 2m temperature (°C) from observations (black) and CTL (red) averaged across all stations for (

**a**) LIR and (

**b**) COI/HIR. The bottom panels are for 10 m winds (m s

^{−1}). The X-axis is the UTC from 1200Z, 25 August to 1200Z, 26 August 2000.

**Figure 4.**(left) Surface skin temperature (TSK), and (right) 2 m temperature (T

_{2}) for GR minus CTL (

**a**,

**b**) at 2000 UTC (1400 Local Standard Time, hereby LST) when the difference between the GR and CTL is maximum, (

**c**,

**d**) during the daytime (averaged between 0700 LST to 1900 LST, 25 August), and (

**e**,

**f**) nighttime (averaged between 1900Z, 25 August to 0700Z, 26 August 2000). Unit: °C.

**Figure 5.**(

**a–c**) Latent heat flux, (

**d**–

**f**) sensible heat flux, (

**g**–

**i**), net shortwave radiation, (

**j**–

**l**), net longwave radiation and (

**m**–

**o**) storage heat flux for (left) CTL, (middle) GR, and (right) GR minus CTL at 1900 UTC (1400 LST). The color bar on the right is for the difference plots. Unit: W m

^{−2}.

**Figure 6.**(left) The local tendency of temperature (LT, red), zonal advection (Z, orange), meridional advection (M, green) and horizontal advection (TA, blue) over LIR from (

**a**) CTL, (

**c**) GR, and (

**e**) GR minus CTL. The right panels are for COI/HIR areas ((

**b**) CTL, (

**d**) GR, and (

**e**) GR minus CTL). Unit in K hr

^{−1}.

**Figure 7.**The terms of the zonal momentum budget from ((

**a**) and (

**b**)) the CTL, ((

**c**) and (

**d**)) the GR and ((

**e**) and (

**f**)) GR minus CTL for local tendency (first term on LHS in Equation (6)), horizontal advection or HADV (sum of the first two terms on the RHS in Equation (6)). The VADV + Residual (vertical advection plus residual; i.e., the sum of the third and sixth terms on the RHS in Equation (6)) and the pressure gradient and Coriolis (PGF + Cori; i.e., the sum of the fourth and fifth terms on RHS in Equation (6)). Unit m s

^{−1}day

^{−1}.

**Figure 8.**Same as Figure 7, but for the meridional momentum budget from ((

**a**) and (

**b**)) the CTL, ((

**c**) and (

**d**)) the GR and ((

**e**) and (

**f**)) GR minus CTL.

**Figure 9.**Time-averaged (1800Z, 25 August–2200Z, 25 August) terms in ((

**a**) and (

**b**)) zonal and ((

**c**) and (

**d**)) meridional momentum budgets for CTL (blue) and GR (red) over LIR and COI/HIR. Unit in mm sec

^{−1}day

^{−1}.

**Figure 10.**The moisture budget terms (averaged over 1800Z–2200Z, 25 August) over the (

**a**) LIR and (

**b**) COI/HIR areas from the CTL (blue) and GR (red) simulations. E is the evaporation, HADV is the horizontal moisture advection, VADV is the vertical moisture advection, ∂q/∂t is the local tendency of moisture, and R is the residual. unit in mm day

^{−1}.

**Table 1.**Parameter values used in the simulations for three different urban categories (low-intensity residential, LIR; high-intensity residential, HIR; and commercial/industrial, COI).

COI | HIR | LIR | Unit | |
---|---|---|---|---|

Mean building height (h) | 10 | 7.5 | 5 | m |

Roof width (R) | 10 | 9.4 | 8.3 | m |

Road width (Rd) | 10 | 9.4 | 8.3 | m |

Impervious surface fraction (f_{impervious}) | 95 | 90 | 50 | % |

Roof fraction of the impervious part (f_{roof} = R/(R + Rd)) | 50 | 50 | 50 | % |

Canyon fraction of the impervious part (f_{canuon} = 1 − f_{roof}) | 50 | 50 | 50 | % |

Roof fraction in the whole urban grid (f_{roof} × f_{impervious}) | 47.5 | 45 | 25 | % |

**Table 2.**Summary of the model configurations. The references for the planetary boundary layer (PBL) schemes can be found in the text.

Experiment | Urban Parameterization & Hydrological Options | Purpose | Planetary Boundary Layer Schemes |
---|---|---|---|

Control (CTL) | Single-layer urban canopy model (SLUCM) with the following hydrological options: anthropogenic heat, urban oasis, urban irrigation, and evaporation. | Use as a benchmark for SLUCM with urban hydrological processes | Yonsei University (YSU) scheme Mellor–Yamada–Janjia (MYJ) scheme Mellor–Yamada–Nakanishi–Niino (MYNN2.5) scheme Boujeault–Lacarrere (BouLac) scheme |

Green Roof (GR) | Same as control (CTL), but with multi-layer green roof systems | To explore the extent to which the multi-layer green roof can influence the advective processes |

**Table 3.**Mean bias and RMSE of 2 m temperature (°C) and 10 m winds (m s

^{−1}) for LIR and COI/HIR stations for simulations using different PBL schemes. The ensemble refers to the average of all 4 simulations using different PBL schemes. The period for the calculation is 1200Z, 25 August to 1200Z, 26 August 2000.

CTL | PBL Scheme | T2 Mean Bias | T2 RMSE | W10 Mean Bias | W10 RMSE |
---|---|---|---|---|---|

LIR | YSU | −0.62 | 0.72 | 0.38 | 0.65 |

MYJ | −0.55 | 0.66 | 0.00 | 0.49 | |

MYNN2.5 | −0.76 | 0.96 | 0.06 | 0.7 | |

BouLac | −0.32 | 0.48 | 0.02 | 0.6 | |

Ensemble | −0.56 | 0.59 | 0.12 | 0.53 | |

COI/HIR | YSU | −1.01 | 1.33 | 0.21 | 1.33 |

MYJ | −0.83 | 1.01 | −0.18 | 1.01 | |

MYNN2.5 | −0.99 | 1.2 | −0.17 | 1.2 | |

BouLac | −0.57 | 0.87 | −0.21 | 0.87 | |

Ensemble | −0.85 | 1.01 | −0.09 | 0.88 |

**Table 4.**Texas Commission on Environmental Quality (TCEQ) stations: low-intensity residential (LIR), high-intensity residential (HIR) and commercial/industrial (COI). CAMS: Continuous Ambient Monitoring Station.

Station ID | Latitude (°) | Longitude (°) | Land Category |
---|---|---|---|

CAMS 1 | 29.7681 | −95.2206 | LIR |

CAMS 15 | 29.8025 | −95.1256 | LIR |

CAMS 81 | 29.7335 | −95.3156 | HIR |

CAMS 100 | 29.3900 | −94.9194 | COI |

CAMS 108 | 29.9010 | −95.3261 | LIR |

CAMS 146 | 29.6957 | −95.4992 | HIR |

CAMS 167 | 29.7342 | −95.2383 | COI |

CAMS 169 | 29.7062 | −95.2611 | LIR |

CAMS 403 | 29.7336 | −95.2575 | COI |

CAMS 404 | 29.8069 | −95.2847 | COI |

CAMS 409 | 29.6239 | −95.4742 | LIR |

CAMS 603 | 29.7633 | −95.1811 | COI |

**Table 5.**Latent heat flux, sensible heat flux and surface skin temperature associated with 4 PBL schemes during daytime (averaged over 1200-0000Z, 25 August 2000), nighttime (averaged over 0000-1200Z, 26 August 2000) and daytime plus nighttime over LIR and COI/HIR areas.

LIR | Daytime | Nighttime | Day+Night |
---|---|---|---|

Latent heat flux (W m ^{−2}) | 266–273 (CTL) 273–278 (GR) | 5–7 (CTL) 6–8 (GR) | 137–140 (CTL) 141–143 (GR) |

Sensible heat flux (W m ^{−2}) | 60–70 (CTL) 44–53 (GR) | −3– −1 (CTL) –2– −3 (GR) | 29–34 (CTL) 21–25 (GR) |

Surface skin temperature (°C) | 32–34 (CTL) 31–33 (GR) | 26–27 (CTL) 26–27 (GR) | 29–30 (CTL) 29–30 (GR) |

COIHIR | |||

Latent heat flux (W m ^{−2}) | 54–56 (CTL) 72–77 (GR) | 16–16 (CTL) 17–19 (GR) | 35–36 (CTL) 44–47 (GR) |

Sensible heat flux (W m ^{−2}) | 172–179 (CTL) 135–139 (GR) | 18–19 (CTL) 16–16 (GR) | 95–98 (CTL) 76–78 (GR) |

Surface skin temperature (°C) | 35–39 (CTL) 31–33 (GR) | 27–28 (CTL) 26–27 (GR) | 31–33 (CTL) 29–30 (GR) |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tan, H.; Ray, P.; Tewari, M.; Brownlee, J.; Ravindran, A.
Response of Near-Surface Meteorological Conditions to Advection under Impact of the Green Roof. *Atmosphere* **2019**, *10*, 759.
https://doi.org/10.3390/atmos10120759

**AMA Style**

Tan H, Ray P, Tewari M, Brownlee J, Ravindran A.
Response of Near-Surface Meteorological Conditions to Advection under Impact of the Green Roof. *Atmosphere*. 2019; 10(12):759.
https://doi.org/10.3390/atmos10120759

**Chicago/Turabian Style**

Tan, Haochen, Pallav Ray, Mukul Tewari, James Brownlee, and Ajaya Ravindran.
2019. "Response of Near-Surface Meteorological Conditions to Advection under Impact of the Green Roof" *Atmosphere* 10, no. 12: 759.
https://doi.org/10.3390/atmos10120759