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Application of a sensor network to study the energy budget in urban canopies

Application of a sensor network to study the energy budget in urban canopies
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  1  Application of a sensor network to study the energy budget in urban canopies Zhi-Hua Wang, Elie Bou-Zeid, James A. Smith  Department of Civil and Environmental Engineering, Princeton University, Princeton,  New Jersey 08544, USA    Abstract   We adapt a physically-based single-layer urban canopy model (UCM) to study the energy budget in urban areas. The energy budget scheme in this study is similar to the scheme implemented in the WRF-NOAH urban canopy model, but it is decoupled from the Weather Research and Forecasting (WRF) model. Simulations are carried out to quantify the sensitivity of the model to each atmospheric and surface parameter input. To validate and provide accurate inputs to this UCM, several sensing systems are being deployed over the Princeton campus through the Sensor Network Over Princeton (SNOP)  project. When the deployment is complete, the measured field data will include: (1) surface and air temperatures, longwave and shortwave radiation, soil moisture and soil heat flux using a wireless sensing network and two standard surface energy budget stations; (2) vertical profile of wind speed and temperature up to 1000 meters using a SODAR/RASS system; and (3) vertical shear stress and heat flux using a scintillometer. The collected data will be used to derive input parameters representative of the campus area, and to test the model predictions.  2 1. Introduction   According to the United Nations (UN, 2010), more than 50% of world  populations currently live in urban areas. This fraction is projected to increase for the foreseeable future. Consequently, urban environmental conditions and infrastructure (e.g. transportation) are becoming increasingly critical to the well-being of the residents in urban area. Examples of adverse environmental impact of dense urbanization include the heat island effect (Oke 1973, 1982) and air pollution in cities. Unlike flat surfaces or forest canopies, flow in urban canopies is highly localized, inhomogeneous and exhibits more complex patterns. Consequently, modeling and  parameterization in urban canopy are more challenging in the sense that they require a much richer parameter space to capture the physics of the flow environment. The main objectives of this study are: (1) to understand the mechanisms controlling land-atmosphere exchanges in urban areas; (2) to evaluate the current urban canopy model (UCM) implemented in WRF; (3) to improve the parameterization schemes through a distributed sensor network over Princeton campus. 2. Offline WRF-NOAH Urban Canopy Model  Different land use types were conventionally treated as flat surfaces with modified surface parameters (roughness lengths, thermal properties, etc) in climate models. This is an over-simplified approach particularly for urban canopies. Masson (2000) proposed a single-layer energy balance model for urban area, consisting of two surfaces (roof+ground) with systematic parameterization of all surface budgets. The framework was adopted and further developed by Kusaka et al. (2001) and Martilli et al. (2001). Multiple-layer energy balance models with vertical stratification have also been developed (e.g. Kanda et al. 2005) which are more computationally involved. A schematic plot of the turbulent flux resistance network for a single layer model is shown in Fig. 1. The energy budget balance equation involved in the model is given by n F   R Q H LE G + = + +  (1)  3 where n  R  is the net radiation, F  Q ,  H   and  LE   are anthropogenic, sensible, latent heat fluxes respectively, and G  is the heat flux conducted to the solid ground or buildings.  Note in Fig. 1 that we have partitioned the ground into two categories, viz. impervious (concrete/asphalt) and vegetated surfaces. This additional feature is absent from previous models but is essential for a better representation of suburban areas and cities with significant green spaces. Similar division can be made to the roof as well, if the fraction of green roofs is substantial in the study area. Different parameterization schemes for turbulent fluxes, in particular, latent heat due to evaporation are adopted for different categories.  H  G 1 T  G 1  z T   z  R  z a  zT  a  T  a T  W  T  can T  G 2 T   R  H   R  H  can  H  W   H  G 2 T   building hr w   Figure 1: Schematics of turbulence resistance network in single-layer energy balance model The meteorological (forcing) and surface input parameters required by the UCM are listed in Tables 1 and 2 respectively. We obtain the forcing parameters directly from field measurements. From a preliminary sensitivity analysis, the most critical surface  parameters are the thermal properties of solids surfaces (roof, wall and ground).  4 Table 1: Meteorological parameters for UCM Meteorological input data SymbolReference height [m]  z a Temperature at  z a  [K] T  a Zonal wind at  z a  [m/s] u a Meridional wind at  z a  [m/s] v a Specific humifity at  z a  [kg/kg] q a Downward direct solar radiation on a horizontal surface [W/m 2 ] S   D Downward diffuse solar radiation on a horizontal surface [W/m 2 ] S  Q Downward longwave radiation on a horizontal surface [W/m 2 ]  L ↓ Latitude [rad]  φ  Logitude [rad]  λ   Table 2: Canyon dimensions and surface parameters for UCM Canyon dimensions and surface parametersSymbolRoof level (building height) [m]  z r   Normalized building height [-] h  Normalized roof width [-] r   Normalized road width [-] w Zero plane displacement height [m]  z d  Roughness length [m]  z 0 Roof/wall/road surface albedo  α   R  , α  W   , α  G Roof/wall/road surface emissivity  ε   R  , ε  W   , ε  G Roof/wall/road thermal conductivity [W/mK]  κ   R  , κ  W   , κ  G Roof/wall/road heat capacity [J/m 3 K] C   R  , C  W   , C  G Street canyon orientation [rad]  θ  can   3. Sensor Network Over Princeton (SNOP)    Numerous field experimental campaigns have been reported in the meteorological literature. One of the problems associated with most meteorological measurements is the limited coverage both in time and space. SNOP is designed to have a broad coverage by deploying a large array of various sensing systems over a range of sites through  5 continuous measurements. In this project, Princeton campus serves as a test bed resource for the development of environmental sensing systems (the efforts are intimately related to the activities of the Mid-InfraRed Technologies for Health and the Environment, MIRTHE). Instruments that have already been deployed include:    Two Meteorological/Eddy Covariance (EC) Flux stations: one over a roof top and one over a grass field    A scintillometer measuring wind drag and vertical heat flux over a street canyon,    A LIDAR system for monitoring the atmosphere boundary layer,    Three radiometers for measuring longwave and shortwave radiation,    IR cameras monitoring wall surface temperature, and    One disdrometers. The experimental setup of the eddy covariance station and the scintillometer on the roof top of a building are shown in Figure 2. Figure 2: Experimental set up for (a) roof EC station and (b) scintillometer. In addition, a range of instruments will be added to the network, including    A distributed wireless network of 12 meteorological stations,   (a) (b)
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