The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan INFLUENCE OF THE URBAN CANOPY EFFECT IN OSAKA CITY AREA ON THE SURROUNDING METEOROLOGICAL FIELDS Atsushi Moribe*, Ryusuke Yasuda* and Atsumasa Yoshida* *Osaka Prefecture University, Osaka, Japan Abstract In this study, we aimed to investigate the influence of urban canopy effect in Osaka city area on the meteorological field of the surrounding area. A meso-scale meteorological model with an urban canopy model was used to simulate the local circulation in Osaka plain. By model simulation, the decrease of wind speed, the decrease of the maximum air temperature, and increase of the minimum air temperature were confirmed at Osaka, Sakai, Hirakata and Higashi-osaka cities. By urban canopy effect, the minimum air temperature of Osaka increases by 1.5°C and the maximum air temperature at there decreases by 0.6°C. Key words: Meso-scale meteorological model, Urban canopy model, Shaded area 1. INTRODUCTION The space beneath the roof-level of building complex in an urban area is called as “urban canopy layer”. Urban canopy affects local circulation by reduction of wind velocity, enhancement of turbulence near the ground surface, inter-reflection of radiation among building complex, and sensible and latent heat emission from the roof and the walls. Osaka is the third populous city in Japan. The mean air temperature of Osaka city has been increased about 2 °C during the last century, which corresponds to the twice of the increment observed in un-urbanized area in Japan. In recent years, extremely high temperature is often observed.in daytime of mid-summer at the surrounding cities of Osaka city such as Sakai, Hirakata, however, it is not clear how much the urban canopy effect of Osaka city influences on it. In this study, we aimed to investigate the influence of the urban canopy effect of Osaka city area on the air temperature of its surroundings. Thermal environment in mid-summer was investigated by a meso-scale numerical model combined with an urban canopy model. 2. LONG TERM TREND OF AIR TEMPERATURE IN OSAKA AREA Figure1 shows the trend of the monthly mean air temperature in August observed at AMeDAS monitoring stations in Osaka prefecture. The location of these stations are shown in Figure 4. It is clear that mid-summer temperature has been increasing in all of these cities. In these 30 years, the temperature of the surrounding cities (Sakai and Hirakara) have been getting closer to that of Osaka city. 3. NUMERICAL MODEL 3.1 Meso-scale model SAIMM(1) was used to calculate meteorological field. This model is an incompressible dry model and based on hydrostatic approximations. Although this architecture is rather classical and its applicability is limited compared to the up-to-date non-hydrostatic comprehensive models (e.g. WRF, MM5, RAMS), it has been often used in meso-scale meteorological analysis such as land-sea breeze circulation. In this analysis, neither non- hydrostatic effect nor precipitation process are important, so we used this model as a framework with which an urban canopy model can be easy coupled. 3.2 Urban canopy model The urban canopy model we used is a multi-layer model and similar to the one developed by Harayama et al.(2).The schematic diagram of the canopy configuration is shown in Figure 2. Within a grid of the meso-scale model, uniform blocks are extended infinitely. Each building is assumed to be a square prism, so it can be characterized by three parameters, i.e. the width (B), the height (H) and the interval (R) of the buildings. The alignment of the street is parallel or perpendicular to the meridian. According to Harayama et al.(2), the influence of urban buildings on the meso-scale fields is summarized as follows, i) wind reduction by the building complex, ii) production of turbulence by the building complex, iii) solar radiation heat transfer inside and outside of the building complex, iv) long-wave radiation heat transfer inside and outside of the building complex, and v) sensible and latent heat transfer from the building surfaces. To take into account theese urban canopy effects, the following functions were added to SAIMM. 1) Reduction of wind speed Similar to a vegetation canopy model, pressure drag term SWi modeled by the Eq.(1) is added to the momentum equations. Corresponding author: Atsushi Moribe,1-1 Gakuen-cho, Naka-ku, Sakai, Osaka JAPAN 5998531 TEL +81-72-254-9231 / E-mail [email protected] The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan wi −= CS αη u id u k u k (1) where η is a horizontal coverage ratio of the urban canopy area within a grid, Cd is a drag coefficient of the building, αi is the ratio of a wall frontal area in i-direction to the cell volume, and ui is wind velocity in i-direction, respectively. 35 Osaka 33 Sakai N Hirakata 31 29 Temperature(℃) 27 25 1980 1985 1990 1995 2000 2005 Year E Figure 1 Variation of mean temperatures in August . Figure 2 Building arrangement of the urban canopy model 2) Production of turbulence kinetic energy Corresponding to Eq.(1), Eq.(2) is added as a source term to the prognostic equation of turbulence kinetic energy. 2/3 E = CS αη ki (2 u u dk ) (2) 3) Heat budget on building surfaces Heat budget on a wall is expressed as HR s L EH ↓ +++ L↑ R − w R L = G ww , (3) where Rs is net incident shortwave radiation which will be discueed in later. R L↓ and R L↑ is the absorbed and the emitted long wave radiation, respectively. Since our urban canopy model is a dry model, the latent heat flux on a wall surface, LEw , is neglected. The sensible heat flux on a wall, Hw, is estimated by Jurges’s equation. Heat flux into the building wall is obtained by solving one-dimensional heat conduction equation for each surface, ∂ TW (4) W −= kG W ∂z z=0 2 ∂TW ∂ TW ρ cWW = kW (5) ∂t ∂z 2 where TW is the temperature in the wall, cw is the specific heat, ρw is the density, and kw is the heat conductivity, respectively. 4) Radiation in urban canopy layer To calculate direct insolation flux in urban canopy layer, the shaded area on the building surface must be estimated. Kawamoto et al.(3) proposed a simple method to estimate the shaded ratio on the ground surface, but estimation method on the side walls is not clear. We extended it to be applicable to the estimation of the shaded ratio on the side walls. The shadow length of a building on the ground surface can be estimated by the following equations, if no other buildings exist around it, = Hx cot sin φθ , = Hy cot cosφθ (6) where θ is a solar elevation angle, φ is a solar azimuth angle. x and y is the shadow length in east-west and in north-south direction, respectively. For simplification, we classified the shaded pattern on walls into seven types according to the relationships between the block width and the shadow length. For example, a case of the shadow reaching one western block and one northern block (i,e, R < x < 2R+B and R < y < 2R+B ) is shown in Figure 3. The shaded area and the direct insolation flux on the eastern wall are estimated as follows. EA ( HA −= R × tanθ /sinφ )× (− RB / tanφ ) +( − RH × tanθ / cosφ )× (− Ry ) . (7) A = IS cos cos() φθ −ν EA (8) Wall d HB where ν is the angle between the direct insolation beam and the normal vector of the wall, Id is the direct normal insolation. The results obtained by this method show good agreement, the difference is less than 5 %, with ray The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan tracing results. Taking into account the multiple reflection in the canopy layer, the absorbed amount of short- and long-wave radiations are calculated by using the model of Kanda(4)(5) . H-R×tanθ/sinφ y-R B-Rcotφ y-R B-R/tanφ Figure 3 Estimation of shaded area ratio Figure 4 Calculation domain and evaluation points 3.3 Calculation conditions To investigate a typical weather condition in mid-summer, a simulation period was set from 0900 (JST) August 1 to 0900 JST August 6. Only the results of the last 24 hours were used for evaluation. Figure 4 shows the computation domain, which coveres a region of 313 km (E-W) x 378 km (N-S) x 6.6 km (vertical) to reproduce the large-scale land-sea breeze system in Kinki area. The central area of Osaka prefecture, 52 km x 77km, is resolved by 1 km grid. The location of evaluation points (Osaka, Sakai, Hirakata and Higashi-osaka) is also shown in Figure 4. The lower boundary condition is given based on Monin-Obukhov similarity theory. Zero gradient is applied as the lateral boundary condition. In the upper layer, Rayleigh absorption layer is applied . A set of initial vertical profiles for temperature, wind velocity and specific humidity was made by averaging the JMA (Japan Meteorological Agency) aerological data, observed on several days with light-wind clear-sky condition in July and August in 2003 and 2004. The dataset made by Shimoda et al.(6) was used as the anthropogenic heat source (sensible heat only). As the land use data, the digital national information (in 1997) made by Geographical Survey Institute was used.. The urban canopy parameters ( i.e.