The Model Simulation of the Architectural Micro-Physical Outdoors Environment

Transcription

1 The Model Simulation of the Architectural Micro-Physical Outdoors Environment Che-Ming Chiang 1, De-En Lin 2, Po-Cheng Chou 3, Yen-Yi Li 3 1 Department of Architecture, National Cheng Kung University, Tainan, Taiwan 2 Archilife Research Foundation, Taipei, Taiwan 3 Department of Interior Design, Shu-Te University of Science and Technology, Kaohisiung, Taiwan ABSTRACT: To pursue higher security, better healthy, and more comfort for dweller, it is very important to study the correlation about outdoor and indoor atmosphere status and the sensitive information of micro-climate meteorology which the building provided. Through the latest meso-scale model WRF to proces numerical emulation about the Archilife research living center, it could make up the space and time resolution deficiency in traditional weather observed tools, then in accordance with the model emulation results to retrieve information back to the background data of indoor micro climate and weather station, so it could provide more scientific evidence to clarify the relation between indoor, outdoor physical environment and buildings. The research results reveal that the ability of WRF model emulating the micro climate and weather status is well generally, as for the problem of higher weather factors magnitude in model simulated result than real observed one, especially in wind field, this maybe caused by terrain characteristic and geographical features over there, that could be fixed by improving the terrain parameter and tuning the boundary condition of model in the future. KEYWORDS: WRF model, micro-physical environment, weather forecast system 1. INTRODUCTION The main function of the modern buildings, in addition to provide the residents a secure living environment, the ability of pursuing the quality of life and offering comfortable living is also more and more subjected by people as time goes by. Perceiving the characteristics of weather system and environment around buildings thoroughly, and controlling the period and frequency of variety by the way of following tendency, turning resistance into assistance, and adverse status into fortunate status, the life property will be advanced more effectively and the restriction produced by natural environment will also be reduced. To retrieve the environmental static data into reality generation, then identify the relation between various weather systems owning different scales, and find out the optimum experiment scheme, the scientific significance will be acquired and extracted more legitimately. 2. RESEARCH METHOD The research method is applying the WRF meso-scale model to process numerical emulation about the Archilife research living center. The latest WRF model (Weather Research Forecasting model) adopts the sigma coordinate in vertical plane and Arakawa B cross grid in horizontal plane, and the multi-nest grid scheme could A01-1

2 simulate the interaction between various weather owning different scales effectively. For the complex meso-scale weather system, WRF model could make up the space and time resolution deficiency in traditional weather observed tools. Recently, because of improved boundary layer condition, cumulus parameter, and Four-Dimension Assimilation, the WRF model had been used to study and emulate the meso-scale weather system extensively by scientist so far. There are two level in model grid nested scheme, the procedure is so-called two-way interactive method, which could retrieve the fine grid data back to coarse grid data. In order to raise the time and space resolution of model, the grid interval is set from 0.5 to 1.5 km (Fig.1 and Fig.2 shows model coarse and fine grid integral region, respectively) and time interval is set 1 hr to emulate the characteristics of meso- and small-scale weather systems. For model fine grid region, the maximum terrain height in Taiwan area is about 3731 meter, cumulus parameter is set Kain-Fritsch SchemeKain and Fritsch, 1993, then boundary layer is set Yonsei University schemeysu, Hong, and Pan,1996, and micro physics parameter is set WRF Single-Moment 5-class schemewsm5, Hong et al., The total model integral time is 48 hr, and apply the NCEPNational Center for Environmental PredictionGFS AVN model data to be model initial data. 3. VERIFICATION AND EXAMINATION For the purpose of understanding the ability of WRF model emulating various weather system more clearly, we choose weather systems occurred from Oct 2006 to Jan 2007 (there are 20 weather systems cases, 80 samples during four month totally), in which including a variety of weather systems (e.g. front, front pass through Taiwan northern ocean, northeastern monsoon in the winter season, cyclone flow near and outside typhoon, cold surge, continental high pressure, and continental high pressure backflow etc.) and weather phenomenon (e.g. low temperature, small rain, rain, rain shower, sunny, and cloudy etc.), we try to investigate the long-term weather characteristics and organization around and near the Archilife research living center in favor of following verifying and examining the simulated capability of WRF model. Real observed data was adopted from two micro climate observation stations, one constructed at and another constructed near the Archilife research living center, above two stations data was compared each other to get the optimum observed data. Observed data time is 02, 08, 14, and 20 o clock local time, and observed weather factors include temperature, pressure, humidity, precipitation, and wind field etc, beside verifying and examining WRF model, the model initial time is set 12hr early than the observed data time to avoid the problem of model spin up. Figure 3 shows the evolution and variation of temperature factor in this research, in which, the blue line represents the real data recorded by micro climate and weather station, caled observation-1, the red line represents the high-resolution WRF model simulated data, called simulation-1, and the brown one represents the low-resolution WRF model simulated data, caled simulation-2. The research result reveals that the high resolution model data is more sensitive, exact, and much similar to real atmosphere status then low resolution one, no matter in variation magnitude, profile, and amplitude of temperature factor. For simulation-2, on the other hand, appears excess magnitude and exists some phase shift (displacement) then observation-1, especially in sample from 46 to 52 and from 61 to 67. In general, in simulation-1 and simulation-2, there are more or less excess magnitudes A01-2

3 temperature factor (but there are some magnitude shortage samples in simulation-2, that maybe generated by the stable characteristic of wave), especially in simulation-2. As for relative humidity, pressure, and precipitation, the variation tendency of model emulated results are very similar to observed data (figure not shown), that also can illustrate the great ability of WRF model simulated the meso-scale weather system in local area. 4. SIMULATIONAL RESULTS For the purpose of displaying the excellent ability of WRF model simulated the meso-scale weather system around and near the Archilife research living center and providing residents the latest model data in time to satisfy their living needs, so select the cold surge case occurred during 16 th to 17 th Dec Severe continental high pressure moved southward and northeastern monsoon penetrated directly during 16 th to 17 th Dec 2006, because the specific location of the Archilife research living center (windward side for northeastern monsoon), there would be obvious variation in various atmospheric weather factors in this period. Fig.4 shows the variation of temperature factor in all Dec month, there were two low temperature phenomenon appeared in 16 th and17 th Dec which was the minimum th th temperature value in all Dec month(14in 16 and 13.5in 17 Dec, respectively), followed by cold surge weaken and temperature raised. If consider the pressure variation in Fig.5 simultaneously, then the transformation and development procedure of cold surge moving southward can be described expressly. The Severe continental high pressure started to move southward in 14 th Dec, then the strength of continental high pressure arrived strongest in 16 th and 17 th Dec, the center pressure magnitude of continental high pressure is about hPa during this period, followed by strength weaken produced by the characteristic distinction between continent and ocean and far from away the formatted source area of high pressure. Fig.6 shows the wind field pattern, in which reveals when severe continental high pressure moved southward, accompanied by strong wind field, speed from 8 to 10 m/s in average and sustained 36 to 48 (or above) hr. Clearly, this is a traditional severe cold surge case in winter season because of the obvious variation in temperature, pressure, and wind field factors. For precipitation, on the other hand, there was no obvious magnitude observed by research weather station, this maybe caused by a little amount of water vapor in atmosphere and the track of continental high pressure moved southward. The model simulated weather factors were a little bit great than observed one in magnitude, the temperature factor was 13-14in 16 th and 12-13in 17 th Dec in average, this was very similar to real observed data, but there was much variation in wind field, speed about m/s in 16 th, and m/s in 17 th which maybe caused by the simulated continental high pressure burst southward suddenly. Although the simulated wind field was higher than reality data in magnitude, but the variation tendency is very similar each other (Fig.8 shows the relation of temperature factor between model simulated result and observed data, in which, the correlation coefficient is ), this maybe formed by terrain characteristics and geographical features over there, so this problem could be fixed by improving the terrain parameter and tuning the boundary condition of model in the future. 5. DISCUSSION AND CONCLUSION In general, through verified and examined the model simulated results, the ability of A01-3

4 WRF model emulated the micro climate and weather system well can be identified, so the simulated results can be retrieved back to the basic information of micro climate and weather data, then provide the more scientific information to clarify the relation between buildings and indoor, outdoor environments, and supply the important parameters of designing and selecting the material of buildings to fit the needs of residents. As for the problem of model simulated weather factors is excess than real observed data in magnitude, especially in wind field, this maybe caused by terrain characteristics and geographical features over there, and that could be fixed by improving the terrain parameter and tuning the boundary condition of model in the future. REFERENCES [1] Hong,S.-Y.,and H.-L.Pan,1996:Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon.Wea.Rew., 124, [2]Hong,S.-Y.,J.Dudhia and S.-H.Chen,2004:A revised approach to ice microphysical processes for the Bulk parameterization of clouds and precipitation. Mon.Wea.Rew.,132, A01-4

5 Figure 1. Model coarse grid integral region. Figure 2. Model fine grid integral region. A01-5

6 Figure 3.The evolution and variation of temperature factor at the Archilife research living center in Oct 2006 to Jan 2007 research period (the blue line represents the real data observed by micro climate and weather station, the red line represents the high resolution WRF model simulated data, and the brown one represents the low resolution WRF model simulated data.) Figure 4. The variation curve of temperature factor observed by micro climate and weather station at the Archilife research living center in Dec A01-6

7 Figure 5. The variation curve of pressure factor observed by micro climate and weather station at the Archilife research living center in Dec Figure 6. The variation curve of wind field factor observed by micro climate and weather station at the Archilife research living center in Dec A01-7

8 a. b. A01-8

9 c. Figure 7.The WRF model simulated temperature and windfield factors, the data time is (a) 20L 16 th, (b) 08L 17 th, and (c) 20L 17 th Dec, respectively, the model initial time is 08L 16 th Dec Figure 8. The correlation of temperature factor between WRF model stimulated and station observed. A01-9