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The impact of topography and urban building parameterization on the photochemical ozone concentration of Seoul, Korea
ARTICLE IN PRESS
Atmospheric Environment 42 (2008) 4232–4246 www.elsevier.com/locate/atmosenv
The impact of topography and urban building parameterization on the photochemical ozone concentration of Seoul, Korea Hwa Woon Leea, Hyun-Jung Choia,, Soon-Hwan Leeb, Yoo-Keun Kima, Woo-Sik Jungc a
Division of Earth Environmental System, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Republic of Korea b BK21 Coastal Environment System Research, Pusan National University, Busan, Republic of Korea c Department of Atmospheric Environment Information Engineering, Inje University, Gimhae, Republic of Korea Received 13 July 2007; received in revised form 8 January 2008; accepted 9 January 2008
Abstract Several numerical experiments have been undertaken in order to clarify the impacts of detailed topography and building parameterization on meteorological and photochemical environments. In this study, we carried out a comparative examination on the meteorological fields of topographies that had different resolutions and building information. By analyzing practical urban ground conditions, we revealed that there were large differences in the ozone concentration for each run. The MM5-CMAQ model was used to assess the ozone differences in each case, during the episode day in Seoul, Korea. Meteorological conditions estimated by MM5 command a great influence on the dispersion of air pollutants in complex areas. The reasonable feature of topography that has a high resolution induces a steeper slope in comparison with that of a topography that has a low resolution. Therefore, there is a difference in orographic forcing between the two sets of simulations. This causes a difference in the estimated ozone concentration. A higher ozone concentration tends to be forecasted when using topography data that have a high resolution with an appropriate limitation to the mixing height and the nocturnal boundary layer. The urban buildings parameterization scheme is also strongly associated with the estimation of meteorological and photochemical fields. The length of homogeneous roughness in an urban area corresponds to observations in all other parameterizations of urban buildings in this study. r 2008 Elsevier Ltd. All rights reserved. Keywords: Topography; Urban building; Photochemical modeling; Ozone concentration; Numerical simulation
1. Introduction Terrain features and surface characteristics are the most important elements in meteorological Corresponding author. Tel.: +82 51 583 2651; fax: +82 51 515 1689. E-mail address: [email protected] (H.-J. Choi).
1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.01.021
modeling and air quality modeling. The diurnal evolution of local climates over complex terrains may be significantly controlled by the heterogeneity of surface characteristics, including topography, land-use between urban and rural areas, and anthropogenic heat environments. Orographic forcing is one of the predominant factors that induce and modify mesoscale atmospheric circulations,
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which consequently changes the thermal environment over the regional scale (Atkinson, 1981; Lee and Kimura, 2001; Lee et al., 2007). Furthermore, over a complex terrain, the variations in mesoscale atmospheric circulation often results from the inconsistent distribution of solar radiation, which is highly determined by ground geometrical characteristics, i.e. slope and orientation (Mahrer and Pielke, 1977). A considerable amount of research, through numerical experiments and observations, has been carried out on how orographic forcing occurs and how it causes a variation in the meteorological wind field. (Pielke et al., 1991; Kimura and Kuwagata, 1993; Lee, 1998; Lee et al., 2005). Pielke et al. (1991) and Kimura and Kuwagata (1993) showed that the intensity of the anabatic wind that is induced by the topography mainly depends on the width of the mountains within the topographical area. They also demonstrated that the highest anabatic wind tends to occur over mountains that are about 100 km wide. In addition, Lee and Kimura (2001) compared the diurnal variations of anabatic wind and land– land breeze using a numerical model. They then presented evidence to suggest that orographic forcing is the predominant factor causing the development of mesoscale circulation. They also reported that the impact of orographic forcing increases in a stable atmospheric stability and that the intensity of anabatic wind associated with orographic forcing is stronger during the morning in comparison with land–land breeze. Lee et al. (2005) analyzed the impact of land-use in mountainous areas on the variation of mesoscale circulation. They also reported that, because the orographic effect over steep mountainous areas is predominant, a change in the land-use of a small area does not change the mesoscale circulation or atmospheric photochemical environments. Therefore, in order to increase the accuracy of the meteorological field and the photochemical ozone estimation, the clarification of orographic forcing depending on its spatial resolution is necessary before making an assessment. Orographic forcing consists in natural topography structures as well as in artificial construction in highly urbanized areas such as buildings and construction zones. A road, which has buildings flanking on both sides (termed a ‘‘street canyon’’), forms the basic geometry in addition to the natural topography. Research concerning urban environment assessments has mainly been conducted on a microscopic scale (Baik
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and Kim, 2002; Daul and Pielke, 1993; Eliasson et al., 2006). However, due to the numerous difficulties in the complexity of urban land-use and urban boundary layers, few studies have been carried out on mesoscale urban atmospheric pollution that focuses on the additional orographical height caused by buildings. Many factors such as roughness, heat capacity, conductivity, albedo, evaporation, and anthropogenic heat flux need to be considered in order to estimate and forecast urban air pollution. In particular, orographic related factors must be treated with more caution. In this study, we therefore analyzed the impact of orographic forcing on the ozone concentration over urban metropolitan areas on natural structures and urban artificial buildings. We also carried out several numerical experiments with various parameterization methods for urban buildings. In Section 2 of this paper, the numerical atmospheric and photochemical models are briefly described. Section 3 presents the influence of high-resolution topography, land-use, and urban building parameterization on the evolution of meteorological circulation. The variation of photochemical phenomena depending on the accuracy of surface boundary data is shown in Section 4, while Section 5 outlines the conclusions for the study. 2. Meteorological model description 2.1. Atmospheric model Three-dimensional momentum and thermodynamical equations that are able to describe the atmospheric process are used in this study. In addition, prognostic meteorological fields are generated using the PSU/NCAR mesoscale model (MM5). The PSU/NCAR MM5 model is a nonhydrostatic limited area model. Using finite difference methods, it solves the pressure, three-dimensional momentum, and thermodynamical equations that describe the atmosphere. The equations are integrated by time on an Arakawa-B grid using a second-order leapfrog scheme. Some terms, such as fast moving sound waves, are handled using a timesplitting scheme. Table 1 shows the grid description of the model and its physical parameterizations. The four levels of a two-way nested domain are used with a grid resolution of 27, 9, 3, and 1 km. The center is 381N, 1261E. Vertically it has 33 sigma-layers and the top of the domain is considered to be 15 km above the
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Table 1 The grid system of the horizontal dimensions for model integration and physical parameterizations in Fig. 1 Domain
Number of grid points x
y
1 2 3 4
190 69 75 170
170 75 85 160
Vertical coordinate Upper boundary condition Lateral boundary condition
Terrain following p–s Upper radiative condition Mother domain—relaxation/inflow–outflow Fine domain—time-dependent/nest 2-way nest
Grid size (km)
Time step (s)
27 9 3 1
60 20 6 5
Cloud effects on radiation Moist vertical diffusion in clouds Vertical moisture advection uses log interpolation Advection of temperature uses potential temperature Vertical temperature advection uses theta interpolation
60 4550
• Sanggae Mt. Bukhan •· Shinsul • Shinjung
37.6 4050
50 Latitude
3550 3050
40
• Siheung
2550 2050
30
• Chunho • Daechi Han river
Mt. Kwanak
37.4 1550 1050
20 D1
550 50
80
90
100 110 120 130 140 150 160 170 Longitude
D4 126.6
126.8
127.0
127.2
Fig. 1. The coarse and nested grid domains used in this study.
surface. The coarse and nested grid domains used in this study are shown in Fig. 1. In this study, the turbulent boundary layer is parameterized according to the Hong–Pan scheme (Hong and Pan, 1996), while cloud physics and precipitation processes are parameterized according to Grell et al. (1995) and Reisner2 (Reisner et al., 1998), respectively. Initial and boundary data utilized the three hourly Regional Data Assimilation and Prediction System (RDAPS) provided by the Korea Meteorology Administration (KMA). RDAPS is a grid point value data created through data assimilation with a well-balanced model and observation data.
The target area in this study is the Seoul Metropolitan Area of Korea. Seoul, as one of the largest cities in the world, is often severely confronted with photochemical pollutants. This is due to its location and the presence of dense emissions in the area. Seoul is located in a basin on the west coast of the Korean peninsula and is crossed by a remarkable cluster of hills and mountains, which have a small horizontal scale and form a channel with only one major opening towards the sea to the southwest. A high level of ozone concentration appeared during the 11th and 12th of June 2005. We therefore
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carried out a 5-day analysis and simulation of the ozone concentrations with various topographical and building parameterizations, including the high ozone period. For this period, a weakening of the synoptic wind (geostrophic wind and direction, o5 m s1 and WSW) was observed from the Automatic Weather Station (AWS) sites in a simulated domain. This weakening was induced by the development of a sea/land breeze local circulation system, as well as by an anabatic/catabatic flow from the mountains and valley and a westerly and easterly week wind (Table 2). 2.2. Photochemical model The meteorological results, including the impact of topography and urban building parameterization, were input to Models-3/CMAQ (Byun and Ching, 1999). CMAQ is a numerical model, which gives concentration and deposition values of various air pollutants. This is a next generation modeling system designed to handle research and application issues for multi-scale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particulates) air quality problems. The emission for the background concentration of pollutants used in this modeling was applied from the Aerosol Characterization Experiments (ACE) in Table 2 The frequency distribution (%) of wind direction and speed during the episode days (obtained from 30 AWS sites at Seoul area) Direction
Speed (m s1) o2
2–3
3–4
NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N
3.759 0.453 0.725 1.543 1.223 2.268 1.857 0.996 2.853 2.495 1.042 2.223 2.083 2.540 2.812 0.951
1.449 1.132 2.132 1.498 1.272 1.045 2.174 3.083 2.717 1.993 2.679 1.857 3.216 2.627 2.627 0.408
1.359 0.679 0.861 0.589 0.861 0.589 3.533 0.906 1.721 2.31 0.861 0.679 1.857 1.766 2.129 0.725
Total
29.823
31.909
21.425
4–6 0.147 0.245 0.147 0.343 1.225 0.245 0.735 0.539 0.147 2.647 1.324 2.539 1.686 1.1020 0.588 0.245 13.904
6–10 0.049 0.000 0.098 0.000 0.245 0.049 0.245 0.000 0.552 0.247 0.392 0.049 0.149 0.000 0.224 0.098 2.397
Total 6.763 2.509 3.963 3.973 4.826 4.196 8.544 5.524 7.99 9.692 6.298 7.347 8.991 8.035 8.382 2.427 100
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Asia. ACE-Asia took place during the spring of 2001 off the coasts of China, Japan, and Korea. The ACE-Asia region has an atmosphere with many types of aerosol particles with widely varying compositions and sizes, since the ACE-Asia region is one of the largest sources of aerosol emission regions in the world. The emission source for the fine domain was driven from the Clean Air Policy Support System (CAPSS) of the Korea National Institute of Environmental Research. This is a 1 km 1 km grid that was designated in South Korea during 2001. The CAPSS divides the grid into 10 categories including the source emission of point, line, and area, which are written as ton yr1 (Table 3). The fine domain has a 70 km 55 km grid, using a horizontal resolution of 1 km, 21 layers high with various degrees of thickness. The lowest corresponding height is approximately 25 m above the surface. The respective domain use CB-IV, for the chemical mechanism and simulation period is 120 h. 2.3. Topography and land-use data Two sets of simulations were carried out in this study in order to explain the variation in the numerical simulation of photochemical ozone concentration, which is effected by the difference in spatial resolution of topography data. The first of these simulations has a topography data with a high spatial. The topography data that have a high spatial resolution were derived from the Korea Ministry of Environment Digital Elevation Model (KDEM) and has a grid distance of 90 m. This is a 2-character array, with latitude/longitude data and direct-access. In comparison with the KDEM data, the second topography data are provided by the US Geological Survey Digital Elevation Model (USGSDEM) and has a grid distance of 1.1 km. The high-resolution topographical data need to be converted into suitable formats in order to use the simulation with the various grid sizes from each nested domain. Lee et al. (2005) explained the converted methods, including the interpolation process. The interpolation process is based on the Smolarkiewicz and Grell (1992) scheme. Land-use factors such as roughness, heat capacity, short wave albedo, reflection and absorption of long wave radiation, thermal conductivity, and surface evaporation must be carefully analyzed in urban areas. The moisture availability of the surface determines the potential evaporative cooling of the
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Table 3 The distribution of emission amount (2001CAPSS, ton yr1) Description of pollution source
CO
NOX
SOX
TSP
PM10
VOC
Total emission amount Combustion of energy industry Combustion of non-industry Combustion of manufacturing industry Road movement pollution source Non-road movement pollution source Process of production Waste treatment Conveyance and store of energy Use of organic solvent Nature pollution source
833 931 26 614 56 470 13 074 660 052 46 895 29 155 1671 0 0 0
1 045 334 165 382 84 136 110 313 456 125 162 900 52 735 13 743 0 0 0
526 598 196 604 63 440 125 048 7300 41 537 91 445 1224 0 0 0
1 348 501 10 586 4333 20 091 1 284 551 8665 19 967 308 0 0 0
305 207 7256 2806 13 398 263 364 7630 10 694 59 0 0 0
1 216 227 4217 2709 2263 107 766 16 723 125 726 32 475 26 559 380 779 517 010
Urban Drylnd Crop. Past Irrg. Crop. Past. Mix.Dry/Irrg.C.P Crop./Grs. Mosaic Crop./Wood Mosc Grassland Shrubland
Mix Shrb./Grs. Savanna Decids. Broadlf Decids. Needlf E vergrn. Broadlf Evergrn. Needlf Mixed Forest Water Bodies
Herb. Wetland Wooded Wetland Bar. Sparse Veg Herb. Tundra Wooden Tundra Mixed Tundra Bare Grnd. Tundra Snow or Ice
Fig. 2. The land-use which is derived from the output of TERRAIN program.
surface (latent heat flux), while also influencing the surface albedo. Due to the complexity of the underlying surface, urban boundary layers may exhibit very different wind-temperature field structures when compared with rural areas (Tong et al., 2005). In addition to these surface characteristics, substrate thermal properties can play a significant role in the storage of heat during the day as well as the release of heat at night. Thus accuracy of landuse data is an important factor in the process of estimating meteorological and ozone concentrations in urban areas. Land-use in this study is taken from satellite-based data with a high resolution of 30 m and is provided by the Korea Ministry of Environment (Fig. 2). 2.4. Building height parameterization Although buildings greatly contribute to the modification of the mesoscale wind field, urban buildings are only considered in a few studies concerning mesoscale wind estimation. However, the presence of buildings often modifies the airflow
in surrounding areas and contributes to the orographic forcing, as mentioned above. It is, therefore, necessary to consider the orographic forcing that is caused by urban buildings in order to understand the impact of urban buildings on urban air pollution. Table 4 shows the distribution numbers for buildings and the estimated roughness length of Seoul, provided by the Geographic Information System. In this study, two sets of simulations were carried out for building height in order to improve the estimation accuracy of urban air pollution. One of these simulations uses the average building height method, which adds data directly to the KDEM topography (Di_KDEM) method. The other simulation uses the RG_KDEM method, which estimates building height and converts it into data that represent a type of urban roughness length. The area that is covered by buildings in Seoul comprises approximately 39.9% of the total land area of Seoul (Table 5). Buildings are grouped into land-use categories, including residential, apartment, business, and industrial sections. The roughness length
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Table 4 Distribution number of buildings driven from GIS (Geographic Information System) and roughness length at Seoul Area
Floor 1
A B C D E F G H I J K L M N O P Q R S T U V W X Y Total
Total buildings 2
3
4
5
6
12 621 8475 9035 7067 2981 13 186 6174 18 996 10 439 4050 4360 14 175 8737 9689 3266 6422 6203 2703 12 148 8397 4928 2297 1968 1996 3459
8471 8959 11 426 10 221 13 318 13 241 15 743 15 039 12 912 5736 4070 15 593 12 889 11 186 9073 10 027 9729 9568 12 724 14 104 16 799 5742 5439 8641 9700
3755 3481 3952 4166 5150 5222 4419 5307 3713 2706 2002 5499 4887 5354 3772 4023 3068 2480 4065 3988 5855 3312 4820 4112 3542
2547 2046 1882 2156 3460 2655 2639 3031 2760 2200 1670 4245 2801 3017 2447 3955 2314 1369 2468 2709 3854 2504 3798 3742 2488
1154 1292 896 742 1321 1103 1202 1191 986 809 867 1923 1201 1895 1217 1898 1522 669 1011 1046 1795 2570 3846 4177 1935
361 438 195 129 186 243 130 232 85 123 152 220 264 472 193 296 158 108 179 227 616 993 1563 514 264
183 772
270 350
102 650
68 757
38 268
8341
yy
Avg. building height (m)
Roughness length (m)
25 379 27 825 25 099 26 815 36 259 30 805 44 402 31 245 16 429 14 604 42 160 31 321 32 476 20 904 27 887 23 869 17 267 33 660 31 003 34 679 18 877 23 691 24 449 22 219
4.959 4.529 5.001 5.413 4.526 4.906 4.243 4.437 6.019 7.233 4.552 4.778 5.191 5.986 5.924 5.627 5.089 4.854 4.789 5.479 7.383 8.315 7.090 5.810
1.97 1.807 1.995 2.159 1.805 1.957 1.692 1.770 2.401 2.885 1.816 1.906 2.071 2.388 2.363 2.245 2.030 1.936 1.910 2.186 2.945 3.317 2.823 2.312
692 733
5.461
2.171
69
1
1
2
A: Jongrogu, B: Junggu, C: Yongsangu, D: Sungdonggu, E: Kwangjingu, F: Dongdaemoongu, G: Joongranggu, H: Sungbuckgu, I: Kangbuckgu, J: Dobonggu, K: Rowongu, L: Eungpyunggu, M: Seodaemoongu, N: Mapogu, O: Yangchungu, P: Kangseogu, Q: Gurogu, R: Keumchungu, S: Youngdeungpogu, T: Dongjakgu, U: Kwanakgu, V: Seochogu, W: Kangnamgu, X: Songpagu, and Y: Kangdonggu. Table 5 Land amount in use at Seoul area (source from 2005, Seoul, Department of Building Planning) Category Residence Apartment Business section Industry section Road Forest River Parking place A large scale apartment A large scale building A large scale factory Total
Amount in use (%) 32.217 1.381 2.633 2.827 10.009 32.213 4.040 14.648 0.019 0.001 0.012 100.000
of each region is calculated at an average building height and its proportion to the urban area. The Seoul urban area has updated roughness length
values of 2.1 m, while the default urban roughness length was over four times this measurement. Table 6 shows the four sets of numerical experiments. The simulated meteorological factors and the photochemical ozone levels were compared with the differences in spatial resolution of the topography data. Exp Runs 1 and 2 are calculated with USGSDEM and KDEM topography data, respectively. In order to clarify the impact of building height parameterization, numerical experiments have also been designed with the various building height parameterizations as mentioned above. Runs 3 and 4 are MM5 simulations that have the building height parameterization added directly to the KDEM topography, while also considering roughness length. Because accurate wind and temperature fields are required to produce realistic urban air quality modeling, comparative simulations with various surface conditions have been discussed. Each of the four runs uses the same
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emission information data and also includes an estimation of the photochemical ozone from the CMAQ simulation.
to understand the impact of orographic forcing and urban height parameterization on ozone concentrations. Fig. 3 shows the diurnal variation of daytime mixing heights and the heights of the nocturnal boundary layer (NCB) in Daechi, which is one of the most urbanized areas in the Seoul Metropolitan Area. The mixing height and the NCB indicate atmospheric stability, depending on momentum and heat flux, of the urban surface in both daytime and nighttime periods, respectively. The mixing height in Run 4, using the RG_KDEM method, develops the strongest run with a height reaching to 2400 m. Conversely, the mixing height in Run 1, using the USGSDEM method, is only 1750 m and has an appearance time on the maximum height that is later than that on Run 4. This variation in mixing heights is caused by the different resolutions of topography data. The high spatial resolution of topography data using KDEM tends to increase the slope of the inserted topography to a numerical model. The steep topography in the KDEM leads to an intensification of friction, which should develop vertical mixing during daytime periods. During nighttime periods, since radiation cooling increases with time, the height of NCB also increases with time. The intensity of NCB
3. The result of meteorological modeling Temporal and spatial variations of mixing heights associated with the dispersion of pollutants directly results from vertical momentum and heat fluxes. Because heat and momentum flux are also influenced by the friction caused by the roughness of the surface, the analysis of diurnal variation of atmospheric stability, which includes daytime as well as nighttime periods, is necessary in order
Table 6 Description of MM5 numerical experiment Runs
USGSDEM
1 2
Yes
KDEM
Yes Land-use_updated urban zone Di_KDEM method Yes Yes
2700 2650 2600 2550 2500 2450 2400 2350 2300 2250 2200 2150 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0
Run 1 Run 2 Run 3 Run 4
Height (m)
Mixing Height (m)
3 4
RG_KDEM method
1380 1350 1320 1290 1260 1230 1200 1170 1140 1110 1080 1050 1020 990 960 930 900 870 840 810 780 750 720 690 660 630 600 570 540 510 480 450 420 390 360 330 300 270 240 210 180 150 120 90 60 30
Run 1 Run 2 Run 3 Run 4
0
5
10
15
20
25
30
35
5
10
15
Time (LST)
Fig. 3. Simulated mixing heights (a) and NCB (b) in case study.
20
Time (LST)
25
30
35
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development in Run 4 is stronger than it is in other runs. The difference in maximum height of NCB in Runs 4 and 1 reaches about 200 m at 06:00 LST. This disparity in the NCB heights is also a result of the steepness of topography used in numerical simulations. Steep topography indicates that the strong drainage flow occurs easily during the night. This strong drainage flow along steep slopes accumulates quickly in basin areas and the atmospheric stability over the area stabilizes due to cold air drainage flow.
Fig. 4 shows the hourly mean wind speed distribution in each run during nighttime periods (2005/06/ 11/19:00LST to /12/08:00LST) at Daechi. The presence of a ground-based temperature inversion suppresses convective mixing and ascends the roughness length, the NCB and the friction velocity. The weakened wind speed under 1.5 m s-1 is often generated in Run 4, yet is not generated in Run 1. Therefore, the weakened wind speed during nighttime periods in Run 4 may have a higher concentration of pollutants and precursor in NCB than that in Run 1.
90 120
90 60
3.5
120
2.5
2.5 30
2.0
150
1.5
1.5
1.0
1.0 0.5
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
180
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5 1.0
1.0
1.5
1.5 210
330
2.0
210
2.5 3.0
3.0
120
3.5
300
240
90
90 60
3.5
120
2.5 30
150
30
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
180
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
1.0
1.0
1.5
1.5 330
2.0
210
330
2.0
2.5
2.5
3.0 240
60
3.5 3.0
2.0
210
300 270
2.5
180
3.5
270
3.0 150
330
2.0
2.5 240
30
2.0
0.5 180
60
3.5 3.0
3.0 150
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3.0
3.5
300 270
240
3.5
300 270
Fig. 4. The wind speed distribution in each run for 2005/06/11/1900–/12/08:00LST: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
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Fig. 5 shows the horizontal flow distributions simulated at 2005/06/12/15:00LST on 10 m above the ground. The simulated wind fields have relatively variable flow patterns and several significant features can be identified. The wind pattern in Run 1 is dominated by a strong westerly wind from the coastline and has little confused flow. The air mass stagnates along the mountain valley and ridges. Another interesting feature is the blocking caused by the mountain and more converged flows in Run 2. More complex flow patterns arise in Runs 3 and 4, which are induced from thermal and mechanical flows such as blocking, dividing, and flow meandering. Fig. 6 shows the horizontal temperature distributions simulated at 2005/06/12/ 15:00LST on 10 m above the ground. The variation at 15:00 LST in Runs 1 and 2 are shown to have an appreciable disparity in the region of increased temperature by the respective topography and landuse. There are actually more increased temperatures due to the land and building effect in Runs 3 and 4.
37.7
37.7
37.5
37.5
37.3
37.3 126.5
126.7
126.9
In order to understand the quantitative impact of each run, the statistical root mean square deviation (RMSD) of simulated and observed values have been calculated and are shown in Fig. 7. RMSD is " #1=2 N 1X 2 RMSD ¼ ðPi Oi Þ , N i¼1 where Pi and Oi are simulated and observed data, respectively, for measurement i. N is the total number of data. The skill level of the model is regarded as high if the RMSD is less than the standard deviation of observed data and if the standard deviation for simulated data is comparable with that for observed data. As a result, the accuracy of predicted values have been improved by the following sequential modifications of Run 4: use of reasonable features of topography; updated urban land-use patterns; corrected input of surface roughness length parameter values and suitable limitations of mixing height values.
126.5
127.1
37.7
37.7
37.5
37.5
126.7
126.9
127.1
Reference Vectors
37.3
37.3 126.5
126.7
126.9
127.1
6 126.5
126.7
126.9
127.1
Fig. 5. The horizontal flow distributions simulated at 2005/06/12/15:00LST on 10 m above the ground: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
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37.7
37.7
37.5
37.5
37.3
37.3 126.5
126.7
126.9
127.1
126.5
126.7
126.9
4241
127.1
34.5 37.7
37.7
33 31.5 30
37.5
28.5
37.5
27 25.5
37.3
24
37.3 126.5
126.7
126.9
22.5 21
127.1
126.5
126.7
126.9
127.1
Fig. 6. The horizontal temperature simulated at 2005/06/12/15:00LST on 10 m above the ground: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
2.0 1.6 1.4 1.2 1.0 0.8 0.6 0.4
Run 1 Run 2 Run 3 Run 4
2.0 RMSD value
RMSD value
2.5
Run 1 Run 2 Run 3 Run 4
1.8
1.5 1.0 0.5
0.2
Observation Sites
ne un g C hu nh o Sa ng ga e D ae ch i Si ns ul
un g
Si
in j Sh
Si
in j Sh
ne un g C hu nh o Sa ng ga e D ae ch i Si ns ul
0.0 un g
0.0
Observation Sites
Fig. 7. Horizontal wind speed (a) and temperature (b) statistics calculated for 6 stations.
4. Impact of meteorological conditions on photochemical modeling During the period of the CMAQ case study (9/6/ 2005–14/6/2005), there was a relatively high level of
ozone concentration at the monitoring sites. Fig. 8 presents the time variation of horizontal distribution of the observed hourly ozone concentration at air quality stations and the simulated ozone concentration of Run 4. Circles and contours in Fig. 8
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126.6
126.8 127 Longitude
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Fig. 8. Horizontal distributions of ozone concentration simulated by Run 4 and observed ozone concentration at air quality stations (K).
indicated the intensity in observed and simulated ozone concentrations. The ozone density over the Seoul downtown area is low at 12:00LST. However, the ozone concentration dramatically increases at 13:00LST. The maximum value of ozone density reaches 130 ppb, which is widely observed. This is caused by the increase of the photochemical reaction due to strong solar radiation. It should also be noted that a high ozone concentration mainly occurs around the southwestern part of the Seoul Metropolitan Area near the emission sources of the pollutants. Over time, the density of ozone tends to reduce slightly from 13:00LST. Although ozone concentration over the southwestern part of the Seoul Metropolitan Area decreases in comparison with the distribution of ozone at 14:00LST, the ozone density in the eastern part of Seoul noticeably increases at 15:00LST. Simulated values of ozone concentration show the same pattern. The change of the location of maximum ozone concentration causes the advection of ozone precursors and of the ozone itself due to mesoscale circulations. The
sea breeze, which occurs around west coastal areas, often flows from the east inland through to the downtown area Seoul Metropolitan Area. This sea breeze, which delivers a high ozone density, appears near emission sources, on the down wind side of the eastern part of Seoul. In order to more precisely compare the simulated and observed ozone concentrations in each run, the time variation of ozone concentration at six monitoring sites is shown in Fig. 1. Fig. 9 shows the diurnal variations of simulated and observed ozone concentrations in each region. The shading in this figure represents the observational ozone concentration. Although the variation patterns in all runs are similar, their quantitative densities do not correspond. Specifically, the ozone concentration at all sites in Run 1 is under-estimated in comparison to observed levels. However, in Runs 2, 3, and 4, the estimated ozone concentrations come close to the observed levels. The episode day peak is clearly seen by taking into account the respective topography, land-use, and building height effect.
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Fig. 9. Diurnal variations of simulated and observed ozone concentrations in each region: (a) Shinjung, (b) Siheung, (c) Chunho, (d) Sanggae, (e) Daechi, and (f) Sinsul.
The variation in station peak prediction accuracies between Run 1 and Runs 2, 3, and 4 is about 20 ppb. The values in Run 4 seem to be the largest and show the most similarity to the observation data. In order to quantitatively compare the impact of urban parameterization on the estimation of ozone concentration, the statistical RMSD of simulated and observed ozone concentrations have been calculated. The EPA recommends that the average bias be within 15–20%. Thus, the predictions and observations are matched in space and time. Fig. 10 shows estimated RMSD values of ozone concentration in Runs 2, 3, and 4, at six monitoring sites with 2-h intervals. The accuracy for the prediction of the ozone concentrations has been improved by applying the following sequential modifications to Run 4: the use of a reasonable feature of topography; updated urban land-use patterns; corrected input of surface roughness; length parameter values, and an appropriate limitation of mixing height values. Fig. 11 shows the difference in the simulated ozone concentration between Runs 1 and 4 from midnight to early morning (06/12/01LST–09LST). In this figure, the difference in the ozone concentration between these two Runs has the most remark-
able contrast. At the above times, the stable boundary in Run 4 is strengthened due to the weakened wind speed. The precursor and residual ozone concentration in Run 4 became more trapped (15–20 ppb) than it did in Run 1. These layers were emitted to the basin area during the day.
5. Conclusion Due to the complexity of the underlying surface, urban boundary layers may exhibit very different wind–temperature field structures when compared with those in rural areas. It was expected that the meteorological factors derived from the respective topography, land-use, and buildings should be able to predict different flow fields. This difference could clearly affect the diffusion intensity of pollutants in complex areas. The question is how much of this area is under stagnation. In this paper, we examined a comparative study of topography on meteorological fields with different resolutions and building information. By analyzing practical urban ground conditions, we uncovered large differences in the ozone concentration between each run. The MM5-CMAQ model
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3
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5
6 7 8 Time (hr)
9
10 11 12
1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
Fig. 10. RMSD analysis between simulated and observed ozone concentrations in each Run case: (a) Shinjung, (b) Siheung, (c) Chunho, (d) Sanggae, (e) Daechi, and (f) Sinsul.
was used in order to assess the ozone differences in each case during the episode day in Seoul, Korea. The MM5 apparently includes some forcing effects caused by the topography, and the difference of topography is important as it impacts the determination of the rate of convective heating/ cooling of the surface. In addition, we analyzed the impact that orographic forcing has on ozone
concentrations over natural structures and manmade buildings in the urban metropolitan area. We also carried out several numerical experiments on urban buildings using various parameterization methods. Two different parameterizations of building height were used in this study in order to improve the estimation accuracy of urban air pollution. The first parameterization uses the
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precursor and residual ozone concentration in Run 4 became more trapped (15–20 ppb) than it did in Run 1. These layers were emitted to the basin area during the day.
Shinsul Shinjung Chunho Sanggae Daechi Siheung
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Acknowledgment
15
This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2205.
10 5 0
1
2
3
4 5 6 Time (hr)
Fig. 11. The difference (DRun 4Run concentration between Runs 1 and 4.
1)
7
8
9
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of simulated ozone
average building height, which contributes directly to the KDEM topography method (Di_KDEM). The second parameterization uses the RG_KDEM method, which estimates building height and converts it into data that represent a type of urban roughness length. The mixing height and the NCB indicate the atmospheric stability depending on momentum and heat flux from the urban surface areas during daytime and nighttime periods. This difference in mixing height results from the different resolutions of topography data. KDEM high spatial resolution topography data tend to increase the slope of the inserted topography to a numerical model. Steep topography in the KDEM method induces an incensement of friction, which should develop vertical mixing during daytime periods. Because radiation cooling increases with time, the height of the NCB during nighttime periods also increases in acceleration over time. The intensity of NCB development in Run 4 is stronger than it is in other runs. Steep topography indicates that strong drainage flow occurs easily during nighttime periods. This strong drainage flow along steep slopes can accumulate quickly on basin areas and atmospheric stability over the area stabilizes due to a cold air drainage flow. The presence of a ground-based temperature inversion suppresses convective mixing and ascends roughness length, NCB and friction velocity. In contrast to Run 1, the weakened wind speed under 1.5 m s1 is often generated in Run 4. Therefore, in Run 4, the weakened wind speed at night may keep a higher concentration of pollutants and precursors in the NCB than in Run 1. The
References Atkinson, B.W., 1981. Meso-scale Atmospheric Circulations. Academic Press, London, 495pp. Baik, J.-J., Kim, J.-J., 2002. On the escape of pollutants from urban street canyons. Atmospheric Environment 36, 527–536. Byun, D.W., Ching, J.K.S., 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system. EPA Report No. EPA-600/R-99/030, Office of Research and Development, US Environmental Protection Agency, Washington, DC. CARB, 2003. Daul, G.A., Pielke, R.A., 1993. Vertical heat fluxes generated by mesoscale atmospheric flow induced by thermal inhomogeneities in PBL. Journal of Atmospheric Science 50, 919–928. Eliasson, I., Offerle, B., Grimmond, C.S.B., Lindqvist, S., 2006. Wind fields and turbulence statistics in an urban street canyon. Atmospheric Environment 40, 1–16. Grell, G.A., Dudhia, J., Stauffer, D.R., 1995. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR, National Center for Atmospheric Research, Boulder, CO, p. 107. Hong, S.Y., Pan, H.L., 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Monthly Weather Review 124, 2322–2339. Kimura, F., Kuwagata, T., 1993. Thermally induced wind passing from plain to basin over a mountain range. Journal of Applied Meteorology 23, 1538–1547. Lee, S.H., 1998. Numerical studies on the impacts of topography and heterogeneous surface moisture estimated by satellite data on the local circulation. Ph.D. Thesis, University of Tsukuba. Lee, S.-H., Kimura, F., 2001. Comparative studies in the local circulation induced by land-use and by topography. Boundary Layer Meteorology 101, 157–182. Lee, H.W., Choi, H.-J., Lee, K-Y., 2005. The effect of the detailed bottom boundary condition and numerical interpolation on the simulation of the air flow field with complex topography. Journal of Korean Meteorology 41 (1), 73–87. Lee, S.-H., Kim, Y.-K., Kim, H.-S., Lee, H.-W., 2007. Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula. Atmospheric Environment 41 (21), 4451–4465. Mahrer, Y., Pielke, R.A., 1977. The effects of topography on sea and land breezes in a two-dimensional numerical model. Monthly Weather Review 105, 1151–1162.
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Pielke, R.A., Dalu, G.A., Snook, J.S., Lee, T.J., Kittle, T., 1991. Nonlinear influence of mesoscale land use on weather and climate. Journal of Climate 4, 1053–1069. Reisner, J., Rassmussen, R.J., Bruintjes, R.T., 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quarterly Journal of the Royal Meteorological Society 124B, 1071–1107.
Smolarkiewicz, P.K., Grell, G.A., 1992. A class of monotone interpolation schemes. Journal of Computational Physics 101, 431–440. Tong, H., Walton, A., Sang, J., Chan, J.C.L., 2005. Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong. Journal of Atmospheric Environment 39, 3549–3563.
Atmospheric Environment 42 (2008) 4232–4246 www.elsevier.com/locate/atmosenv
The impact of topography and urban building parameterization on the photochemical ozone concentration of Seoul, Korea Hwa Woon Leea, Hyun-Jung Choia,, Soon-Hwan Leeb, Yoo-Keun Kima, Woo-Sik Jungc a
Division of Earth Environmental System, Pusan National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Republic of Korea b BK21 Coastal Environment System Research, Pusan National University, Busan, Republic of Korea c Department of Atmospheric Environment Information Engineering, Inje University, Gimhae, Republic of Korea Received 13 July 2007; received in revised form 8 January 2008; accepted 9 January 2008
Abstract Several numerical experiments have been undertaken in order to clarify the impacts of detailed topography and building parameterization on meteorological and photochemical environments. In this study, we carried out a comparative examination on the meteorological fields of topographies that had different resolutions and building information. By analyzing practical urban ground conditions, we revealed that there were large differences in the ozone concentration for each run. The MM5-CMAQ model was used to assess the ozone differences in each case, during the episode day in Seoul, Korea. Meteorological conditions estimated by MM5 command a great influence on the dispersion of air pollutants in complex areas. The reasonable feature of topography that has a high resolution induces a steeper slope in comparison with that of a topography that has a low resolution. Therefore, there is a difference in orographic forcing between the two sets of simulations. This causes a difference in the estimated ozone concentration. A higher ozone concentration tends to be forecasted when using topography data that have a high resolution with an appropriate limitation to the mixing height and the nocturnal boundary layer. The urban buildings parameterization scheme is also strongly associated with the estimation of meteorological and photochemical fields. The length of homogeneous roughness in an urban area corresponds to observations in all other parameterizations of urban buildings in this study. r 2008 Elsevier Ltd. All rights reserved. Keywords: Topography; Urban building; Photochemical modeling; Ozone concentration; Numerical simulation
1. Introduction Terrain features and surface characteristics are the most important elements in meteorological Corresponding author. Tel.: +82 51 583 2651; fax: +82 51 515 1689. E-mail address: [email protected] (H.-J. Choi).
1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.01.021
modeling and air quality modeling. The diurnal evolution of local climates over complex terrains may be significantly controlled by the heterogeneity of surface characteristics, including topography, land-use between urban and rural areas, and anthropogenic heat environments. Orographic forcing is one of the predominant factors that induce and modify mesoscale atmospheric circulations,
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which consequently changes the thermal environment over the regional scale (Atkinson, 1981; Lee and Kimura, 2001; Lee et al., 2007). Furthermore, over a complex terrain, the variations in mesoscale atmospheric circulation often results from the inconsistent distribution of solar radiation, which is highly determined by ground geometrical characteristics, i.e. slope and orientation (Mahrer and Pielke, 1977). A considerable amount of research, through numerical experiments and observations, has been carried out on how orographic forcing occurs and how it causes a variation in the meteorological wind field. (Pielke et al., 1991; Kimura and Kuwagata, 1993; Lee, 1998; Lee et al., 2005). Pielke et al. (1991) and Kimura and Kuwagata (1993) showed that the intensity of the anabatic wind that is induced by the topography mainly depends on the width of the mountains within the topographical area. They also demonstrated that the highest anabatic wind tends to occur over mountains that are about 100 km wide. In addition, Lee and Kimura (2001) compared the diurnal variations of anabatic wind and land– land breeze using a numerical model. They then presented evidence to suggest that orographic forcing is the predominant factor causing the development of mesoscale circulation. They also reported that the impact of orographic forcing increases in a stable atmospheric stability and that the intensity of anabatic wind associated with orographic forcing is stronger during the morning in comparison with land–land breeze. Lee et al. (2005) analyzed the impact of land-use in mountainous areas on the variation of mesoscale circulation. They also reported that, because the orographic effect over steep mountainous areas is predominant, a change in the land-use of a small area does not change the mesoscale circulation or atmospheric photochemical environments. Therefore, in order to increase the accuracy of the meteorological field and the photochemical ozone estimation, the clarification of orographic forcing depending on its spatial resolution is necessary before making an assessment. Orographic forcing consists in natural topography structures as well as in artificial construction in highly urbanized areas such as buildings and construction zones. A road, which has buildings flanking on both sides (termed a ‘‘street canyon’’), forms the basic geometry in addition to the natural topography. Research concerning urban environment assessments has mainly been conducted on a microscopic scale (Baik
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and Kim, 2002; Daul and Pielke, 1993; Eliasson et al., 2006). However, due to the numerous difficulties in the complexity of urban land-use and urban boundary layers, few studies have been carried out on mesoscale urban atmospheric pollution that focuses on the additional orographical height caused by buildings. Many factors such as roughness, heat capacity, conductivity, albedo, evaporation, and anthropogenic heat flux need to be considered in order to estimate and forecast urban air pollution. In particular, orographic related factors must be treated with more caution. In this study, we therefore analyzed the impact of orographic forcing on the ozone concentration over urban metropolitan areas on natural structures and urban artificial buildings. We also carried out several numerical experiments with various parameterization methods for urban buildings. In Section 2 of this paper, the numerical atmospheric and photochemical models are briefly described. Section 3 presents the influence of high-resolution topography, land-use, and urban building parameterization on the evolution of meteorological circulation. The variation of photochemical phenomena depending on the accuracy of surface boundary data is shown in Section 4, while Section 5 outlines the conclusions for the study. 2. Meteorological model description 2.1. Atmospheric model Three-dimensional momentum and thermodynamical equations that are able to describe the atmospheric process are used in this study. In addition, prognostic meteorological fields are generated using the PSU/NCAR mesoscale model (MM5). The PSU/NCAR MM5 model is a nonhydrostatic limited area model. Using finite difference methods, it solves the pressure, three-dimensional momentum, and thermodynamical equations that describe the atmosphere. The equations are integrated by time on an Arakawa-B grid using a second-order leapfrog scheme. Some terms, such as fast moving sound waves, are handled using a timesplitting scheme. Table 1 shows the grid description of the model and its physical parameterizations. The four levels of a two-way nested domain are used with a grid resolution of 27, 9, 3, and 1 km. The center is 381N, 1261E. Vertically it has 33 sigma-layers and the top of the domain is considered to be 15 km above the
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Table 1 The grid system of the horizontal dimensions for model integration and physical parameterizations in Fig. 1 Domain
Number of grid points x
y
1 2 3 4
190 69 75 170
170 75 85 160
Vertical coordinate Upper boundary condition Lateral boundary condition
Terrain following p–s Upper radiative condition Mother domain—relaxation/inflow–outflow Fine domain—time-dependent/nest 2-way nest
Grid size (km)
Time step (s)
27 9 3 1
60 20 6 5
Cloud effects on radiation Moist vertical diffusion in clouds Vertical moisture advection uses log interpolation Advection of temperature uses potential temperature Vertical temperature advection uses theta interpolation
60 4550
• Sanggae Mt. Bukhan •· Shinsul • Shinjung
37.6 4050
50 Latitude
3550 3050
40
• Siheung
2550 2050
30
• Chunho • Daechi Han river
Mt. Kwanak
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20 D1
550 50
80
90
100 110 120 130 140 150 160 170 Longitude
D4 126.6
126.8
127.0
127.2
Fig. 1. The coarse and nested grid domains used in this study.
surface. The coarse and nested grid domains used in this study are shown in Fig. 1. In this study, the turbulent boundary layer is parameterized according to the Hong–Pan scheme (Hong and Pan, 1996), while cloud physics and precipitation processes are parameterized according to Grell et al. (1995) and Reisner2 (Reisner et al., 1998), respectively. Initial and boundary data utilized the three hourly Regional Data Assimilation and Prediction System (RDAPS) provided by the Korea Meteorology Administration (KMA). RDAPS is a grid point value data created through data assimilation with a well-balanced model and observation data.
The target area in this study is the Seoul Metropolitan Area of Korea. Seoul, as one of the largest cities in the world, is often severely confronted with photochemical pollutants. This is due to its location and the presence of dense emissions in the area. Seoul is located in a basin on the west coast of the Korean peninsula and is crossed by a remarkable cluster of hills and mountains, which have a small horizontal scale and form a channel with only one major opening towards the sea to the southwest. A high level of ozone concentration appeared during the 11th and 12th of June 2005. We therefore
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carried out a 5-day analysis and simulation of the ozone concentrations with various topographical and building parameterizations, including the high ozone period. For this period, a weakening of the synoptic wind (geostrophic wind and direction, o5 m s1 and WSW) was observed from the Automatic Weather Station (AWS) sites in a simulated domain. This weakening was induced by the development of a sea/land breeze local circulation system, as well as by an anabatic/catabatic flow from the mountains and valley and a westerly and easterly week wind (Table 2). 2.2. Photochemical model The meteorological results, including the impact of topography and urban building parameterization, were input to Models-3/CMAQ (Byun and Ching, 1999). CMAQ is a numerical model, which gives concentration and deposition values of various air pollutants. This is a next generation modeling system designed to handle research and application issues for multi-scale (urban and regional) and multi-pollutant (oxidants, acid deposition, and particulates) air quality problems. The emission for the background concentration of pollutants used in this modeling was applied from the Aerosol Characterization Experiments (ACE) in Table 2 The frequency distribution (%) of wind direction and speed during the episode days (obtained from 30 AWS sites at Seoul area) Direction
Speed (m s1) o2
2–3
3–4
NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW N
3.759 0.453 0.725 1.543 1.223 2.268 1.857 0.996 2.853 2.495 1.042 2.223 2.083 2.540 2.812 0.951
1.449 1.132 2.132 1.498 1.272 1.045 2.174 3.083 2.717 1.993 2.679 1.857 3.216 2.627 2.627 0.408
1.359 0.679 0.861 0.589 0.861 0.589 3.533 0.906 1.721 2.31 0.861 0.679 1.857 1.766 2.129 0.725
Total
29.823
31.909
21.425
4–6 0.147 0.245 0.147 0.343 1.225 0.245 0.735 0.539 0.147 2.647 1.324 2.539 1.686 1.1020 0.588 0.245 13.904
6–10 0.049 0.000 0.098 0.000 0.245 0.049 0.245 0.000 0.552 0.247 0.392 0.049 0.149 0.000 0.224 0.098 2.397
Total 6.763 2.509 3.963 3.973 4.826 4.196 8.544 5.524 7.99 9.692 6.298 7.347 8.991 8.035 8.382 2.427 100
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Asia. ACE-Asia took place during the spring of 2001 off the coasts of China, Japan, and Korea. The ACE-Asia region has an atmosphere with many types of aerosol particles with widely varying compositions and sizes, since the ACE-Asia region is one of the largest sources of aerosol emission regions in the world. The emission source for the fine domain was driven from the Clean Air Policy Support System (CAPSS) of the Korea National Institute of Environmental Research. This is a 1 km 1 km grid that was designated in South Korea during 2001. The CAPSS divides the grid into 10 categories including the source emission of point, line, and area, which are written as ton yr1 (Table 3). The fine domain has a 70 km 55 km grid, using a horizontal resolution of 1 km, 21 layers high with various degrees of thickness. The lowest corresponding height is approximately 25 m above the surface. The respective domain use CB-IV, for the chemical mechanism and simulation period is 120 h. 2.3. Topography and land-use data Two sets of simulations were carried out in this study in order to explain the variation in the numerical simulation of photochemical ozone concentration, which is effected by the difference in spatial resolution of topography data. The first of these simulations has a topography data with a high spatial. The topography data that have a high spatial resolution were derived from the Korea Ministry of Environment Digital Elevation Model (KDEM) and has a grid distance of 90 m. This is a 2-character array, with latitude/longitude data and direct-access. In comparison with the KDEM data, the second topography data are provided by the US Geological Survey Digital Elevation Model (USGSDEM) and has a grid distance of 1.1 km. The high-resolution topographical data need to be converted into suitable formats in order to use the simulation with the various grid sizes from each nested domain. Lee et al. (2005) explained the converted methods, including the interpolation process. The interpolation process is based on the Smolarkiewicz and Grell (1992) scheme. Land-use factors such as roughness, heat capacity, short wave albedo, reflection and absorption of long wave radiation, thermal conductivity, and surface evaporation must be carefully analyzed in urban areas. The moisture availability of the surface determines the potential evaporative cooling of the
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Table 3 The distribution of emission amount (2001CAPSS, ton yr1) Description of pollution source
CO
NOX
SOX
TSP
PM10
VOC
Total emission amount Combustion of energy industry Combustion of non-industry Combustion of manufacturing industry Road movement pollution source Non-road movement pollution source Process of production Waste treatment Conveyance and store of energy Use of organic solvent Nature pollution source
833 931 26 614 56 470 13 074 660 052 46 895 29 155 1671 0 0 0
1 045 334 165 382 84 136 110 313 456 125 162 900 52 735 13 743 0 0 0
526 598 196 604 63 440 125 048 7300 41 537 91 445 1224 0 0 0
1 348 501 10 586 4333 20 091 1 284 551 8665 19 967 308 0 0 0
305 207 7256 2806 13 398 263 364 7630 10 694 59 0 0 0
1 216 227 4217 2709 2263 107 766 16 723 125 726 32 475 26 559 380 779 517 010
Urban Drylnd Crop. Past Irrg. Crop. Past. Mix.Dry/Irrg.C.P Crop./Grs. Mosaic Crop./Wood Mosc Grassland Shrubland
Mix Shrb./Grs. Savanna Decids. Broadlf Decids. Needlf E vergrn. Broadlf Evergrn. Needlf Mixed Forest Water Bodies
Herb. Wetland Wooded Wetland Bar. Sparse Veg Herb. Tundra Wooden Tundra Mixed Tundra Bare Grnd. Tundra Snow or Ice
Fig. 2. The land-use which is derived from the output of TERRAIN program.
surface (latent heat flux), while also influencing the surface albedo. Due to the complexity of the underlying surface, urban boundary layers may exhibit very different wind-temperature field structures when compared with rural areas (Tong et al., 2005). In addition to these surface characteristics, substrate thermal properties can play a significant role in the storage of heat during the day as well as the release of heat at night. Thus accuracy of landuse data is an important factor in the process of estimating meteorological and ozone concentrations in urban areas. Land-use in this study is taken from satellite-based data with a high resolution of 30 m and is provided by the Korea Ministry of Environment (Fig. 2). 2.4. Building height parameterization Although buildings greatly contribute to the modification of the mesoscale wind field, urban buildings are only considered in a few studies concerning mesoscale wind estimation. However, the presence of buildings often modifies the airflow
in surrounding areas and contributes to the orographic forcing, as mentioned above. It is, therefore, necessary to consider the orographic forcing that is caused by urban buildings in order to understand the impact of urban buildings on urban air pollution. Table 4 shows the distribution numbers for buildings and the estimated roughness length of Seoul, provided by the Geographic Information System. In this study, two sets of simulations were carried out for building height in order to improve the estimation accuracy of urban air pollution. One of these simulations uses the average building height method, which adds data directly to the KDEM topography (Di_KDEM) method. The other simulation uses the RG_KDEM method, which estimates building height and converts it into data that represent a type of urban roughness length. The area that is covered by buildings in Seoul comprises approximately 39.9% of the total land area of Seoul (Table 5). Buildings are grouped into land-use categories, including residential, apartment, business, and industrial sections. The roughness length
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Table 4 Distribution number of buildings driven from GIS (Geographic Information System) and roughness length at Seoul Area
Floor 1
A B C D E F G H I J K L M N O P Q R S T U V W X Y Total
Total buildings 2
3
4
5
6
12 621 8475 9035 7067 2981 13 186 6174 18 996 10 439 4050 4360 14 175 8737 9689 3266 6422 6203 2703 12 148 8397 4928 2297 1968 1996 3459
8471 8959 11 426 10 221 13 318 13 241 15 743 15 039 12 912 5736 4070 15 593 12 889 11 186 9073 10 027 9729 9568 12 724 14 104 16 799 5742 5439 8641 9700
3755 3481 3952 4166 5150 5222 4419 5307 3713 2706 2002 5499 4887 5354 3772 4023 3068 2480 4065 3988 5855 3312 4820 4112 3542
2547 2046 1882 2156 3460 2655 2639 3031 2760 2200 1670 4245 2801 3017 2447 3955 2314 1369 2468 2709 3854 2504 3798 3742 2488
1154 1292 896 742 1321 1103 1202 1191 986 809 867 1923 1201 1895 1217 1898 1522 669 1011 1046 1795 2570 3846 4177 1935
361 438 195 129 186 243 130 232 85 123 152 220 264 472 193 296 158 108 179 227 616 993 1563 514 264
183 772
270 350
102 650
68 757
38 268
8341
yy
Avg. building height (m)
Roughness length (m)
25 379 27 825 25 099 26 815 36 259 30 805 44 402 31 245 16 429 14 604 42 160 31 321 32 476 20 904 27 887 23 869 17 267 33 660 31 003 34 679 18 877 23 691 24 449 22 219
4.959 4.529 5.001 5.413 4.526 4.906 4.243 4.437 6.019 7.233 4.552 4.778 5.191 5.986 5.924 5.627 5.089 4.854 4.789 5.479 7.383 8.315 7.090 5.810
1.97 1.807 1.995 2.159 1.805 1.957 1.692 1.770 2.401 2.885 1.816 1.906 2.071 2.388 2.363 2.245 2.030 1.936 1.910 2.186 2.945 3.317 2.823 2.312
692 733
5.461
2.171
69
1
1
2
A: Jongrogu, B: Junggu, C: Yongsangu, D: Sungdonggu, E: Kwangjingu, F: Dongdaemoongu, G: Joongranggu, H: Sungbuckgu, I: Kangbuckgu, J: Dobonggu, K: Rowongu, L: Eungpyunggu, M: Seodaemoongu, N: Mapogu, O: Yangchungu, P: Kangseogu, Q: Gurogu, R: Keumchungu, S: Youngdeungpogu, T: Dongjakgu, U: Kwanakgu, V: Seochogu, W: Kangnamgu, X: Songpagu, and Y: Kangdonggu. Table 5 Land amount in use at Seoul area (source from 2005, Seoul, Department of Building Planning) Category Residence Apartment Business section Industry section Road Forest River Parking place A large scale apartment A large scale building A large scale factory Total
Amount in use (%) 32.217 1.381 2.633 2.827 10.009 32.213 4.040 14.648 0.019 0.001 0.012 100.000
of each region is calculated at an average building height and its proportion to the urban area. The Seoul urban area has updated roughness length
values of 2.1 m, while the default urban roughness length was over four times this measurement. Table 6 shows the four sets of numerical experiments. The simulated meteorological factors and the photochemical ozone levels were compared with the differences in spatial resolution of the topography data. Exp Runs 1 and 2 are calculated with USGSDEM and KDEM topography data, respectively. In order to clarify the impact of building height parameterization, numerical experiments have also been designed with the various building height parameterizations as mentioned above. Runs 3 and 4 are MM5 simulations that have the building height parameterization added directly to the KDEM topography, while also considering roughness length. Because accurate wind and temperature fields are required to produce realistic urban air quality modeling, comparative simulations with various surface conditions have been discussed. Each of the four runs uses the same
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emission information data and also includes an estimation of the photochemical ozone from the CMAQ simulation.
to understand the impact of orographic forcing and urban height parameterization on ozone concentrations. Fig. 3 shows the diurnal variation of daytime mixing heights and the heights of the nocturnal boundary layer (NCB) in Daechi, which is one of the most urbanized areas in the Seoul Metropolitan Area. The mixing height and the NCB indicate atmospheric stability, depending on momentum and heat flux, of the urban surface in both daytime and nighttime periods, respectively. The mixing height in Run 4, using the RG_KDEM method, develops the strongest run with a height reaching to 2400 m. Conversely, the mixing height in Run 1, using the USGSDEM method, is only 1750 m and has an appearance time on the maximum height that is later than that on Run 4. This variation in mixing heights is caused by the different resolutions of topography data. The high spatial resolution of topography data using KDEM tends to increase the slope of the inserted topography to a numerical model. The steep topography in the KDEM leads to an intensification of friction, which should develop vertical mixing during daytime periods. During nighttime periods, since radiation cooling increases with time, the height of NCB also increases with time. The intensity of NCB
3. The result of meteorological modeling Temporal and spatial variations of mixing heights associated with the dispersion of pollutants directly results from vertical momentum and heat fluxes. Because heat and momentum flux are also influenced by the friction caused by the roughness of the surface, the analysis of diurnal variation of atmospheric stability, which includes daytime as well as nighttime periods, is necessary in order
Table 6 Description of MM5 numerical experiment Runs
USGSDEM
1 2
Yes
KDEM
Yes Land-use_updated urban zone Di_KDEM method Yes Yes
2700 2650 2600 2550 2500 2450 2400 2350 2300 2250 2200 2150 2100 2050 2000 1950 1900 1850 1800 1750 1700 1650 1600 1550 1500 1450 1400 1350 1300 1250 1200 1150 1100 1050 1000 950 900 850 800 750 700 650 600 550 500 450 400 350 300 250 200 150 100 50 0
Run 1 Run 2 Run 3 Run 4
Height (m)
Mixing Height (m)
3 4
RG_KDEM method
1380 1350 1320 1290 1260 1230 1200 1170 1140 1110 1080 1050 1020 990 960 930 900 870 840 810 780 750 720 690 660 630 600 570 540 510 480 450 420 390 360 330 300 270 240 210 180 150 120 90 60 30
Run 1 Run 2 Run 3 Run 4
0
5
10
15
20
25
30
35
5
10
15
Time (LST)
Fig. 3. Simulated mixing heights (a) and NCB (b) in case study.
20
Time (LST)
25
30
35
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development in Run 4 is stronger than it is in other runs. The difference in maximum height of NCB in Runs 4 and 1 reaches about 200 m at 06:00 LST. This disparity in the NCB heights is also a result of the steepness of topography used in numerical simulations. Steep topography indicates that the strong drainage flow occurs easily during the night. This strong drainage flow along steep slopes accumulates quickly in basin areas and the atmospheric stability over the area stabilizes due to cold air drainage flow.
Fig. 4 shows the hourly mean wind speed distribution in each run during nighttime periods (2005/06/ 11/19:00LST to /12/08:00LST) at Daechi. The presence of a ground-based temperature inversion suppresses convective mixing and ascends the roughness length, the NCB and the friction velocity. The weakened wind speed under 1.5 m s-1 is often generated in Run 4, yet is not generated in Run 1. Therefore, the weakened wind speed during nighttime periods in Run 4 may have a higher concentration of pollutants and precursor in NCB than that in Run 1.
90 120
90 60
3.5
120
2.5
2.5 30
2.0
150
1.5
1.5
1.0
1.0 0.5
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
180
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5 1.0
1.0
1.5
1.5 210
330
2.0
210
2.5 3.0
3.0
120
3.5
300
240
90
90 60
3.5
120
2.5 30
150
30
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
180
0 0.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.5
1.0
1.0
1.5
1.5 330
2.0
210
330
2.0
2.5
2.5
3.0 240
60
3.5 3.0
2.0
210
300 270
2.5
180
3.5
270
3.0 150
330
2.0
2.5 240
30
2.0
0.5 180
60
3.5 3.0
3.0 150
4239
3.0
3.5
300 270
240
3.5
300 270
Fig. 4. The wind speed distribution in each run for 2005/06/11/1900–/12/08:00LST: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
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Fig. 5 shows the horizontal flow distributions simulated at 2005/06/12/15:00LST on 10 m above the ground. The simulated wind fields have relatively variable flow patterns and several significant features can be identified. The wind pattern in Run 1 is dominated by a strong westerly wind from the coastline and has little confused flow. The air mass stagnates along the mountain valley and ridges. Another interesting feature is the blocking caused by the mountain and more converged flows in Run 2. More complex flow patterns arise in Runs 3 and 4, which are induced from thermal and mechanical flows such as blocking, dividing, and flow meandering. Fig. 6 shows the horizontal temperature distributions simulated at 2005/06/12/ 15:00LST on 10 m above the ground. The variation at 15:00 LST in Runs 1 and 2 are shown to have an appreciable disparity in the region of increased temperature by the respective topography and landuse. There are actually more increased temperatures due to the land and building effect in Runs 3 and 4.
37.7
37.7
37.5
37.5
37.3
37.3 126.5
126.7
126.9
In order to understand the quantitative impact of each run, the statistical root mean square deviation (RMSD) of simulated and observed values have been calculated and are shown in Fig. 7. RMSD is " #1=2 N 1X 2 RMSD ¼ ðPi Oi Þ , N i¼1 where Pi and Oi are simulated and observed data, respectively, for measurement i. N is the total number of data. The skill level of the model is regarded as high if the RMSD is less than the standard deviation of observed data and if the standard deviation for simulated data is comparable with that for observed data. As a result, the accuracy of predicted values have been improved by the following sequential modifications of Run 4: use of reasonable features of topography; updated urban land-use patterns; corrected input of surface roughness length parameter values and suitable limitations of mixing height values.
126.5
127.1
37.7
37.7
37.5
37.5
126.7
126.9
127.1
Reference Vectors
37.3
37.3 126.5
126.7
126.9
127.1
6 126.5
126.7
126.9
127.1
Fig. 5. The horizontal flow distributions simulated at 2005/06/12/15:00LST on 10 m above the ground: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
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37.7
37.7
37.5
37.5
37.3
37.3 126.5
126.7
126.9
127.1
126.5
126.7
126.9
4241
127.1
34.5 37.7
37.7
33 31.5 30
37.5
28.5
37.5
27 25.5
37.3
24
37.3 126.5
126.7
126.9
22.5 21
127.1
126.5
126.7
126.9
127.1
Fig. 6. The horizontal temperature simulated at 2005/06/12/15:00LST on 10 m above the ground: (a) Run 1, (b) Run 2, (c) Run 3, and (d) Run 4.
2.0 1.6 1.4 1.2 1.0 0.8 0.6 0.4
Run 1 Run 2 Run 3 Run 4
2.0 RMSD value
RMSD value
2.5
Run 1 Run 2 Run 3 Run 4
1.8
1.5 1.0 0.5
0.2
Observation Sites
ne un g C hu nh o Sa ng ga e D ae ch i Si ns ul
un g
Si
in j Sh
Si
in j Sh
ne un g C hu nh o Sa ng ga e D ae ch i Si ns ul
0.0 un g
0.0
Observation Sites
Fig. 7. Horizontal wind speed (a) and temperature (b) statistics calculated for 6 stations.
4. Impact of meteorological conditions on photochemical modeling During the period of the CMAQ case study (9/6/ 2005–14/6/2005), there was a relatively high level of
ozone concentration at the monitoring sites. Fig. 8 presents the time variation of horizontal distribution of the observed hourly ozone concentration at air quality stations and the simulated ozone concentration of Run 4. Circles and contours in Fig. 8
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2005/06/12/1300LST
2005/06/12/1200LST
Latitude
37.5
63 6457 5671 60 42 7068 69 58 80 68 104 116
37.5
37.3
37.3 126.4
6883 71
37.7 Latitude
2329 0 23 2626 27 21 15 2434 30 22 30 16 23 35 0
37.7
126.6
126.8 127 Longitude
127.2
126.4
126.6
Latitude
37.5
49 6573 111 5777 64 62 110 68 104 113 73 7188
37.5
37.3
37.3 126.4
97110 89
37.7 Latitude
5360 57 44 6995 48484823 5552 10348 53 6968
127.2
2005/06/12/1500LST
2005/06/12/1400LST
37.7
126.8 127 Longitude
126.6
126.8 127 Longitude
127.2
126.4
126.6
126.8 127 Longitude
127.2
Fig. 8. Horizontal distributions of ozone concentration simulated by Run 4 and observed ozone concentration at air quality stations (K).
indicated the intensity in observed and simulated ozone concentrations. The ozone density over the Seoul downtown area is low at 12:00LST. However, the ozone concentration dramatically increases at 13:00LST. The maximum value of ozone density reaches 130 ppb, which is widely observed. This is caused by the increase of the photochemical reaction due to strong solar radiation. It should also be noted that a high ozone concentration mainly occurs around the southwestern part of the Seoul Metropolitan Area near the emission sources of the pollutants. Over time, the density of ozone tends to reduce slightly from 13:00LST. Although ozone concentration over the southwestern part of the Seoul Metropolitan Area decreases in comparison with the distribution of ozone at 14:00LST, the ozone density in the eastern part of Seoul noticeably increases at 15:00LST. Simulated values of ozone concentration show the same pattern. The change of the location of maximum ozone concentration causes the advection of ozone precursors and of the ozone itself due to mesoscale circulations. The
sea breeze, which occurs around west coastal areas, often flows from the east inland through to the downtown area Seoul Metropolitan Area. This sea breeze, which delivers a high ozone density, appears near emission sources, on the down wind side of the eastern part of Seoul. In order to more precisely compare the simulated and observed ozone concentrations in each run, the time variation of ozone concentration at six monitoring sites is shown in Fig. 1. Fig. 9 shows the diurnal variations of simulated and observed ozone concentrations in each region. The shading in this figure represents the observational ozone concentration. Although the variation patterns in all runs are similar, their quantitative densities do not correspond. Specifically, the ozone concentration at all sites in Run 1 is under-estimated in comparison to observed levels. However, in Runs 2, 3, and 4, the estimated ozone concentrations come close to the observed levels. The episode day peak is clearly seen by taking into account the respective topography, land-use, and building height effect.
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90 80 75 70 65 60 55 50 45 40 35 30
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90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Time (hr)
Time (hr)
Time (hr)
90
100
80 75 70 65 60 55 50 45 40
70
90
Ozone concentration (ppb)
85
Ozone concentration (ppb)
Ozone concentration (ppb)
85
Ozone concentration (ppb)
95 90 85 80 75 70 65 60 55 50 45 40 35 30
Ozone concentration (ppb)
Ozone concentration (ppb)
H.W. Lee et al. / Atmospheric Environment 42 (2008) 4232–4246
80 70 60 50 40 30 20
35
65
Obs. Run 1 Run 2 Run 3 Run 4
60 55 50 45 40 35
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Time (hr)
Time (hr)
Time (hr)
Fig. 9. Diurnal variations of simulated and observed ozone concentrations in each region: (a) Shinjung, (b) Siheung, (c) Chunho, (d) Sanggae, (e) Daechi, and (f) Sinsul.
The variation in station peak prediction accuracies between Run 1 and Runs 2, 3, and 4 is about 20 ppb. The values in Run 4 seem to be the largest and show the most similarity to the observation data. In order to quantitatively compare the impact of urban parameterization on the estimation of ozone concentration, the statistical RMSD of simulated and observed ozone concentrations have been calculated. The EPA recommends that the average bias be within 15–20%. Thus, the predictions and observations are matched in space and time. Fig. 10 shows estimated RMSD values of ozone concentration in Runs 2, 3, and 4, at six monitoring sites with 2-h intervals. The accuracy for the prediction of the ozone concentrations has been improved by applying the following sequential modifications to Run 4: the use of a reasonable feature of topography; updated urban land-use patterns; corrected input of surface roughness; length parameter values, and an appropriate limitation of mixing height values. Fig. 11 shows the difference in the simulated ozone concentration between Runs 1 and 4 from midnight to early morning (06/12/01LST–09LST). In this figure, the difference in the ozone concentration between these two Runs has the most remark-
able contrast. At the above times, the stable boundary in Run 4 is strengthened due to the weakened wind speed. The precursor and residual ozone concentration in Run 4 became more trapped (15–20 ppb) than it did in Run 1. These layers were emitted to the basin area during the day.
5. Conclusion Due to the complexity of the underlying surface, urban boundary layers may exhibit very different wind–temperature field structures when compared with those in rural areas. It was expected that the meteorological factors derived from the respective topography, land-use, and buildings should be able to predict different flow fields. This difference could clearly affect the diffusion intensity of pollutants in complex areas. The question is how much of this area is under stagnation. In this paper, we examined a comparative study of topography on meteorological fields with different resolutions and building information. By analyzing practical urban ground conditions, we uncovered large differences in the ozone concentration between each run. The MM5-CMAQ model
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2.5
2.5
2.0
2.0 RMSD value
RMSD value
4244
1.5 1.0 0.5 0.0
1.5 1.0 0.5
1
2
3
4
5
6 7 8 Time (hr)
9
0.0
10 11 12
1.8
1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
2.5
1.6 2.0
1.2
RMSD value
RMSD value
1.4 1.0 0.8 0.6 0.4
1.0 0.5
0.2 0.0
0.0 1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
2.5
2.5
2.0
2.0 RMSD value
RMSD value
1.5
1.5 1.0
Run 2 Run 3 Run 4
1.5 1.0 0.5
0.5
0.0
0.0 1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
1
2
3
4
5
6 7 8 Time (hr)
9
10 11 12
Fig. 10. RMSD analysis between simulated and observed ozone concentrations in each Run case: (a) Shinjung, (b) Siheung, (c) Chunho, (d) Sanggae, (e) Daechi, and (f) Sinsul.
was used in order to assess the ozone differences in each case during the episode day in Seoul, Korea. The MM5 apparently includes some forcing effects caused by the topography, and the difference of topography is important as it impacts the determination of the rate of convective heating/ cooling of the surface. In addition, we analyzed the impact that orographic forcing has on ozone
concentrations over natural structures and manmade buildings in the urban metropolitan area. We also carried out several numerical experiments on urban buildings using various parameterization methods. Two different parameterizations of building height were used in this study in order to improve the estimation accuracy of urban air pollution. The first parameterization uses the
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Ozone difference (ppb)
35
precursor and residual ozone concentration in Run 4 became more trapped (15–20 ppb) than it did in Run 1. These layers were emitted to the basin area during the day.
Shinsul Shinjung Chunho Sanggae Daechi Siheung
30 25
4245
20
Acknowledgment
15
This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006-2205.
10 5 0
1
2
3
4 5 6 Time (hr)
Fig. 11. The difference (DRun 4Run concentration between Runs 1 and 4.
1)
7
8
9
10
of simulated ozone
average building height, which contributes directly to the KDEM topography method (Di_KDEM). The second parameterization uses the RG_KDEM method, which estimates building height and converts it into data that represent a type of urban roughness length. The mixing height and the NCB indicate the atmospheric stability depending on momentum and heat flux from the urban surface areas during daytime and nighttime periods. This difference in mixing height results from the different resolutions of topography data. KDEM high spatial resolution topography data tend to increase the slope of the inserted topography to a numerical model. Steep topography in the KDEM method induces an incensement of friction, which should develop vertical mixing during daytime periods. Because radiation cooling increases with time, the height of the NCB during nighttime periods also increases in acceleration over time. The intensity of NCB development in Run 4 is stronger than it is in other runs. Steep topography indicates that strong drainage flow occurs easily during nighttime periods. This strong drainage flow along steep slopes can accumulate quickly on basin areas and atmospheric stability over the area stabilizes due to a cold air drainage flow. The presence of a ground-based temperature inversion suppresses convective mixing and ascends roughness length, NCB and friction velocity. In contrast to Run 1, the weakened wind speed under 1.5 m s1 is often generated in Run 4. Therefore, in Run 4, the weakened wind speed at night may keep a higher concentration of pollutants and precursors in the NCB than in Run 1. The
References Atkinson, B.W., 1981. Meso-scale Atmospheric Circulations. Academic Press, London, 495pp. Baik, J.-J., Kim, J.-J., 2002. On the escape of pollutants from urban street canyons. Atmospheric Environment 36, 527–536. Byun, D.W., Ching, J.K.S., 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system. EPA Report No. EPA-600/R-99/030, Office of Research and Development, US Environmental Protection Agency, Washington, DC. CARB, 2003. Daul, G.A., Pielke, R.A., 1993. Vertical heat fluxes generated by mesoscale atmospheric flow induced by thermal inhomogeneities in PBL. Journal of Atmospheric Science 50, 919–928. Eliasson, I., Offerle, B., Grimmond, C.S.B., Lindqvist, S., 2006. Wind fields and turbulence statistics in an urban street canyon. Atmospheric Environment 40, 1–16. Grell, G.A., Dudhia, J., Stauffer, D.R., 1995. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR/TN-398+STR, National Center for Atmospheric Research, Boulder, CO, p. 107. Hong, S.Y., Pan, H.L., 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Monthly Weather Review 124, 2322–2339. Kimura, F., Kuwagata, T., 1993. Thermally induced wind passing from plain to basin over a mountain range. Journal of Applied Meteorology 23, 1538–1547. Lee, S.H., 1998. Numerical studies on the impacts of topography and heterogeneous surface moisture estimated by satellite data on the local circulation. Ph.D. Thesis, University of Tsukuba. Lee, S.-H., Kimura, F., 2001. Comparative studies in the local circulation induced by land-use and by topography. Boundary Layer Meteorology 101, 157–182. Lee, H.W., Choi, H.-J., Lee, K-Y., 2005. The effect of the detailed bottom boundary condition and numerical interpolation on the simulation of the air flow field with complex topography. Journal of Korean Meteorology 41 (1), 73–87. Lee, S.-H., Kim, Y.-K., Kim, H.-S., Lee, H.-W., 2007. Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula. Atmospheric Environment 41 (21), 4451–4465. Mahrer, Y., Pielke, R.A., 1977. The effects of topography on sea and land breezes in a two-dimensional numerical model. Monthly Weather Review 105, 1151–1162.
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Pielke, R.A., Dalu, G.A., Snook, J.S., Lee, T.J., Kittle, T., 1991. Nonlinear influence of mesoscale land use on weather and climate. Journal of Climate 4, 1053–1069. Reisner, J., Rassmussen, R.J., Bruintjes, R.T., 1998. Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quarterly Journal of the Royal Meteorological Society 124B, 1071–1107.
Smolarkiewicz, P.K., Grell, G.A., 1992. A class of monotone interpolation schemes. Journal of Computational Physics 101, 431–440. Tong, H., Walton, A., Sang, J., Chan, J.C.L., 2005. Numerical simulation of the urban boundary layer over the complex terrain of Hong Kong. Journal of Atmospheric Environment 39, 3549–3563.