Effects of the density of meteorological observations on the diagnostic wind fields and the...

Atmospheric Environment 36 (2002) 201–212

Effects of the density of meteorological observations on thediagnostic wind fields and the performance of photochemical

modeling in the greater Seoul area

Jin Young Kim, Young Sung Ghim*

Global Environment Research Center, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang,

Seoul 130-650, South Korea

Received 28 February 2001; received in revised form 13 August 2001; accepted 24 August 2001

Abstract

The high ozone episode in the greater Seoul area (GSA) for the period of 27 July–1 August 1997 was modeled by theCalifornia Institute of Technology (CIT) three-dimensional photochemical model. During the period, ozone

concentrations around 140 ppb were observed for 2 days. Two sets of diagnostic wind fields were constructed byusing observations from the weather stations operated by the Korea Meteorological Administration. One set of windfields utilized only observations from the surface weather stations (SWS) and the other set also utilized observations

from the automatic weather stations (AWS) that were more densely distributed than the SWS. The results showed thatutilizing observations from the AWS could represent fine variations in the wind field such as those caused bytopography. A better wind field gave a more reasonable spatial distribution of ozone concentrations. The modelperformance of ozone prediction was also improved to some extent, but only marginally acceptable owing to large day-

to-day variations. Overshoots of primary pollutants particularly for NO2 were observed as pollutants were accumulatedwhere low wind speeds were maintained. More precise information on diurnal and daily variations in emissions waswarranted in order to better model the photochemical phenomena over the GSA. r 2002 Elsevier Science Ltd. All

rights reserved.

Keywords: Photochemical model; Diagnostic wind field; Local variations; Spatial distribution; Performance evaluation

1. Introduction

The greater Seoul area (GSA), which includes Seoulproper and its neighboring satellite cities, accounts forabout 40% of Korea’s population but o5% of its total

land. Seoul, which has an area of 606 km2, is crowdedwith 10 million people and 2.3 million cars. Further-more, Seoul is surrounded by mountains and hills. There

is a low flat area along the Han River flowing from eastto west through the city as shown in Fig. 1. Annualaverage wind speeds in Seoul are only 2.4m/s with about

5% calm hours, and atmospheric conditions are stableabout 40% of the time (KMA, 1991; Ghim, 1994).

Topography and meteorological conditions as well ashigh emission density within a small area are unfavor-

able for air pollutant dispersion.In the early 1990s, Seoul was notorious for the serious

pollution of sulfur dioxide and suspended particulate

matter. Concentration levels of these pollutants wereconspicuously lowered after the use of clean fuel (low-sulfur oil and liquefied natural gas) had been urged.

However, photochemical ozone has become a stubbornproblem since the hot summer of 1994. In Korea, anozone warning is issued when 1-h ozone levels are

120 ppb or higher, and an ozone alarm occurs when 1-hozone levels are 300 ppb or higher. Since the ozonewarning system was first introduced in Seoul in July1995, the number of ozone warning days has had an

increasing trend: 6 days in 1996, 10 days in 1997, 11 days*Corresponding author. Fax: +82-2-958-5805.

E-mail address: [email protected] (Y.S. Ghim).

1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 4 4 3 - 5

in 1998, and 8 days in 1999 (KME, 2000). A year-to-yearvariation in the number of high ozone days wasconsistent to some extent with the number of high

temperature days above 301C (Ghim et al., 2000).As a result of increased concern about photochemical

ozone, efforts have been made to model ozone episodesin the GSA in order to understand the high ozone

phenomena and finally to develop control strategies (Leeet al., 1998; Kim and Sunwoo, 1998; Kim et al., 1998).These efforts, however, have been seriously hampered

because of incomplete emission inventory and difficultiesin constructing an appropriate wind field. Also, it is

difficult to set boundary conditions because influencesfrom China are usually not quantified. (Note that the

Korean Peninsula is located downwind of the prevailingwesterlies from China as shown in Fig. 1. See Ghim et al.(2001b), for example.) As for emission inventories, those

for criteria pollutants from point sources have becomeapplicable during the past few years. However, formobile sources, traffic data are not common even forarteries, and emissions for volatile organic compounds

(VOC) are only vaguely estimated. Basically, there is noprecise information on temporal variations of emissioninventory.

Difficulties in constructing the wind field are asso-ciated with the characteristics of high ozone occurrencesin the GSA. In fact, the distinct features of photo-

chemical ozone in the GSA are a localization of highozone and disparate ozone patterns among monitoringstations. This is certainly because local effects are

emphasized in association with low wind speeds (Ghimet al., 2001a). So far, a few observation data fromsurface weather stations (SWS) have been used toconstruct the wind field. As shown in Fig. 1, there are

three SWS in the GSA. These stations are appropriatefor monitoring synoptic airflow, but are very few toresolve micrometeorological phenomena, at most, of

several-kilometer scale (Kim et al., 1998). There are,however, 40 automatic weather stations (AWS) in thearea. Recently, Kim et al. (2000a) investigated the

validity of wind observations from AWS for Seoul andInchon in comparison with those from SWS. Theyconcluded that since 1997 wind observations from AWScould be utilized if some anomalies were removed.

In the present research, the effectiveness of utilizationof AWS as well as SWS in constructing the wind fieldand finally in simulating the photochemical phenomena

will be investigated. The California Institute of Technol-ogy (CIT) diagnostic routine (McRae et al., 1992) isemployed in constructing the wind field. This is because

a diagnostic model is more dependent on the inputobserved data and consequently more suitable to test theeffectiveness of utilization of AWS than a prognostic

model. First, wind fields are obtained by using observa-tions from SWS and compared with those obtained byusing observations from AWS as well. Next, the CITEulerian airshed model (McRae et al., 1992) is applied

to simulate the photochemical phenomena. The resultswith the two wind fields are compared and analyzed.

2. Modeling methods

2.1. Target domain and period

Fig. 1 shows the study area of 60� 60 km2, centering

on Seoul. The domain is horizontally divided into a2� 2 km2 regular grid. Vertically, there are five layers to

160 170 180 190 200 210 220

TMX (km) - Easting

414

424

434

444

454

464

474

TM

Y (

km)

- N

orth

ing

Japan

Korea

China

110 120 130 140 150

Longitude (oE)

20

30

40

50

Latit

ude

(oN

)

Fig. 1. Modeling domain and distribution of monitoring

stations. Double rectangles denote surface weather stations

and open triangles denote automatic weather stations; both are

operated by the Korea Meteorological Administration. Solid

circles denote air quality monitoring stations operated by the

Korea Ministry of Environment. Filled contours represent

topography a.s.l. starting from 50m at intervals of 100m.

J.Y. Kim, Y.S. Ghim / Atmospheric Environment 36 (2002) 201–212202

the model top of 1100m. The CIT model uses a terrain-following coordinate system; over the flat terrain at sea

level, the depth is 38m at the lowest level and graduallyincreases to 1100m. In the model, the movement of theinversion layer is simulated implicitly by changes in

the vertical diffusivity rather than movement of themodel top itself (McRae et al., 1992). Along with theaforementioned SWS and AWS, there are 37 air qualitymonitoring stations in the domain. Fig. 2 shows the

variations of ozone concentrations from monitoringstations during the modeling period. The latter six daysfrom the high ozone episode lasting from 22 July to 2

August 1997 were chosen as the modeling period.During the modeling period, the lowest peak concentra-

tion was 91 ppb on 27 July and the highest peakconcentration was 149 ppb on 29 July.High ozone concentrations in Fig. 2 resulted from the

influence of the sweltering North Pacific High expandingto the central part of Korea from the northeast after therainy season ended. During the episode, daily ave-rage wind speeds observed at the Seoul SWS were 1.0–

1.8m/s, which were 0.5–1.2m/s lower than those in anordinary year (KMA, 1991). Between 26 and 27 July, atyphoon passed on the sea between Korea and Japan,

and ozone concentration decreased for a while. Follow-ing the highest peak concentrations on 29 July, ozoneconcentrations substantially dropped on 30 July. At this

time, air mass over the Korean Peninsula was unstableat the edge of the North Pacific High; it showered inthe afternoon. It is surmised that increases of ozone

concentrations on the subsequent days were alsosuppressed as a result of shower (Ghim et al., 2001a).

2.2. Meteorological data

Three-dimensional wind fields were generated diag-nostically by using the observations from SWS or fromboth SWS and AWS along with upper air data. Before

using the data from AWS, the data validation waschecked; the data recovery rate was around 87% as awhole (Kim et al., 2000a). The station density, i.e. the

number of stations per unit area, is 8.3� 10�4 km�2

when using only SWS data and 1.2� 10�2 km�2 whenusing AWS data as well. Therefore, the radius of

influence was set to 90 and 30 km in each case by

22 23 24 25 26 27 28 29 30 31 1 2

July and August 1997

0

40

80

120

160

Ozo

ne (

ppb)

Modeling Period

Fig. 2. Variations of ozone concentrations from the monitoring

stations in the GSA from 22 July to 2 August including the

modeling period. Crosshair denotes the measurement at

individual stations. Horizontally dashed line denotes the 1-h

ozone standard of 100 ppb.

160 170 180 190 200 210 220

TMX (km)

NOx

414

424

434

444

454

464

474

TM

Y (

km)

160 170 180 190 200 210 220

TMX (km)

VOC

414

424

434

444

454

464

474

Fig. 3. Distributions of NOx and VOC emissions. The size of shaded rectangles is proportional to the emission amount. The largest

emission is 39 g/s for NOx and 13 g/s for VOC.

J.Y. Kim, Y.S. Ghim / Atmospheric Environment 36 (2002) 201–212 203

referring to Stephens and Stitt (1970). In order toeliminate the boundary effects during the wind field

estimation, the estimation domain was set larger thanthe study area by 40 km in each direction (Kim et al.,2000b). The sounding data from four upper air stations

distributed over the country were used in constructingthe three-dimensional wind field. The radius of influenceof the upper air data was set to 200 km, sufficiently largeso that the variations in the upper air over the domain

could coincide with that over the country.Other surface meteorological variables, such as

temperature and humidity, were interpolated by using

Barnes (1973) scheme. Clear conditions were assumedfor ultra-violet solar radiation. Mixing heights at thelocations of SWS were determined by using the

hypsometric equation (Holton, 1992) with air tempera-ture and pressure at SWS, and the vertical profile ofpotential temperature obtained from upper air sounding

at the Osan Airforce Base (four times a day, 03:00,09:00, 15:00, and 21:00 LST), located about 40 km southof Seoul. During the modeling period, the mixingheights at 15:00 were 730–1310m while those at 03:00

were 180–670m by stations. The mixing heights at 15:00were used as the maximum heights. However, theminimum mixing heights were assumed to occur at

06:00, just before sunrise. Since sounding data were notavailable at this time, the height of 200m was used as theminimum height by referring to the measurements of

100–300m made in the southeast of the KoreanPeninsula in summer (Min et al., 1999). Spatial andtemporal variations of mixing heights were obtained byinterpolating the mixing heights at SWS.

2.3. Emission data

Fig. 3 shows the distributions of NOx and VOCemissions. All sources including area, line, and pointsources were combined on the 2� 2 km2 grid base. Both

NOx and VOC emissions are high in the downtownSeoul area except some large NOx emission sources inthe industrial area near the west seashore. The distribu-

tion in Fig. 3 is the same as that prepared by NIER(1994) for the year of 1991 (stationary sources) and 1994(mobile sources). Recently, Park and Kim (1999)

indicated that the emission rate of SO2 suggested byNIER (1994) was overestimated by a factor of 2.5although its spatial distribution was plausible. There-fore, in the present study, the total amounts were scaled

by an optimization method (Kuester and Mize, 1973) bycomparing the results from the Empirical KineticModeling Approach (EKMA) model (US EPA, 1989)

with air quality data at the monitoring stations andambient VOC measurements made in August 1997 (Naet al., 1998). As a result, the total amounts used were 0.1

for NOx, 0.5 for VOC and 0.3 for CO of those originallysuggested by NIER (1994). It is thought that these

overestimations could be partly due to the fact that theemission itself had decreased during the past several

years, particularly for CO.Since reliable data for diurnal variations of emissions

are still lacking, the step change in the emission was

assumed: 170% of the hourly average emission accountsfor 07:00–19:00 LST and 30% of the hourly averageemission accounts for the remaining 12 h. A change of150–50% was also tested, but that of 170–30% was

selected because this gave a better result in comparisonof predicted concentrations with the observed ones.

2.4. Initial and boundary conditions

Initial concentration fields were constructed by using

air quality monitoring data along with VOC measure-ments in August 1997 (Na et al., 1998). Simulationstarted from 25 July (Fig. 2); the first 2 days were

devoted to a spin-up period in order to minimize theinfluence of assumed initial conditions. The domain inFig. 1 was determined mainly because comparativelyreliable emission inventory for this area was available.

However, aside from the limitation in handling theregional-scale transport, it comprises a few problems: (1)the densely populated area is extended over the east

boundary; (2) many inhabited islands exist beyond thewest boundary and (3) several monitoring stations forair quality are located near the boundaryFmeasure-

ments from these stations will be used for modelevaluation. To alleviate these problems, boundaryvalues of the concentration fields constructed by the

interpolation of air quality monitoring data were used asthe inflow conditions.

3. Results and discussion

3.1. Wind fields

Fig. 4 shows a comparison of wind speed variationsbetween using only SWS data and using both SWS and

AWS data for constructing the wind field. (Hereafter,the first case will be referred to as ‘‘SWS only’’ and thesecond case as ‘‘SWS+AWS’’.) Although the upper

bounds of SWS only are larger than those ofSWS+AWS on 1 August, they are not much differentfrom each other as a whole. However, the lower boundsof SWS+AWS are generally lower than those of SWS

only, and the average wind speeds of SWS+AWSbecome lower. The main reason for this is that the windspeed observed at AWS is generally lower than that

from SWS. This is because the location of AWS isrelatively susceptible to the influence of obstructionssuch as buildings and trees (Kim et al., 2000a). More-

over, since the number of AWS, 40, is much larger thanthe number of SWS, 3, the wind speed distribution of


SWS+AWS could show a larger variation in space.

Fig. 4 also shows that the temporal variation of SWSonly is larger. This is because the number of SWS issmall and the variation at each station has a larger effect

on the overall variation.Fig. 5 shows wind fields at 02:00 (nighttime), at 08:00

(when the land breeze is dominant), and at 16:00 (when

the sea breeze is dominant) on 28 July. Wind speeds atnight were generally low because turbulence could notdevelop in the stable atmosphere. At 02:00 shown in

Figs. 5(a) and (b), winds are weak on the west coast, andthe average wind speed over the domain is o0.3m/s.Wind speeds seem lower as westerly sea breeze hasdiminished and easterly land breeze only begins to grow.

In spite of low wind speeds over the domain, differencesin wind direction are not small at the lee side of themountain, in the middle of the upper side of the panel,

where only AWS exists (Fig. 1).At 08:00 shown in Figs. 5(c) and (d), the land breezes

blow over the domain since the ground is not yet heated.

While topographic effects such as low wind speedsbehind the mountains are observed in Fig. 5(d) ofSWS+AWS, wind variations centering on SWS aredominant in Fig. 5(c) of SWS only. This indicates that

the wind field is mainly determined by the observationsin the diagnostic routine, although the routine containssome correction procedures for topographic variations.

That is to say, the wind field of SWS+AWS with

topographic variations was obtained because it utilizedthe AWS data that were already influenced by the

topography, while the wind field of SWS only withsimpler variations was obtained because it could notutilize such data.

At 16:00 shown in Figs. 5(e) and (f), the sea breezesprevail over the heated ground. The sea breeze is usuallystronger than the land breeze, because in most cases theland–sea temperature gradient (and hence the pressure

gradient) is larger during the day than at night. InFig. 5(e) of SWS only, strong northwesterly windsdevelop north of the Han River; strong southwesterly

winds develop over the wide area from the southwest tothe south of the domain. On the other hand, in Fig. 5(f)of SWS+AWS, wind speeds are low in the northeast

(due to topographic effects) and just outside the south-west boundary of Seoul. Northwesterly winds towardthe protruded land in the southwest corner are also

observed, which are neglected in the case of SWS only.On the whole, it is understood that the strong sea breezeat 16:00 creates a distinct difference in the wind fieldbetween SWS only and SWS+AWS. It is also confirmed

that the larger portion of low wind speeds ofSWS+AWS in Fig. 4 is due to the topographic effectson the AWS data.

3.2. Prediction of ozone concentrations

It can be inferred that the ozone concentrationdistribution of SWS+AWS shows wider variations thanthat of SWS only, referring to Figs. 4 and 5. Fig. 6 is

similar to Fig. 4, but is for ozone concentrationdistributions at the location of air quality monitoringstations. This is because observed ozone concentrationsare available only at those locations while predicted

values are given at grid points over the domain. Therange of concentration variations of SWS+AWS issomewhat larger than that of SWS only, but far smaller

than that of observations. This could imply that thedifference in wind speed shown in Fig. 4 is not sufficientto generate as large a variation in ozone concentration

distribution of SWS+AWS as that of observations.Fig. 7 shows a correlation between observed and

predicted peak ozone concentrations at the location of

monitoring stations for the modeling period. Thevariation of predicted peak concentrations with respectto the observations is very limited in the case of SWSonly, but larger in the case of SWS+AWS. Never-

theless, the slope of the best-fitted line for SWS+AWSis 0.19, slightly larger than 0.14 for SWS only, althoughboth values are apparently far smaller than 1.0Fperfect

correlation. The limited variations of predicted peakconcentrations in Fig. 7 could be expected to someextent from Fig. 6. Both Figs. 6 and 7 indicate that local

variations of observed concentrations are too large to bereproduced with the present resolution of the modeling.

27 28 29 30 31 10

2

4

6

Win

d S

peed

(m

/s)

SWS Only

27 28 29 30 31 1


0

2

4

6

Win

d S

peed

(m

/s)

SWS+AWS

Fig. 4. Range of wind speed variations represented by upper

and lower bounds at grid points over the domain with time.

Thick line denotes mean wind speed. Upper figure is obtained

by diagnostic analysis of wind data from surface weather

stations (SWS only), and lower figure is obtained from both

surface and automatic weather stations (SWS+AWS).


Fig. 5. Comparison of wind fields obtained by diagnostic analysis of wind data from surface weather stations (SWS only) and from

both surface and automatic weather stations (SWS+AWS) for 28 July 1997.


In order to estimate the spatial variability of observedpeak concentrations, the peak concentration at eachmonitoring station was compared with a weighted

average of peak concentrations from all other stationswithin a radius of 5 km according to McNair et al.(1996). It was revealed that a correlation of peak

concentrations observed at monitoring stations withthose in the nearby area is also low (slope=0.45 andcorrelation coefficient R2 ¼ 0:31), even though some-

what higher than that with predicted peak concentra-tions shown in Fig. 7. This demonstrates that the spatialvariability of ozone concentrations in the GSA is large

even within a radius of 5 km. As was mentioned earlier,the local variability in the GSA could be to some extentdue to low wind speeds (Ghim et al., 2001a). However, itis not certain whether a relatively high density of

monitoring stations shown in Fig. 1 could play a rolein revealing the local variability particularly in associa-tion with the grid resolution in the modeling. Recently,

Russell and Dennis (2000) suggested that the modelperformance be a function of the density of monitoringstations by observing a relatively low performance

reported for the model applications in the Los Angelesarea compared with those in other areas in the US.In Fig. 8, the distribution of predicted peak ozone

concentrations is compared with that of the observedones for 28 JulyFthe wind fields on this day are shownin Fig. 5. Fig. 8(a) is based on observations; (b) and (c)

are from the results of SWS only and SWS+AWS,respectively. As in the case of Fig. 6, predicted values forcontouring are taken from the grid point at the location

of the monitoring stations for comparing the distribu-tions in the same conditions. In the case of observations,shown in Fig. 8(a), concentration is high from the north

of Seoul, including the Uijongbu station, to the south-east of Seoul. Concentration is not so high around

27 28 29 30 31 10

30

60

90

120

150

180

Ozo

ne (

ppb)

Observations

27 28 29 30 31 10

30

60

90

120

150

180

Ozo

ne (

ppb)

SWS Only

27 28 29 30 31 1


0

30

60

90

120

150

180

Ozo

ne (

ppb)

SWS+AWS

Fig. 6. Range of ozone concentration variations represented by

upper and lower bounds with time at the location of air quality

monitoring stations. Thick line denotes mean concentration.

0 40 80 120 160

0

40

80

120

160

Pea

k O

zone

Pre

dict

ed (

ppb)

SWS Only

0 40 80 120 160

Peak Ozone Observed (ppb)

0

40

80

120

160

Pea

k O

zone

Pre

dict

ed (

ppb)

SWS+AWS

Fig. 7. Correlation between observed and predicted peak ozone

concentrations at the location of monitoring stations in the

GSL from 27 July to 1 August 1997.


60–70 ppb along the west coast in the southwest of thedomain. Stations Kuui and Panpo in the east and south

of Seoul recorded the two highest concentrations of 143and 127 ppb, respectively. Three stations, Ssangmun andUijongbu in the northeast of the domain and Songsu on

the side of the Han River, also recorded a highconcentration of 120 ppb.The peak concentration distribution of SWS only,

shown in Fig. 8(b), is similar to that of the observationsto some extent. However, there are several basicdifferences including the following: (1) concentration

to the south of Seoul is lower than that to the west; (2)the spatial variation is relatively simple, particularlywithin Seoul; and (3) the area for concentration above110 ppb is very small. In the distribution of

SWS+AWS, shown in Fig. 8(c), the concentration in

the east of the southern boundary is still lower.However, the patterns, low concentration in the south-

west and high concentration to the north of Seoularound Uijongbu, are closer to those based on theobservation shown in Fig. 8(a).

One reason for differences between the peak concen-tration distributions of SWS only and SWS+AWS canbe found in the difference in wind fields shown in

Figs. 5(e) and (f ). A relatively simple distribution inFig. 8(b) is a result from a relatively uniform wind fieldin Fig. 5(e). Since strong winds in Fig. 5(e) facilitate the

pollutant transport, high concentration clouds movevery fast to the east, and concentration over the domainis lower in Fig. 8(b). On the other hand, as the wind fieldof SWS+AWS reaches low wind speeds especially in the

northeast of the domain, high concentration in the north

Fig. 8. Peak ozone concentration distributions on 28 July 1997. Filled contour represents the interpolated peak concentration based on

observations (a) and predictions (b, c) at the location of air quality monitoring stations. The size of open circle represents concentration

of (a) 49–143 ppb, (b) 67–111 ppb, and (c) 68–119 ppb. The upper right panel shows the location of monitoring stations.


of Seoul including Ssangmun and Uijongbu can appear,which results from pollutant accumulation.

3.3. Performance evaluation

Summarizing the results, SWS+AWS gave better

predictions for ozone concentration than SWS only butstill had difficulty in reproducing the detailed spatialvariations of observations. However, no models are

perfect, and the input data from the real world cannot becomplete. Therefore, what is attempted is to quantita-tively evaluate the current status of model performance.The US Environmental Protection Agency (US EPA,

1991, 1995) recommended the following three measuresfor statistical evaluation of the model performance:

Unpaired peak prediction accuracy

¼Co; peak � Cp; peak

Co; peak� 100%;

Normalized bias ¼1

N

XCoði; jÞ � Cpði; jÞCoði; jÞ

� 100%; ð1Þ

Gross error ¼1

N

Coði; jÞ � Cpði; jÞ��

Coði; jÞ� 100%;

where Cpeak ¼ peak concentration over all hours andmonitoring stations, Cði; jÞ=concentration at station ifor hour j where observed concentration is above

60 ppb, N=number of ðCo;CpÞ pairs, subscript o=ob-served value, and subscript p=predicted value.In US EPA (1991), the predicted peak concentration

denotes the highest concentration among the predictionsat the location of the monitoring station. However, itwas changed to the highest concentration among all thedata over the domain in the amendment in US EPA

(1995). Nevertheless, the original definition was adoptedin the present analysis because it was considered morereasonable unless the distribution of monitoring stations

was sufficiently dense.The US EPA (1991) Guideline states that the

performance evaluation results would be acceptable

when unpaired peak prediction accuracy, normalizedbias, and gross error statistics fall within the range of(7)15–20%, (7)5–15%, and 30–35%, respectively.

Table 1 shows the performance evaluation results ofthe present modeling on a daily basis in terms ofthe statistical measures in Eq. (1). First, for the wholeperiod, the peak prediction accuracy and normalized

bias indicate a better performance for SWS+AWS,while the gross error indicates a slightly better perfor-mance for SWS only. However, daily values are highly

variable so that it cannot be simply concluded that theperformance of SWS+AWS was better even in terms ofpeak prediction accuracy and normalized bias. In the

sense that the peak prediction accuracy and normalizedbias of SWS+AWS are certainly better for the whole

period, it could be said that the modeling withSWS+AWS would give a general picture of the domain

more accurately from a multi-day simulation. Never-theless, high day-to-day variations along with severallarge values exceeding the guideline range suggest that

the current modeling result is marginally acceptable evenin the case of SWS+AWS.It is interesting to note that both peak prediction

accuracy and normalized bias are largely negative on 30

July when it showered in the afternoon. Since all themeteorological observations except precipitation wereused in the present modeling and since the greater parts

of atmospheric chemical and physical changes for ozoneproduction due to precipitation are uncertain (Ghimet al., 2001a), it is not easy to estimate the effects of the

shower on the model performance. Nevertheless, someincreases in positive biases in peak prediction accuracyand normalized bias could be inferred.

3.4. Prediction of other pollutants

In the preceding two sections, the results andperformance of the model were investigated in termsof ozone concentrations. However, countermeasuresagainst high ozone are accomplished by controlling the

photochemical reactants such as nitrogen oxides andVOC. It is apparent that accurate prediction of thesereactants is as important as that of ozone. Furthermore,

many research works have informed us that thepredictability of other reacting components should beexamined in order to confirm the model performance

since ozone concentrations are frequently not sensitiveto important model parameters (Harley et al., 1993;Roth et al., 1998).In Korea, NO2 and CO are routinely measured at

monitoring stations as criteria pollutants. Therefore,observed and predicted concentrations of NO2 werecompared in Fig. 9, similar to Figs. 4 and 6. Although

the upper bounds of NO2 variations of SWS only aregenerally in the same range with those of observedconcentrations, the average concentrations of SWS only

are higher than those of observed concentrationsbecause the lower bounds are higher. Furthermore, thediurnal variations with high concentrations at night are

distinct in the variations of SWS only; this implies thatthe effect of emissions is emphasized. In the variations ofSWS+AWS, the upper bounds are much higher and theaverage concentrations become larger.

CO variations between observed and predicted con-centrations were analogous to NO2 variations. However,the range of predicted concentration variations was

much smaller than that of observed concentrations. As aresult, the CO variations of SWS+AWS seemed close tothose of observed concentrations because they had

higher upper bounds than those of SWS only. Never-theless, the range of CO variations of SWS only was


very small. This could be interpreted to mean that COclouds were thinly covered over the domain withoutappreciation of local variations. This was probably

caused by a rather uniform wind field and higher windspeeds of SWS only, as was mentioned earlier.Unlike the ozone concentrations in Fig. 6, the ranges

of both NO2 and CO variations substantially increase inthe case of SWS+AWS. This is mainly accomplished bythe accumulation of pollutants with low wind speeds at

night. Investigation of the distribution of CO in thenighttime between 30 July and 31 July, when concentra-tions of both NO2 and CO were highest in the case of

SWS+AWS, demonstrated that a large amount of COwas accumulated centering on Songsu (see Fig. 8 for thelocation) with prolonged low wind speeds. In the case ofSWS only, on the other hand, wind speeds increased at

that time, and most of CO on the domain was alreadyswept. Nevertheless, Fig. 9 shows that NO2 concentra-tion overshoots very much and very frequently in the

prediction of SWS+AWS. It is interesting to note thathigh concentrations of reacting components occurredwhere emissions were high (Fig. 3).

As was already indicated, shallow variations of COand partly NO2 (the latter is because higher lowerbounds were more pronounced) as well as ozone are due

to the fact that the current wind field, even with AWS, isnot sufficient to resolve all the local fine variations. Tosome extent, these may be associated with the current2� 2 km2 grid resolution along with emission data of the

same resolution. However, frequent overshoots of NO2

concentrations in the prediction of SWS+AWS demon-strate that a high resolution alone could not guarantee

better results. This probably results from employingthe same emissions without daily variations andsimple diurnal variations of step change. In addition,

accumulation of pollutants at limited areas suggeststhat the emission could not have been properlyestimated at those areas although it was revealed byprolonged low wind speeds. All these warrant that more

precise information on the emission is a prerequisite forbetter modeling the photochemical phenomena over theGSA.

Table 1

Statistical performance measures for ozone concentration prediction of the model (%)

Date Unpaired peak prediction accuracy Normalized biasa Gross errora

SWS only SWS+AWS SWS only SWS+AWS SWS only SWS+AWS

27 July 6.6 4.4 9.9 15.9 14.3 19.9

28 July 22.4 16.8 10.0 2.7 19.1 18.7

29 July 28.2 23.5 19.1 17.4 23.2 23.5

30 July �5.7 �21.9 �4.6 �12.4 21.6 27.3

31 July 0.9 �2.8 17.2 10.3 22.1 25.7

1 August 11.6 �10.7 �3.7 �16.3 14.8 23.7

Total 25.5 14.1 10.5 5.0 20.4 22.8

aFor pairs whose observed concentration is above 60 ppb.

27 28 29 30 31 10

40

80

120

NO

2 (p

pb)

Observations

27 28 29 30 31 10

40

80

120

NO

2 (p

pb)

SWS Only

27 28 29 30 31 1


0

40

80

120

NO

2 (p

pb)

SWS+AWS

Fig. 9. Range of NO2 concentration variations represented by

upper and lower bounds with time at the location of monitoring

stations. Thick line denotes mean concentration. Upper bound

of SWS+AWS goes up to 323ppb, but is clipped at 120 ppb.


4. Conclusions

Since the beginning of the 1990s, high ozone above120 ppb has frequently occurred in the GSA. However,photochemical modeling for investigating the high

ozone phenomena and for devising the control strategiessuffers from many deficiencies. They include incompleteinformation on emissions, sparse measurements ofVOC, difficulties in setting inflow boundary conditions,

and difficulties in constructing appropriate wind fields.In fact, most of these are common to many regionsexcept a few where air quality modeling has been

regularly carried out with well documented meteorolo-gical and air quality data. However, the last one is ratherspecific to the GSA because this is associated with

distinct characteristics of photochemical ozone in thearea such as localization of high ozone and disparateozone patterns among monitoring stations (Ghim et al.,

2001a).There are three SWS in the GSA of 60� 60 km2.

Recently, Kim et al. (2000a) demonstrated through datavalidation procedures that wind observations from 40

AWS in the area could be utilized if some anomalieswere removed. Therefore, diagnostic wind fields wereconstructed by using the observations from SWS only

and those from AWS as well SWS+AWS for the highozone episode from 27 July to 1 August 1997. The tworesults were compared, and their effects on photoche-

mical modeling were analyzed.The wind field of SWS only was uniform and wind

speeds were higher since the wind field was mainlydetermined by the observations from several SWS that

were more appropriate for monitoring a synoptic featureof airflow. On the other hand, the wind field ofSWS+AWS realized fine variations including realistic

topographic effects by using 40 observations distributedover the domain. The distribution of ozone concentra-tion of SWS+AWS was closer to that based on the

observations. However, the predictability of peakconcentration was very limited in both cases and notmuch improved by using the observations from AWS as

well.The ozone prediction performance of the current

modeling in terms of statistical measures suggested bythe US EPA (1991, 1995) was marginally acceptable

even with SWS+AWS. Owing to large day-to-dayvariations in the performance, sometimes exceeding theguideline range recommended by the EPA, it was

considered that the modeling would give a generalpicture more accurately from a multi-day simulation.The model predicted ozone concentrations better where

emissions were high, particularly in the case ofSWS+AWS. However, accumulation of primary pollu-tants associated with prolonged low wind speeds was

observed in this area; this was particularly true for NO2

prediction of SWS+AWS. It was surmised that more

precise information on diurnal and daily variations inemissions could alleviate the problem.

Acknowledgements

This work was supported by the Green Korea 21 ofthe Korea Institute of Science and Technology. Helpfulcomments of anonymous referees are appreciated.

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