Atmospheric Environment 45 (2011) 6439e6450
Contents lists available at ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Impact of continuously varied SST on land-sea breezes and ozone
concentration over south-western coast of Korea
Soon-Hwan Lee a, Hwa-Woon Lee b, Yoo-Keun Kim b, Won-Bae Jeon b,
Hyun-Jung Choi b, Dong-Hyuk Kim b, *
a
b
Institute for Environmental Studies, Pusan National University, Republic of Korea
Division of Earth Environmental System, Pusan National University, San 30 Jangjeon-Dong Geumjeong-Gu, 609-735 Busan, Republic of Korea
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 19 May 2011
Received in revised form
29 July 2011
Accepted 29 July 2011
Several comparison studies including numerical experiments were carried out at the well urbanized
Gwangyang Bay region, Korea, to clarify the relationship between the continuously varied SST distribution and meteorology and how that impacts the ozone concentration. The numerical models used in
this study were Regional Atmospheric Modeling System (RAMS) and Comprehensive Air quality Model
with eXtensions (CAMx) for meteorological and photochemical ozone fields, respectively.
Based on buoy observations, the sea surface temperature (SST) had a noticeable influence on the near
surface wind field and distribution of photochemical ozone because the mean SST near Gwangyang Bay
changed by 2.8 C over the five day period. Sea breeze with temporally varied SST was better represented
than that estimated without the SST variation. Temporally changed SST distribution and its impact are
more crucial factors for estimating the ozone concentration under weak synoptic conditions. And the
accuracy of the estimated ozone concentration associated with the time varied SST tends to depend on
the distance from the coastline. The acquisition of ocean conditions, including temporal variation in the
SST, is indispensible for assessing and predicting the air quality, especially in well urbanized area near the
coast.
Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
Keywords:
RAMS
CAMx
SST variation
Land-sea breezes
Ozone concentration
South-western coast of Korea
1. Introduction
Local and regional environmental phenomena, including
meteorological phenomena, are often controlled by the surface and
lateral boundary conditions. Boundary forcing, which can induce
the evolution of regional circulation, such as land-sea breeze, can
be classified into two categories: (a) geographical and physical
factors such as latitude, coastal line shape and topography, and (b)
meteorological and hydrological factors, such as atmospheric
stability, land-surface moisture, and sea surface temperature (SST).
The first category contains almost constant values or those that
change very slowly but the second can vary rapidly within few days
(Lee and Kimura, 2001; Lee et al., 2007, 2008; Azorin-Molina and
Chen, 2009). Therefore, some factors including the second category
should be treated carefully when assessing air quality problems.
Thermal contrast between the land and sea surface induces
atmospheric circulation that transports air pollutants to the surrounded regions. Since urbanized cities with a heavy population
* Corresponding author. Tel./fax: þ82 51 583 2651.
E-mail address: heakee@pusan.ac.kr (D.-H. Kim).
and emissions are mainly located on coastal regions, a good
understanding of the airesea interactions and characteristics of
regional circulation is needed for making a precise assessment of
the air quality impact.
Many studies on the relationship between urban air quality and
land-sea breeze circulation have been performed using numerical
models and in situ data (Bowers, 2004; Ding et al., 2004; Lemonsu
et al., 2006). These studies concluded that the meteorological and
environmental phenomena near coastal regions are strongly associated with the locally induced land-sea breeze.
At this point, considerable effort has been made to obtain
precise and detailed SST information affecting land-sea breeze
development using in situ and remote sensing observations. In
recent years, the abundant database of the SST distribution based
on satellite based remote sensing and direct observations have
been produced and released to the public (Reynolds et al., 2002;
Rayner et al., 2003; Worley et al., 2005; Lacasse et al., 2008). And
researches on the impact of the SST variation on various scale
atmospheric phenomina were also carried out using numerical
model. Sura and Sardeshmukh (2008) verified relation between the
skewness and kurtosis of daily SST change and also proposed the
non-Gaussian SST dynamics model is one of useful in forecasting
1352-2310/$ e see front matter Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2011.07.059
6440
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
climate events often depending on underlying local SSTs. Lee and
Ryu (2010) reported that the two dimensional spatial distribution
of SST on Yellow Sea is strongly associated with heavy snowfall on
the Korean Peninsula during the wintertime and the gradient of the
SST is one of the key factors that determines the amount and
intensity of snowfall.
However, almost studies on atmospheric circulation including
land-sea breeze and its impact are concerned only with the spatial
resolution of the SST. This is because the SST often varies relatively
slowly over the length of the typical meteorological simulation
except along boundaries of currents like the Kuroshio and Gulf
Stream. The other reason is the difficulty in obtaining information
on the SST temporal variation. But a large diurnal variation in the
SST has been reported using observational equipment and remote
sensors (Soloviev and Lukas, 1997; Kawai and Kawamura, 2002).
Ward (2006) observed strong diurnal warming up to 4.6 C in the
Gulf of California during the Marine Optical Characterization
Experiments (MOCE-5). Flament et al. (1994) and Kahru et al.
(1995) reported the SST variation over a short time using an
AVHRR sensor in California and the Southern Baltic Sea, respectively. The large variation of SST frequently appeared around the
Korean Peninsula. Sakaida et al. (2005) analyzed the SST variation
observed by a satellite sensor in East Sea/Sea of Japan and the SST
varied by 3 C under calm synoptic conditions. These studies
concluded that the variation in the SST over a short period occurs
frequently, and its impact on the development of land-sea breeze is
important. Therefore, the accuracy of an urban air quality simulation is dependent on the accuracy of the modeled land-sea breeze
circulation, which is dependent on the SST precision.
Unfortunately, there is little information on the impact of the
temporal variation of the SST on regional circulation and air quality
near coastal urbanized areas. Therefore, we clarify that the relationship between the temporal resolution of the SST distribution
and how the variation of SST during one week impacts coastal
urban air quality especially, ozone concentration in the southern
part of the Korean Peninsula.
2. Case study description
Gwangyang Bay, as the target area in this study, is located the
south-western part of the Korean Peninsula, which is a well
urbanized area in Korea. Since the region includes heavy industries, such as chemical factories, steel mills, and power plants, the
amounts of nitrogen oxide compounds (NOx) and volatile organic
compounds (VOCs) as precursors for ozone production emitted
from these industries are 61,204 ton yr1 and 2251 ton yr1,
respectively, and these emissions are higher than that of any
other area on the Korean Peninsula (Jeollanamdo, 2004). Fig. 1
shows the location of Gwangyang Bay and its topography. The
region has a complex coastline with a small peninsulas and
islands as well as coastal mountains that rise as much as 200 m
above sea level. The regional circulations are induced not only by
the heterogeneity of the surface heat flux from the land and sea
but also orographic forcing due to the complex pattern of
topography. Therefore, a precise estimation of the wind field is
challenging when the regional circulations from two different
forces develop simultaneously.
Several sites, where meteorological factors and air quality
including the ozone concentration were observed, were located in
the regions presented in Fig. 1b. Rectangular and circles with an
identification number show the location of the meteorological and
air quality observation sites, respectively. The observation data was
used mainly to verify and analyze the estimated values of the wind
and ozone concentration for several numerical experiments discussed in following section.
Fig. 1. (a) Numerical model domains for the RAMS simulations and (b) topographic
map of Gwangyang bay region including the observation sites. The closed rectangular
and circles indicate the location of the meteorological and air quality observation sites.
Fig. 2 is time variation of observed SST at buoy located at Geoje
Island shown in Fig. 1b over full dates in August 2007. Mean SST at
Korean Strait seems to oscillate gradually due to the current passing
through the strait and daily varied SST also detected. So the origination and development of regional circulation is also strongly
Fig. 2. Temporal variation in the SST observed at a buoy located at Geoje Island near
the Gwangyang Bay area from 1 till 31 August 2007. Rectangular in the figure indicate
the target period in this study.
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
associated with not only gradually varied SST but also sharply
changed SST. Especially, change of SST during 6 day periods from
the 14 August 2007 shown in rectangular is dramatically increased.
The regression of daily mean SST showed a rapid increase from
26.5 C to 27.1 C during just 6 days, although the SST oscillated
between 24.1 C and 28.2 C. The rapid change in SST can change
over a short period of only six days, which will have some impact
on wind field and distribution of ozone.
The assessment period included both the low and high episodes
of ozone concentration over Gwangyang Bay to explain the relationship between the temporal changes in SST and various ozone
concentrations. One week from 14 to 19 August 2007 were selected
for this study. Fig. 3 shows the surface synoptic charts provided by
the Korea Meteorological Administrator (KMA) at 00 UTC 14 and 17
August 2007. Low pressure of 998 hPa was over the western part of
the Korean Peninsula on the 14 August. However, on the 17 August,
the low pressure system migrated away from Korea and was
replaced with a persistent high pressure of 1014 hPa centered over
the central part of the peninsula, so the strong synoptic wind
occurred only at the beginning of the assessment period.
3. Numerical model description
3.1. Atmospheric model
The atmospheric behavior was simulated by the numerical model
RAMS (Regional Atmospheric Modeling System), which was
6441
developed originally by the Colorado State University to facilitate the
study on predominately meso/microscale atmospheric phenomena.
The model has been applied widely to explain the characteristic of
regional circulation and the evolution of urban air quality (Pielke and
Uliasz, 1998; Medvigy et al., 2005; Freitas et al., 2007).
Table 1 shows the detail grid structure and physical parameterization used by RAMS in this study. Three levels of nested
domains in RAMS grids system were specified to model the regions
complex wind flow and air pollution. The regional circulation over
the Gwangyang region is controlled mainly by the pressure system
of the synoptic scale over the Korean Peninsula. Therefore, the first
domain covered the entire area of the Korean Peninsula, including
and adjacent water masses. The coarse domain (D1) is 112 112
with 9 km intervals. The intermediate domain (D2) covering the
Korean Peninsula contains 137 137 grids with a horizontal
resolution of 3 km. The finest domain (D3) encompass the entire
territory of Gwanyang Bay and has 162 162 grids points with
a 1 km grid spacing. Vertically, 33 sigma levels were specified
unequally from the ground to the model top 10 km height. Since the
gradient of heat and momentum flux varies sharply near surface,
the lower 10 layers were designed within a 1 km height to simulate
the sharply varying fluxes near the surface with more precision.
RAMS gives several options for sub grid parameterization.
Prognostic turbulence kinetic energy was calculated using a Louis
parameterization scheme based on Monin-Obukov similarity
theory (Cotton et al., 2003), and the Kuo (1974) convection scheme
and Mahrer and Pileke (1977) radiation parameterization were
used to calculate the cumulus convection only for coarse and
intermediate domains and short/long wave radiation. The KainFritsch scheme was also used for microphysics parameterization
(Kain and Fritsch, 1993). LEAF-2 (Land EcosystemeAtmosphere
Feedback model-version 2) representing exchange of moisture and
heat between atmosphere and vegetation canopy/land surface is
used for land-surface model in this study (Walko et al., 2000). LEAF2 includes prognostic algorithm to estimate the soil and vegetation
canopy temperature. The soil model in this study consists of 10
layers to 2 m depth. The initial soil temperature was set on a slight
offset to lowest level air temperature.
Table 1
Description of the meteorological model (RAMS) and air quality model (CAMx).
RAMS
Domain 1
Horizontal grids
Resolution
Vertical layers
Physical option
112 112
137 137
162 162
9 km
3 km
1 km
33 layers
Mellor-Yamada turbulence (Mellor and Yamada, 1974)
LEAF-2 land-surface model (Walko et al., 2000)
Kuo cumulus parameterization
MahrerePielke radiation parameterization
NCEP/NCAR Reanalysis data (CDAS) with 2.5 2.5 spatial
resolution and 12 h interval
2007.08.13 0000 UTC w 2007.08.20 0000 UTC
Initial data
Time periods
Domain 3
CAMx
Air Quality Domain 1 (AQD1) Air Quality Domain 2 (AQD2)
Horizontal grids
Resolution
Vertical layers
Chemical option
110 110
162 162
3 km
1 km
16 Layers
Gas-phase chemistry: CB-IV (Gery et al., 1989)a
Aqueous chemistry: RADM-AQ (Chang et al., 1987)a
Aerosol chemistry (ionic): ISORROPIA (Nenes et al., 1998)a
Aerosol chemistry (organic): SOAP (Strader et al., 1998)a
ACE-ASIAa, ARCTASa, CAPSSa
2007.08.13 000 UTC w 2007.08.20 0000 UTC
Emissions
Time periods
Fig. 3. Synoptic charts at the ground level at 09 LST a) 14 and b) 17 August 2007.
Domain 2
a
CB-IV: Carbon Bond IV; RADM-AQ: Regional Acid Deposition Model for Aqueous
chemistry; SOAP: Secondary Organic Aerosol formation/Partitioning; ISORROPIA:
Inorganic Aerosol Thermodynamics/Partitioning; ACE-ASIA: Aerosol Characterization Experiment in Asia; ARCTAS: Arctic Research of the Composition of Troposphere from Air craft and Satellite; CAPSS: Clean Air Policy Support System.
6442
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
RAMS also allows Four Dimensional Data Assimilation (4DDA).
The initial and lateral boundary data in the two coarser domains
were nudged, using the Davis scheme, toward the Regional Data
Assimilation and Prediction System (RDAPS) provided by the KMA
at 6 h intervals.
and 1 km, and 110 110 and 151 151 for air quality domain 1
(AQD1) and 2 (AQD2) covered entire Korean peninsula and
Gwangyang Bay area, respectively. Vertical layers for CAMx simulation is 16 sigma levels from the ground to a 5 km height and
lowest layer is approximately 10 m. Meteorological data, such as
wind, temperature, pressure, relative humidity and momentum,
3.2. Air quality model
The air quality model used in this study was the Comprehensive
Air quality Model with eXtensions (CAMx), which is an Eulerian
photochemical model for a widely applicable assessment of
gaseous and particulate air pollution from the urban to regional
scale (Environ, 2004). The model grid system and physical option of
CAMx simulation is also shown in Table 1. The CAMx contains two
different photochemical reaction mechanisms: CB-IV (Gery et al.,
1989) and SAPRC (Carter, 1996). In this study, updated CB-IV
including aerosol chemistry and updated second olefin species
was used to simulate the ozone.
Two nested system were established for CAMx simulations and
their domains are almost same domains for RAMS simulations
(Fig. 1). The horizontal resolution and the grids numbers are 3 km
Fig. 4. Distribution of a) VOC and b) NOx emissions based on the CAPSS (Clean Air
Policy Support System) in Gwangyang bay region.
Fig. 5. SST distribution at the end of the simulation for a) YES-UP case, b) NO-UP case,
and c) MON case on 19 August 2007. The distribution of the SST in NO-UP and MON
cases were maintained over the entire assessment period, but in the YES-UP case, the
SST values varied with time. The shading indicates the topography and intervals of the
solid line is 0.1 C.
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
and heat transfer coefficients estimated by RAMS were used for
input of CAMx simulation each hour.
The anthropogenic emission data for background concentration was applied from the Aerosol Characterization Experiment in
Asia (ACE-Asia) carried out during 2001 in China, Japan,
and Korea. The ACE-Asia touched the core of the variation in
the aerosol composition and sizes during their intercontinental
transportation and contributed to improving the accuracy of
the trans-boundaries pollutant concentration. BVOC and
biomass burning data in coarse domain were obtained from
Guenther et al. (1995) and Arctic Research of the Composition of
6443
Troposphere from Air craft and Satellite (ARCTAS) (http://mic.
greenresource.cn/arctas_premission), respectively. The anthropogenic, biomass burning, and BVOC emission inventories in the
domestic area were derived from the Clean Air Policy Support
System (CAPSS) provided by the Korea National Institute of
Environmental Research (NIER). Fig. 4 shows the distribution of
NOx and VOC in the Gwangyang Bay area. The NOx inventories
were densely located in the Gangyang and Yeosu region. This
pattern agrees well with the population and industry distribution
in the area. On the other hand, the VOC sources are located
widely around the area compared to the distribution of NOx
Fig. 6. Surface wind vectors for the YES-UP, NO-UP and MON cases at 13 LST (left panels) and 15 LST(right panels) 17 August 2007. The shading means the topography and the solid
line denote the coastline.
6444
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
sources. This is due to the biogenic VOC from the mountainous
areas located in the northern part of the domain.
4. Results
4.1. Spatio-temporal variation in the SST
The continuously varying SST known as the New Generation SST
(NGSST) was used to clarify the impact of the temporal variation in
the SST on the urban air quality near the coast (Guan and
Kawamura, 2005). The NGSST provided by the Japan Aerospace
Exploration (JAXA) is quality controlled, cloud free, wide covering,
and high spatial resolution daily SST dataset with 0.05 interval.
The SST data with 24 h intervals was generated by objectively
merging several satellite data sets, such as Advanced Very High
Resolution Radiometers (AVHRR), Moderate Resolution Imaging
Spectroradiometer (MODIS) and Advanced Microwave Scanning
Radiometer for Earth Observing System (AMSR-E), and the observation data were also included in the NGSST product. Sakaida et al.
(2005) compared the NGSST and SST observed in situ by buoys in
the northern part of Japan from July 2000 to October 2004 and
showed a bias and root mean square of 0.15 C and 0.84 C,
respectively. They suggested that the NGSST is reasonable to use for
research on a microscopic ocean structure including eddies and
fronts near the coast. Therefore, the NGSST was adopted as the SST
distribution near Gwangyang Bay with high spatial and temporal
resolution.
Three different numerical experiments were carried out in
accordance with the temporal variation of the SST, which are the
monthly mean values (case MON), the NGSST without a temporal
update (case NO-UP), and updated discretely NGSST each day over
entire period (case YES-UP). Fig. 5 shows the three sets of SST
distributions used in the numerical experiments. The figures indicated the SST distribution at the final stage on the 19 August 2007
for the simulation in each case. The SST distribution in NO-UP case
did not change during simulation (Fig. 5b). On the other hand, the
SST values in the YES-UP case did vary with time. MON case (Fig. 5c)
had constant monthly mean SST values for entire simulation time.
At the start of the simulation period on 15 August 2007, the SST
were distributed around 27 C but the SST reached 29.8 C at the
end of the period. It is well known that the SST evolution should be
often negligible over a period of several days due to its great
thermal capacity in conventional mesoscale meteorological
research. However, in this study, the SST varied 2.8 C over a 5 day
period, which is considerable and should have an impact on the
wind field associated with urban air quality. The monthly mean
values in Fig. 5c were around 28 C. Therefore these different
temporal variations of SST will affect the development of the
mesoscale circulation and its impacts should be clarified to confirm
the ozone concentration in the target area near the coastline.
4.2. Impacts on regional meteorological system
Fig. 6 shows the wind field simulated by the three experiments
at 03 LST and 15 LST 17 August 2007. Land breeze coupled with the
down slope mountain wind due to the radiation cooling was
predominant in all numerical experiments at 03 LST, and the wind
pattern over the inland areas in all cases is nearly identical.
However, the discrepancy in the wind pattern over water is
conspicuous. The simulated wind in the YES-UP case (Fig. 6a) is
northwesterly, almost at right angles to the coastline. On the other
hand, in the NO-UP case, the wind ran parallel to the coastline
(Fig. 6c).
During daytime, sea breeze developed well in three cases and
their patterns at 1500 LST are also similar. But their characteristics
such as onset time, cessation time and maximum wind speed are
some different among the cases. Table 2 shows these statistics at
two sites Hwangjeon and Yeosu shown in Fig. 1b to clarify the
characteristics of modeled sea breeze for each case. Sea breezes at
Hwangjeon and Yeosu begin to organize at 1120 LST and 1100LST
for case YES-UP and 1040 LST and 1030 LST for case NO-UP,
respectively. And their mean cessation times at two monitoring
sites are 1710 LST for YES-UP case and 1745 LST for NO-UP case. In
comparison with NO-UP case, therefore, SST increasing continuously with time for case YES-UP act to prevent the onset of the sea
breeze circulation and make its cessation time earlier.
And the maximum wind speed of sea breeze in the YES-UP case
was the lower about 23% and its penetration distance is also shorter
for 14% than that in case NO-UP. Consequently, the regional wind
pattern over the ocean during the night and change of sea breeze
characteristics during the daytime are influenced by the SST
distribution and they provide proof that a temporal varying SST is
important because it does impact key properties of the local sea
breeze circulation.
In order to clarify the assessments accuracy quantitatively, the
Index of Agreement (IOA) and Root Mean Square Error (RMSE) for
statistical analysis was proposed in this study.
The RMSE is defined as follows:
"
RMSE ¼
N
1X
ðP Oi Þ2
N i¼1 i
#1=2
;
where Pi and Oi are the 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 RMSE is less than the standard
deviation of observed data and if the standard deviation for
simulated data is comparable with that for observed data.
IOA is defined as
PN
IOA ¼ 1 PN
i ¼ 1 ðPi
i ¼ 1 ðjPi
Oi Þ2
Oj þ jOi OjÞ
;
where O is the average of the observed data. Its value of 1 indicates
that there is perfect agreement between the predicted and
observed data, and an IOA of zero shows no agreement at all.
Table 3 presents the IOA and RMSE of the temperature and wind
speed at four meteorological sites. Although the RMSE of the air
temperature are slightly distributed according with locations, the
values is below the 2.55 C and the IOA of the air temperature for all
cases are also over 0.85 for every site except for Suncheon site in
case MON. This low RMSE and high IOA value means that the
estimated air temperature variations are agreement with the
observation and the values are acceptable for use in the next
Table 2
Characteristics of modeled sea breeze at two meteorological sites located inland at
19 August 2007. The number in brackets is identification number shown in Fig. 1b.
Penetrationa
Onset
Cessation Max. wind
time (LST) time (LST) speed (m s1) (km)
Hwangjeon (265) YES-UP
NO-UP
MON
Sangbong (131)
YES-UP
NO-UP
MON
Averaged value
YES-UP
NO-UP
MON
1120
1040
1050
1100
1030
1040
1110
1035
1045
1730
1750
1750
1700
1740
1750
1715
1745
1750
4.1
5.5
5.0
5.3
6.1
6.0
4.7
5.8
5.5
53
65
64
75
81
78
64
73
71
a
Penetration of sea breeze indicates the Sea breeze traveling distance from
coastline passing through each site.
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
Table 3
RMSE and IOA values for wind speed and temperature at four different meteorological observation sites during one week.
Meteorological data sites
Goheung
Sunchun
Hwangjeon
Yeosu
Averaged difference
YES-UP
NO-UP
MON
YES-UP
NO-UP
MON
YES-UP
NO-UP
MON
YES-UP
NO-UP
MON
YES-UP
NO-UP
MON
Wind speed
Temperature
IOA
RMSE (m s1)
IOA
RMSE (C)
0.78
0.75
0.74
0.69
0.68
0.68
0.85
0.79
0.75
0.79
0.71
0.69
0.78
0.73
0.67
0.87
1.11
1.69
1.04
1.41
1.40
0.90
0.97
0.96
1.07
1.38
1.48
0.97
1.21
1.38
0.94
0.91
0.90
0.87
0.85
0.82
0.92
0.89
0.87
0.95
0.93
0.90
0.93
0.89
0.87
1.18
1.53
1.74
2.91
3.60
3.60
2.57
2.93
3.58
0.92
1.79
1.27
1.91
2.46
2.55
photochemical simulation. On the other hand, the IOA of the wind
speed was approximately 0.7, and the values were lower than the
temperature IOA. The prediction accuracy of wind speed tends to be
rough compared to that of temperature, because atmospheric
dynamics, such as the regional wind pattern is more complicated
6445
and sensitive for the surrounding microscopic topography and
observed synoptic condition (Lee et al., 2007). At this point, an IOA
value of 0.7 for wind speed is also adequate for assessing the
photochemical air quality at the Gwangyang Bay region.
Comparing the YES-UP and NO-UP case, the IOA of the
temperature and wind speed for the YES-UP case is greater than the
other two cases. The maximum values of temperature and wind
speed for the YES-UP case was up to 0.95 and 0.75 at Yeosu and 0.92
and 0.85 at Hwajeong, respectively. Therefore, the accuracy of the
SST variation helped improve the prediction of the simulated
regional circulations.
In order to confirm the impact of the temporal varied SST on the
regional wind speed in accordance with the synoptic conditions,
Normalized Wind Difference (NWD) was defined as follows:
NWD ¼
WYESUP WNOUP
;
Wmean
where, WYES-UP and WNO-UP are estimated wind speed for the
YES-UP and NO-UP case, respectively, and Wmean is mean wind
speed for two cases. Because the difference between two numerical
experiments is only temporal variation of SST and the NWD indicates the difference of wind speed estimated by two numerical
cases, large values of absolute NWD mean the impact of temporal
variation of SST become great. Fig. 7 shows the time variation in the
Fig. 7. Time series of the NWD (Normalized Wind Difference) at four meteorological observation sites: a) Goheung, b) Suncheon, c) Yeosu, and d) Hwangjeon. The unit is nondimensional due to the normalization of wind speed.
6446
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
NWD at four meteorological sites. The fluctuation ranges of the
NWD clearly broadened with time at all four sites. In particular,
after the 17 August when the high pressure system was dominant at
the Gwangyang Bay region, the NWD grew rapidly compared to
that before. The maximum NWD at Yeosu and Goheoung was close
to the emission inventories, 0.43 and 0.4, reached on the 19 August,
respectively. This indicates that the fine structure of the SST
distributions depending on their temporal resolution affects the
regional circulation and its impact was more conspicuous under
weak synoptic conditions during the second sub period. Because
a high ozone concentration often occurs under weak synoptic
conditions, a precise understanding of the impact of the temporal
variations of the SST is needed to predict the ozone concentration
precisely near the coastal region.
4.3. Impacts on the ozone concentrations
The influence of the SST with temporal variations on the ozone
concentration over urbanized coastal areas was examined by
measuring the distribution of the ozone concentration for three
cases at 1500 LST and 03 LST 19 August 2007, as shown in Fig. 8. At
15 LST, when the maximum ozone concentrations were measured
at 140 ppb, the maximum values for all cases appeared at the same
area, the Yeosu Industrial Complex (YIC), where a dense population
of emissions is located. However, the patterns of dispersion and
diffusion along the windward direction differ slightly in each case.
A branched flow of ozone originating near the inventories
appeared to move inland along the Sumjin River in Fig. 1b. This was
due to the sea breeze the developed at that time, and the breeze
Fig. 8. Distribution of ozone concentration at ground level for the three cases at 15 LST (Left Panels) and 03 LST (right panels) 19 August 2007. The unit of shading is ppm and the
solid line denotes the coastline.
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
flowed mainly along the river with a lower elevation, as shown in
Fig. 6. The amount of ozone transported by the sea breeze was most
abundant in the NO-UP case than in the other cases. Another
dispersion of ozone was also found near the ocean at the windward
side due to the strong wind along the coastline, as shown in Fig. 6.
On the other hand, at 03 LST, the maximum value ozone concentration was 40 ppb. The maximum ozone concentration was located
at the eastern boundary in each case. This is caused by the land
breeze and wind along the coastal line. The concentration for YESUP case was lowest because of the strong land breeze.
The observed and estimated values of ozone concentration at
photochemical observation sites were compared to verify the
spatial distribution of the simulated ozone concentration and to
clarify the impact of the SST variation on the ozone concentration
quantitatively. Several observation sites, such as Gwangmu (121),
Munsu (125), Samil (122), Jungdong (352), Jinsang (354), and
Sangbong (131), for measuring the photochemical ozone concentration, are included in the model domain and shown in Fig. 1. The
impact on the assessment of the ozone concentration was evaluated by comparing the estimated ozone concentration with the
meteorological data resulting from each case previously mentioned
above.
Fig. 9 shows the time variation in the observed and simulated
ozone concentration at the photochemical observation sites. Ozone
concentrations were lower the first few days and much higher the
6447
last few because of the transition from low pressure to high pressure. Since the peak concentration depends mainly on solar radiation, the diurnal variation pattern of the ozone concentrations
estimated by YES-UP and NO-UP cases agree well with the
observed values.
With regard to their quantitative comparison, some difference
can be found particularly during the second sub period with high
ozone concentrations. The peak concentration in the YES-UP case
tends to be more reasonable than that in the NO-UP and MON
cases.
The IOA for each observation site is presented in Table 4 to
clearly show the impact of the continuously varying SST on the
ozone concentration quantitatively. The absolute ozone concentration is strongly associated with the synoptic conditions. Hence,
the statistical analysis of ozone was also divided into two sub
periods with different synoptic motion, as mentioned above. The
accuracy of the estimated ozone concentration was in the order of
0.86 for IOA values, which was higher in all cases for the second sub
period than in the first sub period (z0.66). This suggests that the
photochemical ozone assessment using CAMx with the meteorological model RAMS can propose more practical results at the high
ozone episode in this area. It should be noted that the case giving
the most successful assessment can vary in accordance with the
synoptic conditions. The most reasonable estimation for first and
second sub period was established in the NO-UP and YES-UP cases,
Fig. 9. Temporal changes in the modeled and observed ozone concentration at six air quality observation sites. The shading denotes the observed ozone concentration.
6448
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
Fig. 9. (continued).
respectively. In particular, in the second sub period, the difference
in the IOA among the cases was noticeable compared to that in the
first sub period, and high IOA values were estimated for the YES-UP
case at all sites except for Gwangmu. However, a uniform IOA
distribution was not observed in the first sub period. This indicates
that the temporal and spatial resolution of SST has a noticeable
influence on ozone concentration during weak synoptic conditions
associated with high pressure.
This study also focused on the spatial distributions of the IOA
difference between the YES-UP and NO-UP cases. The IOA
Table 4
Estimated IOA values at the air quality observation sites for each case at the two
different sub periods. The bold indicates the case for the best performance at each
site.
Site name
Gwangmu (121)
Munsu (122)
Samil (125)
Jungdong (362)
Jinsang (354)
Sangbong (131)
Mean value
1st sub period
2nd sub period
YES-UP
NO-UP
MON
YES-UP
NO-UP
MON
0.63
0.65
0.61
0.74
0.68
0.69
0.66
0.65
0.65
0.62
0.74
0.68
0.69
0.67
0.63
0.64
0.59
0.71
0.61
0.67
0.64
0.87
0.90
0.87
0.92
0.93
0.89
0.90
0.88
0.87
0.82
0.79
0.81
0.78
0.82
0.79
0.81
0.79
0.77
0.81
0.74
0.79
difference during second sub period in Gwangmu, Munsu, and
Samil, which are surrounded by the sea, was below 0.05. However,
the IOA in Jungdong, Jinsang, and Sangbong located near the coast
and inland during same period was over 0.1 and reached 0.13 at
Jungdong. So the impact of the temporal variation in the SST tends
to differ in accordance with the ozone observation sites.
In order to clarify this spatial distribution of the impact, a scatter
plot was made of the simulated and observed ozone concentrations
in two different groups, peninsula type sites (Gwangmu, Munsu,
and Samil) and inland type sites (Jungdong, Jinsang, and Sangbong),
during the second sub period, as shown in Fig. 10. The thick solid
line indicates the perfect estimation and the regression lines for
each scatter data are also included in the figure. As shown in the
figure, although the temporal changes in the SST distributions were
different among the cases, the simulated ozone concentration at
the peninsula type sites for all cases agreed well with the observations during the second sub episode. This indicates that the
impact of the SST temporal variations on the ozone concentration is
not so great in the peninsula type sites.
On the other hand, modeled values in the inland type sites were
different from those in the peninsula type sites. The estimated
values for the YES-UP case corresponded to the observed values.
However, there were some discrepancies between the modeled and
observed ozone concentration in the NO-UP and MON case at high
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
6449
The results are as follows:
Fig. 10. Correlation between the observed and predicted ozone concentration at
a) peninsula type sites, such as Gwangmu, Munsu, and Samil, and b) inland type sites,
such as Jungdong, Jinsang, Sangbong for the second sub period. The thick solid line
denotes the perfect fit line and the thin, dotted, and dashed lines indicate the
regression for the YES-Up, NO-UP and MON cases, respectively.
ozone concentrations. Although there is considerable scatter at
high ozone concentrations, their disagreement become clear and
large in sites with high ozone concentrations. Therefore, a temporally varied SST tends to influence the ozone concentrations at
inland type sites. Although the high ozone episode occurred, the air
quality over the peninsula type area was not controlled as greatly
by the temporal variation in the SST compared with that the over
inland located far from the coastal line.
5. Conclusions
Comparison studies between the observed and numerically
estimated ozone concentrations with various SST temporal resolutions were carried out to understand the impact of the temporal
resolution of the SST on the air quality over well urbanized coastal
area. The numerical models used in this study were the RAMS
mesoscale meteorological model and CAMx air quality model.
A well urbanized target area with a dense population and high
emissions called Gwangyang Bay is situated at the southern part of
the Korean Peninsula, and the assessment period for investigating
the SST impact under various synoptic conditions was from the
15e19 August 2007. Three different SST sets based on the NGSST
were established in accordance with their temporal resolutions.
1) Based on the Buoy observations, the SST at Geoje Island near
the Gwangyang Bay region increased 4.1 C during the target
period, and NGSST at the Gwangyang Bay region also increased
to 29.6 C over same period. This rapid variation in the SST has
a noticeable impact on the estimation of the meteorological
wind field and distribution of photochemical ozone in the
region.
2) The wind intensity including the wind direction is also influenced by the temporally varied SST distribution. In the YES-UP
case with temporally varied SST, the maximum wind speed is
weaker about 23% and meteorological elements more reasonable than that estimated in the other cases due to the precise
information of the SST distribution. In IOA analysis, the most
successfully estimated IOA values for the surface air temperature and wind speed were obtained in the YES-UP case
reaching 0.958 and 0.829, respectively.
3) The impact of the SST variations on the wind and photochemical
ozone concentration often depends on the synoptic scale
atmospheric conditions. In this study, during the first sub
episode when a relative low pressure developed over the whole
domain, the differences in the wind field for the three cases
with various SST temporal change was not so great. However,
the wind speed estimated with the high temporal resolution
(case YES-UP) decreased clearly during the second sub episode
compared with the other cases over the same period.
4) The concentration of ozone is often linked with land-sea
breeze, which also connected by temporally varied SST,
particularly under weak synoptic conditions. For this reason,
during the second sub period, the discrepancies in the ozone
concentration between the cases reached a maximum and the
prediction accuracy of the ozone concentration improved
clearly for the YES-UP case. In particular, the precise SST
distribution and its impact are more crucial factors for estimating the ozone concentration when a high ozone episode
occurred.
5) Although the impact of the SST distribution with temporal
change tends to be magnified in high ozone episodes,
a decrease in the accuracy of the estimated ozone concentration occurred in accordance with the receptor location. In this
study, the ozone concentration over the inland type area was
more sensitive to the SST distribution than that over the
peninsula type areas.
Although this study focuses on high ozone episode during only
several days, the tendency of the impact of continuous SST variation
on the regional scale circulation is confirmed qualitatively. If
diurnal variation of SST due to several factors such as oscillated
solar radiation and air temperature occur, the acquisition of ocean
conditions including the temporal variation in the SST is indispensible for assessing and predicting the air quality in well
urbanized area near coast precisely.
Acknowledgments
This work was funded by the Korea Meteorological Administration Research and development program under Grant CATER
2011-1185.
References
Azorin-Molina, C., Chen, D., 2009. A climatological study of the influence of
synoptic-scale flows on sea breeze evolution in the Bay of Alicante (Spain).
Theoretical Applied Climatology 96, 249e260.
6450
S.-H. Lee et al. / Atmospheric Environment 45 (2011) 6439e6450
Bowers, L.A., 2004. The Effect of Sea Surface Temperature on Sea Breeze Dynamics
Along the Coast of New Jersey. Thesis of Master Degree. The State University of
New Jersey.
Carter, W.P.L., 1996. Condensed atmospheric photooxidation mechanisms for
isoprene. Atmospheric Environment 30, 4275e4290.
Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P., Stockwell, W.R.,
Walcek, C.J., 1987. A three-dimensional Eulerian acid deposition model: physical
concepts and formulation. Journal of Geophysical Research 92, 14681e14700.
Cotton, W.R., Pielke, R.A., Walko, R.L., Liston, G.E., Tremback, C.J., Jiang, H.,
McAnelly, R.L., Harrington, J.Y., Nicholls, M.E., Carrio, G.G., McFadden, J.P., 2003.
RAMS 2001: current status and future directions. Meteorology and Atmospheric
Physics 82, 5e29.
Ding, A., Wang, T., Zhao, M., Wang, T., Zongkai, L., 2004. Simulation of sea-land
breezes and a discussion of their implications on the transport of air pollution during a multi-day ozone episode in the Pearl River Delta of China.
Atmospheric Environment 38, 6737e6750.
Environ, 2004. User’s Guide e Comprehensive Air-Quality Model with Extensions,
Version 4.10s. Environ International Corporation.
Flament, P., Firing, M., Sawyer, M., Trefois, C., 1994. Amplitude and horizontal
structure of a large diurnal sea surface arming event during the coastal ocean
dynamics experiment. Journal of Physical Oceanography 24, 124e139.
Freitas, E.D., Rozoff, C., Cotton, W.R., Silva Dias, P.L., 2007. Interactions of urban heat
island and sea breeze circulations during winter over the metropolitan area of
São Paulo e Brazil. Boundary-Layer Meteorology 122, 43e65.
Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics
mechanism for urban and regional scale computer modeling. Journal of
Geophysical Research 94, 12925e12956.
Guan, L., Kawamura, H., 2005. Merging satellite infrared and microwave SSTs:
methodology and evaluation of the new SST. Journal of Oceanography 60,
905e912.
Guenther, A., Hewitt, C.N., Erickson, D., Fall, R., Geron, C., Graedel, T., Harley, P.,
Klinger, L., Lerdau, M., McKay, W.A., Pierce, T., Scholes, B., Steinbrecher, R.,
Tallamraju, R., Taylor, J., Zimmermann, P., 1995. A global model of natural
volatile organic emissions. Journal of Geophysical Research 100, 8873e8892.
Jeollanamdo, 2004. Practice Plans for the Determination the Air Quality Regulated
Region in Gwangyang Region, Report of Jeollannamdo Province, Gwangju, pp.
100e133 (in Korean).
Kahru, M., Håkansson, B., Rud, O., 1995. Distributions of the sea-surface temperature fronts in the Baltic Sea as derived from satellite imagery. Continental Shelf
Research 15, 663e679.
Kain, J.S., Fritsch, J.M., 1993. Convective parameterization for mesoscale models: the
Kain-Fritsch scheme, in the Representation of cumulus convection in numerical
models. Meteorological Monographs 24, 165e170.
Kawai, Y., Kawamura, H., 2002. Evaluation of the diurnal warming of sea surface
temperatures using satellite-derived marine meteorological data. Journal of
Oceanography 58, 805e814.
Kuo, H.L., 1974. Further studies of the parameterization of the influence of cumulus
convection on large-scale flow. Journal of Atmospheric Science 31, 1232e1240.
Lacasse, K.M., Splitt, M.E., Lazarus, S.M., Lapenta, W.M., 2008. The impact of highresolution sea surface temperatures on the simulated nocturnal Florida
marine boundary layer. Monthly Weather Review 136, 1349e1372.
Lee, S.H., Kimura, F., 2001. Comparative studies in the local circulation induced by
land-use and by topography. Boundary Layer Meteorology 101, 157e182.
Lee, S.H., Ryu, C.S., 2010. Influence of continuous satellite based SST distribution on
heavy snowfall events over the Korean Peninsula. International Journal of
Remote Sensing 31, 2853e2883.
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
southwestern coastal area, Korean Peninsula. Atmospheric Environment 41,
4451e4465.
Lee, S.H., Sung, K.H., Lee, H.W., 2008. Impacts of regional trans boundary ozone
associated with complex terrain on urban air quality. Atmospheric Environment
42, 7384e7396.
Lemonsu, A., Pigeon, G., Masson, V., Moppert, C., 2006. Seaetown interactions over
Marseille: 3D urban boundary layer and thermodynamic fields near the surface.
Theoretical Applied Climatology 74, 1e12.
Mahrer, Y., Pielke Sr., R.A., 1977. A numerical study of the airflow over irregular
terrain. Contributions to Atmospheric Physics 50, 98e113.
Medvigy, D., Moorcroft, P.R., Avissar, R., Walko, R.L., 2005. Mass conservation and
atmospheric dynamics in the regional atmospheric system (RAMS). Environmental Fluid Mechanics 5, 109e134.
Mellor, G.L., Yamada, T., 1974. A hierarchy of turbulence closure models for planetary boundary layers. Journal of Atmospheric Science 31, 1791e1806.
Nenes, A., Pilinis, C., Pandis, S.N., 1998. ISORROPIA: a new thermodynamic model for
multiphase multicomponent inorganic aerosols. Aquatic Geochemistry 4,
123e152.
Pielke, R.A., Uliasz, M., 1998. Use of meteorological models as input to regional and
mesoscale air quality models-limitations and strengths. Atmospheric Environment 32, 1455e1466.
Rayner, N.A., Parker, D.E., Horton, E.B., Folland, C.K., Alexander, L.V., Rowell, D.P.,
Kent, E.C., Kaplan, A., 2003. Global analyses of sea surface temperature, sea ice,
and night marine air temperature since the late nineteenth century. Journal of
Geophysical Research 108, 4407e4435.
Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C., Wang, W., 2002. An improved
in situ and satellite SST analysis for climate. Journal of Climate 15, 1609e1625.
Sakaida, E., Takahashi, S., Shimada, T., Kawai, Y., Kawamura, H., Hosoda, K., Guan, L.,
2005. The production of the new generation sea surface temperature (NGSST-O
Ver.1.0) in Tohoku University. In: Institute of Electrical and Electronics EngineersInternational Geoscience and Remote Sensing Symposium, pp. 2602e2605.
Soloviev, A., Lukas, R., 1997. Observation of large diurnal warming events in the
near-surface layer of the western equatorial Pacific warm pool. Deep Sea
Research 44, 1055e1076.
Strader, R., Gurciullo, C., Pandis, S.N., Kumar, N., Lurmann, F.W., 1998. Development
of Gas-phase Chemistry, Secondary Organic Aerosol, and Aqueous-phase
Chemistry Modules for PM Modeling. Technical Report A21e1. Sonoma Technology, Petaluma, CA.
Sura, P., Sardeshmukh, P.D., 2008. A global view of non-Gaussian SST variability.
Journal of Physical Oceanography 38, 639e647.
Walko, R.L., Band, L.E., Baron, J., Kittel, T.G.F., Lammers, R., Lee, T.J., Ojima, D.,
Pielke, R.A., Taylor, C., Tague, C., Tremback, C.J., Vidale, P.L., 2000. Coupled
atmosphere e biophysics e hydrology models for environmental modeling.
Journal of Applied Meteorology 39, 931e944.
Ward, B., 2006. Near-surface ocean temperature. Journal of Geophysical Research
111, 1e18.
Worley, S.J., Woodruff, S.D., Reynolds, R.W., Lubker, S.J., Lott, N., 2005. ICOADS release
2.1 data and products. International Journal of Climatology 25, 823e842.