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Atmospheric Environment 78 (2013) 277e290

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Atmospheric Environment

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A numerical study of the dependence of long-range transport ofCO to a mountain station in Taiwan on synoptic weather patternsduring the Southeast Asia biomass-burning season

Fang-Yi Cheng a,*, Zhih-Min Yang a, Chang-Feng Ou-Yang b, Fong Ngan c

aDepartment of Atmospheric Sciences, National Central University, No. 300, Chun-Da Rd., Chung-Li City, Tao-Yuan County, TaiwanbDepartment of Chemistry, National Central University, TaiwancAir Research Laboratory, NOAA, Silver Spring, MD, USA

h i g h l i g h t s

� WRF simulations were performed during Southeast Asia biomass burning season.� Cluster analysis was performed with WRF output to classify synoptic weather types.� Using a mountain site in Taiwan to assess long-range transported CO from Indochina.� A different long-range transport mechanism of CO from Indochina is identified.

a r t i c l e i n f o

Article history:Received 26 October 2012Received in revised form8 March 2013Accepted 12 March 2013

Keywords:Weather classificationSynoptic weather patternBiomass-burningCarbon monoxideTransport mechanism

* Corresponding author. Tel.: þ886 3 4227151 6550E-mail address: bonniecheng18@gmail.com (F.-Y. C

1352-2310/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.atmosenv.2013.03.020

a b s t r a c t

This study is conducted to identify the synoptic weather patterns that are prone to cause high carbonmonoxide (CO) concentrations observed at a mountain site, Lulin atmospheric background station(LABS), in Taiwan due to the biomass-burning activity in Southeast (SE) Asia. LABS is recognized as aclean background station. The study period targets the biomass-burning season (February to May) from2007 to 2010. The synoptic weather patterns were classified using a two-stage clustering method withinputs from the Weather Research and Forecasting (WRF) meteorological model simulation result in a27-km spatial grid. A 9-km resolution WRF modeling was performed additionally for 13 to 26 March2007, when a high CO concentration reaching 500 ppb was observed at LABS. The simulation resultindicates that not only the existence of the thermal forcing induced low pressure system formed inIndochina, but also the presence of the high terrain located in the northern part of SE Asia that furtherforced the uplift of the biomass-burning emissions. On the other hand, when the northeasterlymonsoonal flow is strong enough and intruding into Indochina, this would hinder the development ofthe thermal low and weaken the upward movements, in turn preventing the transport of biomass-burning emissions from Indochina to the area of Taiwan. The simulation results also demonstrate thatthe location of the SE Asia high pressure system has a moderate effect on the particle dispersion path inthe upper level.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Taiwan has been suffering from a serious air pollution problemdue to locally produced emissions, long-range transported pollut-ants such as ozone (O3), and particulate matter from East China andthe Gobi desert (Chuang et al., 2008). Recently, biomass burning inIndochina has also drawn attention (Yu et al., 2008). Biomass

8; fax: þ886 3 425 6841.heng).

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burning is an important source of CO and other trace gases(Langenfelds et al., 2003). The burning events usually occur intropical regions, such as Southeast (SE) Asia, South America, andAfrica. In Asia, the air pollution problem in spring is stronglyassociated with biomass-burning activities from SE Asia.

The influence of the biomass-burning pollutants in the down-stream has been examined by many observational and modelingstudies. Chan et al. (2003) found that the enhancement of O3 duringthe springtime in the lower troposphere over Hong Kong was aresult of photochemically produced O3 transported from biomass-burning emissions which are most severe in March and then

Fig. 2. Distance between merged clusters.

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decrease in April. Huang et al. (2012) also indicated that thecontribution of biomass burning represents 26e62% of the totalaerosol optical depth (AOD) in Hong Kong and Taiwan.

Yu et al. (2008) derived the forward trajectories arising from theIndochina to analyze the transport path of biomass aerosols. Theirresults showed the trajectories released from the northern part ofIndochina are able to affect entire East Asia, while the trajectoriesreleased from southern Indochina are restricted in SE Asia. Liu et al.(2003) claimed that deep convection is an important mechanismfor driving the export of biomass-burning effluents from SE Asia.Lin et al. (2009a) found that the existence of the mountain leesidetrough is another important factor for uplifting the tracers from thesurface to above 3 km, and tracers are further transported to thedownwind region along with the strong westerly flow. A study byYen et al. (2013) also indicated that the uplifted biomass-burningpollutants would influence local air quality in Taiwan if pollutantswere brought down from the upper level to the surface due to thesubsidence processes associated with the Asian continental anti-cyclone system.

In this study, an observation site, Lulin atmospheric backgroundstation (LABS), located at the top of a mountain with an altitude of2862m in central Taiwan, serves as an atmospheric background site(Sheu et al., 2010) to observe the biomass burning emissions. LABSis away from the direct impact of local emissions and the back-ground CO concentration is 81.9 ppb (Lin et al., 2009b). The hourly-averaged CO concentration observed at LABS is 148.4 ppb duringthe period of April 2006 to April 2011; however, the CO level israised significantly in March, when it isw242 ppb, 1.6 times higherthan the mean concentration (Ou Yang et al., 2012). The observedCO at LABS is used as an indicator to identify the long-rangetransport (LRT) of biomass-burning emissions from SE Asia. CO isan excellent tracer for LRT, with major sources being vehicles,biofuels, and also biomass burning. Furthermore, the lifetime of COis around a few months, which is long enough to track pollutionplumes at synoptic and intercontinental scales.

Prior researchers have demonstrated that biomass-burningemissions from SE Asia can affect the downstream regions throughcertain transport mechanisms; however, the synoptic weatherpattern that favors the transport of the emissions is still not clear.One of the objectives of this study is to distinguish the role of syn-optic weather conditions that cause high CO concentrationsobserved at LABS in Taiwan during the Indochina biomass-burningseason. Synoptic weather classifications have proved a useful sta-tistical method to identify the meteorological conditions that areprone to high pollution events. As a relatively objective classificationmethod, cluster analysis has been broadly used in many studies andprovides a successful result in determining weather patterns. Eder

Fig. 1. Model simulation domains: D1 and D2 are at 27 and 9 km resolutionsrespectively.

et al. (1994) applied cluster analysis to meteorological data for a10-year period (1981e1990) in Birmingham, the largest city in Ala-bama. The classification results show that the high O3 concentrationis strongly related to the anticyclone weather type that is typicallyassociated with clear skies, strong solar insolation, large diurnaltemperature ranges, and warm temperatures, which are favorablefor the high O3 concentration. Ngan and Byun (2011) applied thetwo-stage cluster analysis method to classify the synoptic weatherpatterns over Eastern Texas and the adjacent states by utilizing the850 hpa wind fields for the five-month O3 season (May toSeptember) in the years 2005 and 2006. The results showed that thepost-frontal condition, clear sky and dry weather with northerly ornortheasterly (NE) flow, favors the development of high O3 con-centration. Low wind speed with shifting of wind direction due to

Fig. 3. The circle marks the location of LABS. Contour lines represent the terrain height(m).

Fig. 4. Left panels are averaged SLP (hPa) (shaded colors) and surface wind fields (m s�1) (arrows) and right panels are geopotential height (km) (shaded colors) and wind fields(m s�1) (arrows) at 700 hPa for each cluster. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 1Numbers of daily occurrence at each month for each clusters.

C1 C2 C3 C4 C5

Feb 35 0 50 2 26Mar 32 0 53 10 29April 18 16 31 35 20May 0 39 7 77 1Total 85 55 141 124 76

Fig. 5. Diurnal variation of observed CO, wind speed (WS), and wind direction (WD)averaged for the days in each cluster at LABS.

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sea breeze is a key meteorological factor for the accumulation of thepollutants in urban areas.

In this study, theweather research forecasting (WRF)model wasused to produce gridded meteorological inputs for February to Mayin the years 2007e2010 at 27-km resolution and the simulationresults were used for weather classification by applying a two-stagecluster analysis method (details of the methodological setup arediscussed in Section 2). The objectives of this study are as follows:(1) to understand the synoptic weather patterns that are prone tocause high CO loading at LABS in Taiwan due to the LRT of biomass-burning emissions from Indochina, and (2) to identify the COtransport mechanism from Indochina to the downwind Taiwanarea. The methodology of the cluster analysis as well as theobserved datasets utilized in this study is addressed in next section.The results of weather classification are shown in Section 3. A 9-kmresolutionWRFmodeling was performed additionally for a high COevent observed at LABS in 2007 to investigate the synoptic weatherpatterns that favor the transport of biomass-burning species fromIndochina to the downwind area in Taiwan. The 9-km WRF simu-lation results are examined in Section 4 and the conclusions aresummarized in Section 5.

2. Methodology and observations

2.1. Configuration of WRF modeling

The WRF model version 3.2.1 was used to generate the meteo-rological datasets for the weather classification. The study periodencompasses the four-month SE Asia biomass-burning seasons(February to May) in 2007, 2008, 2009, and 2010, representing atotal of 481 days. The simulation domain is shown in Fig. 1. Theouter domain is at 27-km grid resolution with 295 � 191 gridpoints. The inner domain is at 9-km resolution. The simulationdomain covers most of China, SE Asia, Taiwan, and the westernPacific Ocean. The vertical layer was composed of 35 full sigmalevels, of which 16 were within the lowest 1.5 km, with the lowestlayer at around 16 m. The model top was at 50 hPa. The initial andboundary conditions were acquired from the final analysis of globaldata of the National Center for Environmental Prediction. The WRFphysical options include the following: the KaineFritsch cumulusscheme (Kain and Fritsch, 1993), a Dudhia shortwave radiationscheme (Dudhia, 1989), the Rapid Radiative Transfer Model (RRTM)longwave radiation scheme (Mlawer et al., 1997), a Yonsei Univer-sity boundary layer scheme (Hong et al., 2006), and the Noah landsurface model (Chen and Dudhia, 2001). The WRF simulation re-sults have been compared with the observation data (figure is notshown) to ensure that the model results are reliable.

2.2. Principle component analysis and cluster analysis

The U and V components of wind fields from the WRF simula-tion outputs at the tenth sigma layer (at around 577m height aboveground level) at 0800 local standard time (LST) were used for thecluster analysis. At the 0800 LST, the PBL just begins to grow andwon’t affect too much on the synoptic weather classifications, yetthe weak ground heating processes in the early morning still tellsthe information that a thermally induced low-pressure systemmight form in the midday; therefore, the wind fields at the timewas selected for classification. The tenth sigma level wind fields,which are within the boundary layer, are selected because it cancharacterize the surface weather patterns such as the Asian conti-nental high pressure system, the subtropical Pacific high pressuresystem, the southwesterly monsoonal flow, and the thermallyinduced low pressure system over Indochina that are the principalweather patterns influencing the transport of air pollutants from

Indochina (Yen et al., 2013). In addition, the tenth sigma level is atthe height able to reflect the surface characteristics but not toomuch affected by the intensive turbulence that is existed close tothe ground level. The wind field above the boundary layer is notappropriate for use inweather classification in this study because itis mostly dominated by the westerly flow and does not showdistinct variability comparing to the within PBL winds. A standardprinciple component analysis (PCA) procedure was performed onthe original data matrix in a 295 � 191 grid at 0800 LST for 481days, covering the spring seasons from 2007 to 2010. The total dataarray size was 481 � 112,204 (295 � 190 þ 294 � 191 ¼ 112,204).The leading 38 principal components, which explained more than90% of the original data variance, were retained as the input matrix(481 � 38) for the two-stage clustering method.

Cluster analysis separates data into groups without knowing thenumber of groups and the assignment of group membership inadvance. A two-stage clustering method was performed subse-quently to produce the weather classifications. The first step was ahierarchical method used to determine the initial number of clus-ters, which was later passed to the second step, a non-hierarchicaliterative method using K-means (MacQueen, 1967). The first step ofthe two-stage clustering method justifies the number of clusters byinspecting the distance between merged groups done in hierar-chical grouping. The potential drawback is that a sample cannot bereallocated even it may have been regrouped at an early step. Thiscan be overcome in the second stage in which non-hierarchicalgrouping allows reassignment of samples until the grouping

Fig. 6. The observed CO, temperature (T), wind speed (WS), and wind direction from13 to 25 March 2007 at LABS.

Fig. 7. Fire locations detected from MODIS sate

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reaching the minimum variability within each cluster andmaximum variability between clusters. Any two clusters separatedby the shortest distance were merged into a new cluster in thehierarchical step. The distance between pairs of points was calcu-lated based onWard’s minimum variance method (Ward, 1963), forwhich the sum of squares between the two clusters summed overall the variables is at a minimum. The minimum value is called thecritical value (CRIT) and is defined as

CRIT ¼ NAðCA � CCÞ2 þ NBðCB � CCÞ

2; (1)

where NA and NB are the numbers of points in the two mergedclusters, and CA, CB, and CC represent the centroid values for the twomerged clusters and the new cluster respectively. The centroid isthe average value of all points within a cluster. Fig. 2 shows thevariation of the CRIT as a function of numbers of clusters. The dis-tance between the merged groups becomes noticeably large whenthe number of clusters equals four; therefore, five clusters werechosen as so-called ‘seeds’ for the initial number in K-meansanalysis. Then, the data points were grouped into the nearestcluster of the total five repeatedly until the final solution with theminimum variability within clusters and the maximum variabilitybetween clusters was reached.

2.3. Observations

The observed data from LABS were used to compute the meanCO concentration for each cluster and to verify the sourcee

llite data on 15, 18, 20 and 22 March 2007.

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receptor relationships. Fig. 3 illustrates the location of LABS inTaiwan, with the contour lines representing the topographicalheight distribution.

3. Characterization of the weather classification results

3.1. Synoptic weather patterns for each cluster

Based on the two-stage clusteringmethod, theweather patternswere classified into five clusters. In Fig. 4, the left panels show the

Fig. 8. Distribution of the SLP (hPa) (shaded colors) and wind fields (m s�1) (arrows) at 14figure legend, the reader is referred to the web version of this article.)

mean value of the sea level pressure (SLP) layered with horizontalwind vectors for each cluster. The right panels are the corre-sponding geopotential height and wind fields at 700 hPa pressurelevel. Table 1 summarizes the number of days onwhich each clusteroccurred in each month during the four-year period 2007e2010.For cluster one (C1), there is a continental anticyclone residing nearthe coastline of Eastern Chinawith high wind speed prevailing overthe South China Sea. The local circulation in Taiwan is under theinfluence of the NE monsoonal flow. The NE wind even intrudesinto the whole of Indochina, where the direction of the prevailing

00 LST from 15 to 22 March 2007. (For interpretation of the references to color in this

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wind is from east to northeast. C1 mostly appears in the earlyspring, occurring on 85 out of the total of 481 days (17.67%).

For cluster two (C2), a strong southeasterly monsoonal flowprevails from the Bay of Bengal to the South China Sea, where acyclonic circulation and a low-pressure system are formed. There isa very weak anticyclonic system located over the East China Sea.Under the influence of the high pressure system, the weak NE windprevails in Taiwan. C2 is the least occurring weather type, whichappears 55 days (11.43%) and exists only in April and May.

The third cluster (C3) is the most prevalent cluster, occurring ona total of 141 days (29.31%). Similar to C1, in C3 a high pressuresystem formed in East China. The high pressure system is located

Fig. 9. Distribution of the geopotential height (km) (shaded colors) and wind fields (m s�1)references to color in this figure legend, the reader is referred to the web version of this ar

further north and with weaker intensity compared to C1. The pre-vailing wind in Taiwan is dominated by easterly to southeasterlyflow. The SLP over Indochina are relatively lower with weak syn-optic wind fields. C3 occurred mostly in February, March, and April;it occurred on only seven days in May.

The fourth cluster (C4) is associated with the weak synopticforcing in the area of Taiwan. The wind speed is also the weakestamong the all clusters in the area of Taiwan. With the weak influ-ence of the subtropical Pacific high pressure system, eastern Taiwanis dominated by the weak easterly to southeasterly flow. Typically,under this type of weak synoptic forcing, the local circulation suchas the landesea breeze flow is easily formed in western Taiwan.

(arrows) at 700 hPa at 1400 LST from 15 to 22 March 2007. (For interpretation of theticle.)

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Over the area of Indochina, the strong southwesterly monsoonalflow prevails over the Bay of Bengal, and low pressure is alsosimulated. The southwesterly wind prevails in the western part ofSE Asia while the southeasterly wind prevails in the eastern side. InC4, the Asian continental anticyclone moves eastward and mergeswith the extending subtropical high-pressure system. C4 occurredon 124 days (25.78%). It mostly appears in May, with the number ofoccurrences increasing from February to May. In the tropical re-gions, the SLP is lower in clusters C2 and C4 compared to otherclusters.

The fifth cluster (C5) is associatedwith a continental anticyclonelike clusters C1 and C3. The location of the anticyclone is still overthe continent and the intensity of the high-pressure system is thestrongest among the all clusters. In the area of Taiwan, the flow isdominated by the strong NE wind. The influence of the cold surgeextends to the eastern part of Indochina. C5 appears on 76 days(15.80%) and occurs only once in May.

For the comparison at the 700 hPa level, the intensity of theupper level trough is strong in clusters C1, C3, and C5 while it ismuch weaker in clusters C2 and C4. The location of the upper leveltrough is the closest to the Taiwan area in C5 while it moveseastward and away from Taiwan in C1. In C3, the location of theupper level trough is between clusters C1 and C5. A tropical SE Asiahigh that typically occurs in springtime (Chen et al., 2002) isdetected in clusters C1, C3, and C5 in the upper level. The location of

Fig. 10. Distribution of near surface air temperature (�C) (shaded colors) and wind fieldsinterpretation of the references to color in this figure legend, the reader is referred to the

the SE Asia high shifts in clusters C1, C3, and C5. In C5, it is locatedon top of Indochina, while its center moves eastward in clusters C1and C3. There is a strong southwesterly to westerly wind flowextending from Indochina to Taiwan in clusters C1, C3, and C5.

In brief summary, clusters C1, C3 and C5 are more winterweather types while the rest have less winter characteristics.Particularly for clusters C1 and C5, they are the cold/dry continentalair mass moving toward the ocean and pushing the cold front to-ward the south (Taiwan area). Among clusters C1, C3 and C5, theSLP over Indochina is the lowest in C3, and increases in clusters C5and C1 along with the southward movement of the continentalhigh pressure system. The SLP is the highest in C1 over Indochina,which could reduce the thermal-forcing-induced upward motionsas well as the uplifting of the biomass-burning emissions.

For C1 and C5, the prevailing surface wind in Taiwan is domi-nated by the NE monsoonal flow due to the strong Asian conti-nental anticyclone system, while in C3 the southeasterly winddominates in eastern Taiwan. In C2, the prevailing surface flow inTaiwan is still affected by the NE wind but with weaker intensitycompared to C1 and C5. In C4, the intensity of the continental an-ticyclone is the weakest and the local circulation in Taiwan isdominated by the weak southeasterly flow. Typically under thiskind of weather type the landesea breeze circulation woulddominate the local circulation in western Taiwan where mostlocally generated emissions are produced.

(m s�1) (arrows) at 1400 LST on 15, 18, 20, and 22 March 2007 in Indochina. (Forweb version of this article.)

Fig. 11. The gray shading is the terrain height. Points are the locations for particledispersion modeling. Lines at 15� N (solid line) and 101� E (dotted line) are used forvertical cross-sectional analysis.

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3.2. Characteristics of observed CO and wind fields at LABS for eachcluster

Fig. 5 shows the diurnal variation of observed CO, wind speed,and wind direction averaged for the days in each cluster at LABS.The CO concentration is higher in clusters C1, C3, and C5 e whichoccur mostly during the active biomass-burning months, February

Fig. 12. Vertical cross-sectional analysis at 1400 LST on 15, 18, 20, and 22 March over Indochrepresent the wind vectors of the u! and w! wind components (m s�1) ( u!: the wind vectorreferences to color in this figure legend, the reader is referred to the web version of this ar

and March e than in other clusters. The CO concentration in clus-ters C1, C3 and C5 are higher than the annual averages (148.4 ppb,based on Ou Yang et al., 2012) with the highest level appearing inC5, having a magnitude of w230 ppb. Both C2 and C4, which existfrequently in April and May, exhibit the lowest CO concentrationswith averaged magnitude w170 ppb.

At LABS, the wind fields are dominated by the southwesterlyflow for all clusters except C2. Among the clusters C1, C3, and C5,the intensity of the wind speed varies along with the location of theupper level trough. The wind speed is the highest in C5 (w5 m s�1),for which the location of the upper level trough is the closest toTaiwan, while it is the lowest in C1 (1e2 m s�1), when the upperlevel trough is farthest away from Taiwan.

4. Case study

The WRF simulation was performed at 9-km resolution (refer toFig. 1 for domain setup) to further investigate the synoptic weatherpatterns that are prone to cause high CO concentrations observed atLABS due to the biomass-burning activity in SE Asia. The simulationepisode was from 13 to 26 March 2007, when high CO concentra-tions were observed at LABS.

4.1. Observed data at LABS

Fig. 6 shows the observed CO, temperature, and wind fields atLABS from 13 to 25 March 2007. From 15 to 18 March, the COconcentration was at a high level, with its maximum concentration

ina at latitude 15� N. Shaded colors are for vertical wind speeds (cm s�1). Wind arrowsalong the cross-section line, w!: 10 times the vertical wind). (For interpretation of theticle.)

F.-Y. Cheng et al. / Atmospheric Environment 78 (2013) 277e290286

reaching 500 ppb around midnight on 16/17 March. From 19 to 23March, the CO concentration was in the range of 100e300 ppb,which was lower than the CO concentrations observed from 15 to18March. The temperature variationwas between 5 and 15 degreesCelsius (�C) most of the time, except in the late afternoon of 19March, when the temperature was between 0 and 4 �C due to thestrong influence of the Asian continental outflow.

4.2. Distributions of fire spots

Fig. 7 is the fire locations detected from Moderate ResolutionImaging Spectroradiometer (MODIS) satellite data on 15,18, 20, and22 March 2007. On March 15, the biomass burning activities werevigorously taking places all over SE Asia and continued through outthe following days. On 18 March, the burning activities almostceased in Vietnam, Liao, Cambodia and southern Thailand due tothe intrusion of the strong NE monsoonal flow. The local residentsstopped the burning processes with the strong prevailing wind. On20 March, the numbers of burning activities reduced further.

4.3. Spatial distribution

Fig. 8 shows the distribution of the SLP layered with horizontalwind fields at 1400 LST from 15 to 22March 2007. Fig. 9 is similar toFig. 8 but shows the geopotential height and wind fields at 700 hPa.The discussion of the synoptic weather pattern during the studyepisode is divided into two parts. The first part focuses on the daysfrom 15 to 18 March. There was a continental high pressure systemresiding in east part of China on 15 March which moved south-eastward on 16 and 17 March. On 18 March, another high pressure

Fig. 13. Similar to Fig. 12 but across the longitude 101� E; the wind vector is for the

system formed and moved southeastward. The 700-hPa weathermap shows the eastward movement of the upper level trough. Overthe area of Taiwan, the upper level wind was affected by thesoutheasterly to westerly flow.

The second part focuses on the days from 19 to 22 March. A highpressure system formed and moved southward on 19 and 20March. Unlike the first part of the episode, the high pressure systemwas pushed further south and even intruded into the whole ofIndochina on 20 March. It began to move eastward on 21 March.The 700-hPa upper level plots indicate that a strong SE Asia highformed from 20 to 22 March. The location and existence of the SEAsia high have a moderate influence on the wind flow patterns inTaiwan area that could affect the air pollutants transporting pro-cesses from Indochina to the downstream Taiwan area.

During the study episode, a low SLP was simulated from 15 to 18March over Indochina.When the NEmonsoonal flows intruded intoIndochina, the SLP increased from 19 to 22 March.

Fig. 10 shows the near-surface air temperature distribution andwind fields over Indochina at 1400 LST on 15, 18, 20, and 22 March.On 15 March, the air temperature was quite high and a thermal-forcing-induced low pressure system formed over Indochina(refer to Fig. 8). On 18March, the NEmonsoonal flow prevailed overthe northeast side of Indochina and the temperature decreased. On20 and 22 March, with the intrusion of the NE flow, the tempera-ture fields decreased further over Indochina. A strong NE wind wassimulated over Indochina.

From 15 to 17 March, we can identify that a thermal-forcing-induced low pressure system formed in SE Asia, providing aconvective process which would uplift the near-surface biomass-burning pollutants to the upper level. Along with the upper level

v! and w! wind components ( v!: the wind vector along the cross-section line).

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westerly wind, the biomass-burning pollutants can be transportedto Taiwan after two to three days, as indicated by the observed dataat LABS showing the high CO concentration from 17 to 18 March.From 19 to 21March, due to the intrusion of the NEmonsoonal flowover Indochina, the surface pressure increased while the convectivemotions decreased.

Follow up the previous cluster classification result, for this casestudy, from 15 to 16March, C3 is identified; from 17 to 20March, C5is classified; and from 21 to 22 March, C1 is classified. Themaximum CO was observed from 17 to 18 March when the C5cluster is identified. The previous weather classification result alsoindicates under C5 synoptic weather condition, typically the highCO concentration would occur at LABS. This indicates the clusterclassification results are able to clarify the characteristics of thesynoptic weather patterns that are prone to cause high CO con-centrations at LABS.

4.4. Vertical cross-section over Indochina

Fig. 11 shows the topographical height distribution over Indo-china. The lines shown in the figurewere used for the vertical cross-

Fig. 14. Particles simulation results after (a) 24 h, (b) 48 h, (c) 72 h, and (d) 96 h of transpintervals thereafter.

sectional analysis. Fig. 12 is a cross-sectional plot of the verticalvelocity layered with and wind components along latitude 15� N at1400 LST on 15, 18, 20, and 22 March. On 15 March, the westerlywind was prevailing and an upward motion was simulated. On 18March, the upward motion was enhanced at longitude 103.5� E,where a convergent flow formed due to the intrusion of the NEmonsoonal flow on the east side meeting the westerly wind fromthe west side. On 20 March, the prevailing wind was completelydominated by the strong NE flow and the strength of the upwardmotion weakened.

The plot of vertical cross-section at 101� E is shown in Fig. 13.The southerly flow coming from the Gulf of Thailand prevailed on15 and 18 March, the flow was forced to lift due to the mountainblocking, and strong upward motions were simulated at the top ofthe mountains at latitude 16.5� N. The upward flows over themountainous areas were stronger on 15 and 18 March but weakeron 20 and 22 March.

The vertical cross-sectional analysis indicated that the thermal-forcing-induced lowpressure system favors the upwardmotions, asshown on 15 March. On 18 March, the westerly prevailing windmeeting the intruding NE flow enhanced the upward motion,

ort, respectively. The particles were released at 1200 LST on 14 March and at hourly

F.-Y. Cheng et al. / Atmospheric Environment 78 (2013) 277e290288

which had a magnitude even stronger than the upward motion on15 March. Besides the thermal-forcing-induced upward motions,the presence of the sloping terrain in the northern part of Indochinafurther supports the uplifting processes.

4.5. HYSPLIT modeling

The thermal-forcing-induced uplifting processes are consideredan important motion for the transport of biomass-burning emis-sions. To further investigate the causes of the higher CO concen-tration observed at LABS from 15 to 18 March but lower CO levelsfrom 19 to 22March, the National Oceanic and Atmospheric Agency(NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) model (Draxler and Rolph, 2012; Rolph, 2012) wasapplied to simulate the particle dispersion processes at ninedifferent locations (refer to Fig. 11 for point locations). Two experi-ments were designed. First, one hundred particles were released at1200 LST (0600 UTC) on 14 March 2007, and particle release wascontinued at hourly intervals. Fig.14 shows theparticle distributionsof the first experiment. Fig. 14aed presents the individual plotsshowing the particles’ locations after 24, 48, 72, and 96 h’ transport,respectively. The second experiment was similar to the first one,with a release time of 1200 LSTon 18March 2007 and the results are

Fig. 15. Similar to Fig. 14 but with a rele

shown in Fig. 15. For the sake of simplicity of the experimentaldesign, the deposition processes were not considered.

On 14 and 15 March, the high pressure system was over thecontinent and near East China, with less influence to Indochina. Thethermal-forcing-induced low pressure system over Indochina waswell developed, which favored the uplifting of the biomass-burningpollutants. Fig. 14 shows that the particles were lifted to the upperlevel 24 h after release and transported northeastward. Many par-ticles arrived in South China after 48 h and even reached Taiwanafter 72 h. For this experiment, the uplifted pollutants were able toreach a quite high altitude and were transported to the area ofTaiwan along with the upper level westerly wind. The experimentconfirmed that the high CO concentration observed on 17 and 18March at LABS originated from SE Asia.

On 18March, the uplifting process remained strong, as indicatedin Figs. 12 and13; however, the intrusion of the NE wind blew theparticles toward the western and southern sides in the boundarylayer. Particles released in second experiment had two majortransport routes; one was southwestward while the other wasnorthward and then continued toward southern China. Fig. 15bshows that on 20 March some particles released near the surfacelayer were dispersed southwestward when the strong NEmonsoonal flows dominated over Indochina and most particles

ase time of 1200 LST on 18 March.

F.-Y. Cheng et al. / Atmospheric Environment 78 (2013) 277e290 289

were uplifted to the upper level and advected northward. However,the uplifting processeswere confinedwithin the 3-kmheight due tothe weakened upward motions. On 21 March, the northward par-ticles were moving toward South China, with very few particlesarriving inTaiwan.Most particleswere dispersed toward the centralpart of China on 22March, when a SE Asian highwas located near tothe Taiwan area (refer to Fig. 9 for the 700-hPa weather chart) andcould deflect the particles toward the central part of China.

The particle dispersion modeling demonstrates that during thefirst period of the episode, the particles emitted from Indochinawere transported northeastward to the downstream Taiwan area.During the second half of the episode, due to the intrusion of the NEmonsoonal flows into SE Asia, some particles were advectedsouthwestward while most of the particles were transported to-ward the central part of China. The location of the SE Asian high alsomoderately deflected the transport path of the particles.

5. Summary

To study the role of the atmospheric environment in causing thehigh CO concentrations observed at a high mountain station, LABSin Taiwan, the two-stage cluster method was applied for synopticweather classification using WRF simulation results for the springseason (February to May) from 2007 to 2010. In addition, a 9-kmWRF simulation was performed from 13 to 26 March 2007 tofurther investigate the synoptic weather patterns that would favorthe LRT of CO from Indochina to the downstream Taiwan area.

The results of cluster analysis clearly distinguished the majorsynoptic weather systems occurred during the spring season suchas the Asian continental anticyclone, the subtropical Pacific high,the low pressure system over Indochina and the southwesterlymonsoonal flow. The high CO concentrations observed at LABSappear in C1, C3 and C5 clusters that exist mostly in active biomass-burning months. In cluster C5, the intensity of the continental an-ticyclone is the strongest. Alongwith the southeastwardmovementof the high pressure system, the cluster C5 migrates into C1 inwhich a strong NE monsoonal flow prevails in SE Asia. The SLP isalso the highest in C1 over Indochina, which could reduce thethermal-forcing-induced upwardmotions as well as the uplifting ofthe biomass-burning emissions.

During the biomass-burning season, the thermal-forcing-induced upward motions existed most of the time except whenthe NE monsoonal flow encroached on Indochina. The 2007 casestudy indicated that not only the existence of the thermal-forcing-induced low pressure system which formed in Indochina but alsothe presence of the high terrain located in the northern part of SEAsia forced the uplift of the biomass-burning emissions.

The thermal low pressure system that formed on 15March 2007over Indochina enhanced the upward movements that uplifted thenear-surface biomass-burning pollutants to the upper level, andalong with the westerly to southwesterly flow the pollutants weredispersed to the Taiwan area. The results of HYSPLIT modelingfurther confirmed that the high CO level observed on 17 and 18March at LABS originated from SE Asia.

From 19 to 22 March 2007, the CO concentration observed atLABS was lower than that observed from 15 to 18 March. Due to theintrusion of the NE monsoonal flow into Indochina, the thermalforcing as well as the upward motions weakened, and the particleswere confined below 3 km in height. The NE wind advected someparticles toward the southwest directionwithin the boundary layerwhile most of the particles were transported northward toward thearea of South China; as a result very few particles arrived in theTaiwan area.

In this study, a different transport mechanism that favors COtransport from Indochina to the downwind Taiwan area is

identified, e.g. the thermal-forcing-induced low pressure systemand the presence of the high terrain enhance the upward motionsthat favor the particle-uplifting processes over Indochina. On theother hand, the intrusion of the NE monsoonal flow over Indochinawould weaken the upward motion and disperse the particlessouthwestward. It seems that the location of the SE Asia high has amoderate effect on the transport path in the upper level. The resultsof this study can help us to better understand the relationshipbetween the synoptic weather patterns and the high CO loading atLABS in Taiwan due to the LRT of the biomass-burning emissionsfrom Indochina.

Acknowledgments

This study was conducted under the research project entitled“Study the effect of boundary layer meteorology on the biomassburning air pollutant in East Asia and Taiwan area” supported bythe National Science Council, Taiwan, NSC-100-2111-M-008-001.The authors acknowledge Dr. Neng-Huei Lin as the principalinvestigator of the LABS project. The authors gratefully acknowl-edge the NOAA Air Resources Laboratory (ARL) for the provision ofthe HYSPLIT transport and dispersion model and/or READY website(http://ready.arl.noaa.gov) used in this publication.

References

Chan, C.Y., Chan, L.Y., Harris, J.M., Oltmans, S.J., Blake, D.R., 2003. Characteristics ofbiomass burning emission sources: transport and chemical speciation ofenhanced springtime ozone profile of the troposphere over Hong Kong. Journalof Geophysical Research 108, 4015.

Chen, T.-C., Yen, M.-C., Huang, W.-R., Gallus, W.A., 2002. An east Asian cold surge:case study. Monthly Weather Review 130, 2271e2290.

Chen, F., Dudhia, J., 2001. Coupling an advanced land surface-hydrology model withthe Penn State-NCAR MM5 modeling system. Part I: model implementation andsensitivity. Monthly Weather Review 129, 569e585.

Chuang, M.T., Fu, J.S., Jang, C.J., Chan, C.C., Ni, P.C., Lee, C.T., 2008. Simulation of long-range transport aerosols from the Asian continent to Taiwan by a southwardAsian high-pressure system. Science of Total Environment 406, 168e179.

Draxler, R.R., Rolph, G.D., 2012. HYSPLIT (HYbrid Single-Particle Lagrangian Inte-grated Trajectory) Model. NOAA Air Resources Laboratory, Silver Spring, MD.Access via NOAA ARL READY Website. http://ready.arl.noaa.gov/HYSPLIT.php.

Dudhia, J., 1989. Numerical study of convection observed during the wintermonsoon experiment using a mesoscale two-dimensional model. Journal ofAtmospheric Science 46, 3077e3107.

Eder, B., Davis, J., Bloomfield, P., 1994. An automated classification scheme designedto better elucidate the dependence of ozone on meteorology. Journal of AppliedMeteorology 33, 1182e1199.

Hong, S.-Y., Noh, Y., Dudhia, J., 2006. A new vertical diffusion package with explicittreatment of entrainment processes. Monthly Weather Review 134, 2318e2341.

Huang, K., Fu, J.S., Hsu, N.C., Gao, Y., Dong, X., Tsay, S.C., Lam, Y.F., 2012. Impactassessment of biomass burning on air quality in Southeast and East Asia duringBASE-ASIA. Atmospheric Environment. (Online express).

Kain, J.S., Fritsch, J.M., 1993. Convective Parameterization for Mesoscale Models: TheKain-Fritsch Scheme. In: The Representation of Cumulus Convection in Nu-merical Models. American Meteorology Society, pp. 165e170. Meteor Mono-graph, No. 24.

Langenfelds, R.L., Francey, R.J., Pak, B.C., Steele, L.P., Lloyd, J., Trudinger, C.M.,Allison, C.E., 2003. Interannual growth rate variations of atmospheric CO2 andits d13C, H2, CH4, and CO between 1992 and 1999 linked to biomass burning.Global Geochemical Cycles 16, 1048.

Lin, C.Y., Hsu, H.M., Lee, Y.H., Kuo, C.H., Sheng, Y.F., Chu, D.A., 2009a. A new transportmechanism of biomass burning from Indochina as identified by modelingstudies. Atmospheric Chemistry and Physics 9, 7901e7911.

Lin, N.-H., Wang, J.-L., Lee, C.-T., Sheu, G.-R., 2009b. Chemical and Physical Mea-surements at Lulin Atmospheric Background Station (LABS, 2862m MSL) inTaiwan. Short abstract, 7th AOGS annual meeting, Singapore, 11e15 August.

Liu, H., Jacob, D.J., Bey, I., Yantosca, R.M., Duncan, B.N., 2003. Transport pathways forAsian pollution outflow over the Pacific: interannual and seasonal variations.Journal of Geophysical Research 108, 8786.

MacQueen, J.B., 1967. Some Methods for Classification and Analysis of MultivariateObservations. In: Proceedings of the 5th Berkeley Symposium on MathematicalStatistics and Probability, vol. 1, pp. 281e297.

Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J., Clough, S.A., 1997. Radiativetransfer for inhomogeneous atmospheres: RRTM, a validated correlated-kmodelfor the longwave. Journal of Geophysical Research 102 (D14), 16663e16682.

Ngan, F., Byun, D.W., 2011. Classification of weather patterns and associatedtrajectories of high-ozone episodes in Houston-Galveston-Brazoria area

F.-Y. Cheng et al. / Atmospheric Environment 78 (2013) 277e290290

during the 2005/06 TexAQS-II. Journal of Applied Meteorology and Clima-tology 50, 485e499.

Ou Yang, C.-F., Lin, N.-H., Wang, S.-H., Sheu, G.-R., Lee, C.-T., Wang, J.-L. Char-acteristics of atmospheric carbon monoxide at a high-altitude mountainbackground station in East Asia. Atmospheric Environment, Submitted forpublication.

Rolph, G.D., 2012. Real-time Environmental Applications and Display System(READY). Website. NOAA Air Resources Laboratory, Silver Spring, MD. http://ready.arl.noaa.gov.

Sheu, G.-R., Lin, N.-H., Wang, J.-L., Lee, C.-T., Ou Yang, C.-F., Wang, S.-H., 2010.Temporal distribution and potential sources of atmospheric mercury measured

at a high-elevation background station in Taiwan. Atmospheric Environment44, 2393e2400.

Ward, J., 1963. Hierarchical grouping to optimize an objective function. Journal ofthe American Statistical Association 58, 236e244.

Yen, M.-C., Peng, C.-M., Chen, T.-C., Chen, C.-S., Lin, N.-H., Tzeng, R.-Y., Lee, Y.-A.,Ling, C.-C., 2013. Climate and weather characteristics in association with theactive fires in northern Southeast Asia and spring air pollution in Taiwan during2010 7-SEAS/Dongsha Experiment. Atmospheric Environment 78, 35e40.

Yu, J.Y., Lin, N.H., Hsieh, H.H., Lin, T.S., 2008. A cluster analysis of the springtimeforward trajectories arising from Southeast Asia and the climate influence.Journal of Atmospheric Science 36 (4), 287e300.