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Volume 10: Atmospheric Science (AS)

Copyright © 2009 by World Scientific Publishing Co. Pte. Ltd.

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Editor-in-Chief: Wing-Huen Ip

Volume 10: Atmospheric Science (AS)

Editor-in-Chief: Jai Ho Oh

Editor: Gyan Prakash Singh

Volume 11: Hydrological Science (HS)

Editor-in-Chief: Namsik Park

Editors: Joong Hoon Kim

Eiichi Nakakita C. G. Cui Taha Ouarda

Volume 12: Ocean Science (OS)

Editor-in-Chief: Jianping Gan

Editors: Minhan Dan

Vadlamani Murty

Volume 13: Solid Earth (SE)

Editor-in-Chief: Kenji Satake

Volume 14: Solar Terrestrial (ST)

Editor-in-Chief: Marc Duldig

Editors: P. K. Manoharan

Volume 15: Planetary Science (PS)

Editor-in-Chief: Anil Bhardwaj

Editors: Yasumasa Kasaba

Paul Hartogh C. Y. Robert Wu Kinoshita Daisuke Takashi Ito

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The Editors of Volume 10 would like to acknowledge the following referees who had helped review the papers published in this volume:

Prof. Dongsong Sun

Dr. V. Rao Kotamarthi

Prof. Choo Hie Lee

Dr. (Mrs.) Ashwani Kulkarni

Prof. Jun Mustsumoto

Prof. Qian Yongfu

Dr. Tomoaki Nishizawa

Prof. Hu Hanling

Dr. Jiangyu Mao

Dr. Kripalani

Prof. Bimblecombe

Dr. Zhou Tianjun

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Editors v

Reviewers vii

Seasonal Variation in the Structure of QTP

Atmospheric Heat Source in 1961—2001 1

Zhong Shanshan, He Jinhai, Guan Zhaoyong and Liu Xuanfei

Rainfall Over Thailand During ENSO (1997-2000) 11

Wonlee Nounmusig and Prungchan Wongwises

Temporal and Spatial Variation of Cloud Measured with a Portable Automated Lidar 19

Tatsuo Shiina, Toshio Honda, Nobuo Takeuchi, Gerry Bagtasa, Hiroaki Kuze, Akihiro Sone, Hirofumi Kan and Suekazu Naito

Satellite-Observed 3D Moisture Structure and Air-Sea Interactions During Summer

Monsoon Onset in the South China Sea 27

Yongsheng Zhang and Tim Li

East Asian Summer Monsoon and the Rainfall in East China 41

Lu Xinyan, Zhang Xiuzhi and Chen Jinnian

Formation of Tropical Cyclone Concentric Eyewalls by Wave-Mean Flow Interactions 57

Jiayi Peng, Tim Li and Melinda S. Peng

Tropical Circulation Indices and Performances of Indian Summer Monsoon Rainfall 73

The Tropical Pacific—Indian Ocean Temperature

Anomaly Mode and its Impact on Asian Climates 83

Yang Hui and Li Chongyin

Boundary Layer Phenomena Observed by Continuously Operated, Temporary

High-Resolution Lidar 99

Nobuo Takeuchi, Gerry Bagtasa, Nofel Lagrosas, Hiroaki Kuze, Suekazu Naito, Makoto Wada, Akihiro Sone, Hirofumi Kan and Tatsuo Shiina

A Mie—Rayleigh-Sodium Fluorescence Lidar System for Atmospheric Detecting 115

Anthropogenic Aerosol Radiative Forcing in the Indo-Gangetic Basin 123

Sagnik Dey and S. N. Tripathi

Precise Measurement of Polarization Plane Rotation of Propagating Beam Due to Atmospheric Discharge 137

Tatsuo Shiina, Toshio Honda and Tetsuo Fukuchi

Characteristics for Optical Properties of Background Aerosols, Water, and Dust Clouds Measured by Using Lidar Over Chung-Li, Taiwan 147

A High-Resolution Simulation of Convective-Scale Transport of Dust Aerosol and its Representation in Cloud-Resolving Simulations 161

Tetsuya Takemi

Ice-Nucleating and Optical Properties of Ice

Cloud Seeded by Dimethyl Sulfoxide (DMSO) 177

L. N. Biswas, A. Hazra, P. Maiti, V. Mandal, U. K. De and K. Goswami

Impact Assessment of Global Temperature Perturbations on Urban and Regional Ozone

Levels in South Texas 197

Jhumoor Biswas, Kuruvilla John and Zuber Farooqui

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Advances in Geosciences Vol. 10: Atmospheric Science (2007) Eds. J. H. Oh and G. P. Singh © World Scientific Publishing Company



Key Laboratory of Meteorological Disasters of Jiangsu, Nanjing University of Information Science and Technology,

Nanjing, China 210044

ECMWF daily reanalysis is applied to investigate 1961—2001 heat source/sink and the climate features in relation to the atmospheric heat distribution over the QTP (Qinghai—Tibetan Plateau) by means of the "inverse algorithm." Results suggest that (1) in March—September (October—February), the QTP acts as a heat (cold) source, the strongest being in June (December). The heating effects of the QTP are asymmetric in the seasons; (2) as shown in the heating vertical profile, the maximum heat source layer occurs dominantly between 500 and 600 hPa, but with the season-dependent heating strength and depth, and, in contrast, the cold source has its maximum layer and intensity varying as a function of time; (3) the horizontal distribution of the heat sources throughout the troposphere (Qi) (from surface to 100 hPa) is complicated, displaying noticeable regionality; (4) since 1979 the seasonal variability of the heat source has shown climate transition signals, as clearly seen in 1990/1991.

1. Introduction

The QTP is a region higher in elevation and more complicated in surface feature than any other area in the world, called a "third pole" next to the Arctic and Antarctic in climate. The tremendous prominence of the QTP has changed the pattern of Asian climate, exerting pronounced effects on atmospheric circulations and climate worldwide.1

Abnormal change in the QTP thermal condition in the atmosphere bears a close relation to East-Asian circulations,2 acting as an indicator of summer precipitation over the Jiang-Huai valley, South China and North China.3'4 When the QTP heat source is strengthened, the rainfall increases in the upper Yangtze and Huaihe valleys as opposed to Southeast and North China. Around seasonal transition, the differences in the source/sink over the QTP with its vicinity are one of the causes of heavy flood/drought happening in the Jiang-Huai valley.5'6

As regards the effects of the QTP thermal role and its ensuing moist process upon monsoon features, numerous Chinese researchers have demonstrated7'8 that the variation in nonadiabatic heating in the transition early in summer gives rise to the change in land-sea thermal contrast, thus providing a favorable background for Asian summer monsoon onset, which has significant impacts on the outbreak.9 As shown by Jiang and Luo,10 when the East Asian monsoon begins, the nonadiabatic heating is responsible for tropospheric explosive warming over the Southeast QTP, leading to change in temperature gradient southward of the east QTP, consequently resulting in the adjustment of wind field for the onset of the east Asian monsoon. Jian11 indicated that the May-June conspicuous warming due to nonadiabatic heating in the mid-higher troposphere over the east QTP is of much importance to the northward march and maintenance of the summer monsoon. Differences in space/ time distribution of QTP summer rainfall also cause variations in the spatial/temporal distribution of the heat sources over the QTP and its neighborhood — the variations make atmospheric circulation change accordingly, finally leading to the difference in the onset time of the monsoon.12 Although monsoon researchers, domestic and foreign, diverge about how the QTP sensible heating affects the summer monsoon onset, undoubtedly, the QTP heating represents one of the mechanisms of the monsoon onset.

For lack of observations, how to obtain correct calculation is the linchpin of studies. The 1961-2001 ECMWF (ERA hereafter) daily reanalysis was employed to calculate the heat source features. The atmospheric apparent heat source Q1 was found by the use of the "inverse algorithm" developed by Yanai.13 Q1 comprised three terms: local term, advection term, and vertical transport term.

2. Regional Mean Climate Condition Due to the QTP Heat Sources

To gain insight into the tropospheric thermal regime 1961-2001 {Q1)-associated mean climate condition was analyzed. Over 1961-2001, within the region of 3000 m, 41-yr monthly climate mean conditions of {Q1) were shown by full line with open circles (Fig. 1), indicating that in MarchSeptember, {Q1) > 0 as the heat source began intensification from March, maximizing at 214 W/m2 in June, and decreasing thereafter; in October-February, {Q1) < 0 suggestive of a cold source, the strongest being in

Fig. 1. Regional averaged monthly (Qi) (W/m2) over the QTP in 1961-2001 (the solid line with open circles on for (Qi), the full line with solid circles upon for the local term, full line with open squares on for the advection term, and the full line with solid squares upon for the vertical transport term).

Fig. 1. Regional averaged monthly (Qi) (W/m2) over the QTP in 1961-2001 (the solid line with open circles on for (Qi), the full line with solid circles upon for the local term, full line with open squares on for the advection term, and the full line with solid squares upon for the vertical transport term).

December (about —84 W/m2). It follows that for (Qi), the yearly thermal regime displayed a longer period of it as a heat source (on the order of 7 months), with the value much higher (250% as strong in absolute value) compared to the cold source (214 vs —84 W/m2), exhibiting asymmetry of the annual cycle.

Comparing the three terms of (Qi), we found that the vertical transport term makes the greatest contribution to (Qi).

The QTP is responsible for noticeably lifting the heat source in the troposphere because of its great elevation. What does the height-varying heating profile look like over the Plateau? Figure 2 presents the height-evolving monthly mean Qi and its components in 1961-2001 over the QTP at an elevation of 3000 m.

The height-varying Qi is featured mainly by the opposite trend of intensity of the heat to cold source, and the whole process can be described as a "cylinder stator" of an engine in operation, with the piston representing source transition that divides the heat and cold source in vertical, as shown in the "steam chests" of the engine. As time goes on, the volume (thickness) is changing constantly for both. As the heat source expands its volume, i.e. the piston goes up, the thickness of the cold source diminishes and

Fig. 2. The vertical profile of monthly mean Q1 and its components at 3000 m level over the QTP, with solid line for Q1, long dashed for the local term, dotted line for the advection term, and dash dotted for the vertical transport term (Units: K day-1).

vice versa. As indicated by the vertical profile, the "piston" reaches its top in July-August when the heat source is the deepest. Conversely, as the "piston" has its lowest position, the cold source covers the greatest depth in October-December, and the troposphere is nearly under the control of the cold source.

The strongest heat source layer occurs almost at 500-600 hPa, except for its intensity that peaks in June and decreases toward January and December (as shown in Fig. 3), with the cold source dominating practically all atmospheric levels in December. Note that the height and vigor of the maximal cold source layer change with time. In June-August, the cold source layer is weak in intensity and shallow in depth, reaching its greatest thickness in cold months (NDJ), with its highest strength at 200 hPa.

The vertical profiles of monthly Q1 and its components (Fig. 2) show that the height-dependent local term value is considerably smaller

compared to the other two, and so we ignored it. Consequently, the terms affecting Q1 are the terms of advection and vertical transport. The summer Q1 profile is similar to that of the vertical term that makes the dominant contribution thereto in summer. In winter the atmospheric cold source is produced as a result of the terms of advection and vertical transport, opposite in phase, with the latter marginally larger.

3. The Horizontal Distribution of (Qi)

The heat source distributed throughout the extent (Qi) over the QTP is given in Fig. 4. In January (figure omitted), the cold source covers almost the entire region at 3000 m level of the QTP, with the core in the Southeast QTP. In February and March, the QTP cold center remains constant, with its domain contracted southward. In April, the whole QTP is under the effect of a heat source, centered on 85°E and 30o-35°N, whose intensity reaches >150 W/m2. The former cold source center has been covered with a heat source, with its value lower compared to the surroundings. With 90°E as the division, we see that the eastern intensity is lower than the western one. In May (figure omitted), the QTP heat source continues to intensify, with its center remaining at the western QTP west of 90°E and the 200 W/m2 core stretches Southeast. In June, the eastern heat source experiences sudden reinforcement, arriving at >200 W/m2 as its central value except that the 200 W/m2 isoline takes a more northern position in the western than in the eastern QTP, suggesting that the western is stronger

Fig. 4. Monthly mean {Q\) distribution in horizontal over the QTP (Units: W/m2).

than the eastern heat source. At this time, the QTP heat source is being the strongest in the months. In July (figure omitted), concurrently with the weakening of the source to the north of the Bay, the QTP 200 W/m2 isoline begins a southward withdrawal. As August arrives, concurrently with the southward retreat of the 200 W/m2 contour, a break occurs to the heat source at the border between the Himalayas and the North and South (Hengduan) ranges. In September (figure omitted), in pace with the southward contraction of the Indo-China heat source, the counterpart of the western QTP begins to weaken swiftly, leading to the 100 W/m2 contour Northeast-Southwest directed, implying the eastern heat source stronger than the western one of the QTP, with the cores located, respectively, in the western Sichuan Basin and around the eastern Himalayas. As October arrives, there appears a cold source in the west and a weak heat source in the east of the QTP. As November (figure omitted) sets in, the QTP is under the control of cold sources distributed in a similar pattern to that in January. In December, the cold source is the strongest, centered at the southeast QTP, with its values lower than —150 W/m2.

To sum up, (Q1) horizontal distribution shows that from April to August, the heat source is stronger in the west than in the east, with the contour located more northward in the west than in the east. In Spring, the western heat source intensifies rapidly and not until May-June does the eastern one do so. The center of 200 W/m2 appears in May in the west, but in June in the east. Starting from July, the heat sources begin weakening southward, with the western one abating quickly. The heat source changes into a cold one in the west (east) in October (November).

4. The {Q1) Space/Time Variations

Only the 1979-2001 data were used for EOF study. The (Q1) was separated into four seasons, and the mode of greater variance contribution (EOF1) was taken out for discussion, as shown in Fig. 5.

In Spring, the (Q1) spatial pattern of EOF1 displays anti-phase change in the central-Northeast and Southeast QTP, the variability center value in the central source being twice as high as in the Southeast counterpart. From the time series, we see that a positive (negative) amplitude occurs in

Fig. 5. EOF analysis of the QTP (Q1), giving the EOF1 Spring space pattern in (a), the timeseries in (b), the Summer pattern in (c), and the timeseries in (d).

1981-1990 (1991-2001), suggesting that the trend of anomalous change in the heat source occurs over 1990/1991. Prior to 1990 the central-Northeast QTP is inside the positive anomaly variability center as opposed to the situation after the year, with an opposite change in the Southeast source.

In summer, (Q\) EOF1 shows a see-saw form divided by 32.5°N, positive in the north and negative values in the south. The maximal variability center was not over the QTP but to the south, where there were three cores. The temporal sequence also indicates the climate change of the anomalous variation in the heat source, with positive (negative) values in 1981-1987 (1994-1998), with 1988-1993 as the transition stage, i.e. south of 32° N there occurred negative anomaly of variability in summer in the 1980s and positive anomaly was from the end of the 1980s to early 1990s.

The EOF1 Winter/Autumn space patterns are similar to that of Spring (figure not shown), showing the anti-phase distribution between the central and Northeast and Southeast QTP, with the 1990/1991 as the climate transition year.

It follows that the EOF1 — given seasonal (Q\) indicates the climate transition, differing in that the variability center is kept over the QTP in all but summer seasons and it is on the south side of the QTP in Summer, i.e. over the Indo-China and northern Indian Peninsula. This may be due to the fact that the summer QTP heat source is not an independent center but part of the source in the Indo-China and Indian Peninsula as well as the Bay of Bengal in between.

5. Conclusions

The study calculated heat source and heat sink over the Tibetan Plateau and its vicinity (QTP) during 1961-2001 using the ERA daily reanalysis and the "inverse algorithm," and discussed the climate regimes linked to the thermal source over the QTP.

Some reasonable conclusions are obtained, that is, the region over the QTP with the height more than 3000 m above the sea level acts as a heat source, and as a heat sink during October-February. The heat source lasts for 7 months in the whole atmospheric extent and much stronger than the sink in the wintertime. Therefore, the heating effects of the QTP are asymmetric in the seasons. We also discussed the space extent in the vertical and time variation of the heat source and sink over the QTP. It is found that during April-August, the heat source is stronger in the west than in the east. As Spring arrives, the western heat source increases rapidly while the eastern one begins to rapidly increase during May-June, with the 200 W/m2 core value appearing in May for the west and in June for the east. The QTP heat source starts to decrease and withdraw southward from July. The western heat source abates faster, changing into a heat sink in October, 1 month earlier compared to the eastern one. It is depicted that since 1979 the seasonal variability of the heat source has shown climate transition signals, as clearly seen in 1990/1991.


The work was supported by a key item of National Natural Science Foundation of China (40633018) and graduate student innovation planning project in Jiangsu of China in 2006.


1. G. X. Wu, Y. M. Liu, X. Liu, A. M. Duan and X. Y. Liang, How the heating over the Tibetan Plateau affects the Asian climate in summer, Chin. J. Atmos. Sci. 29(1) (2005) 47-56.

2. A. M. Duan, Y. M. Liu and G. X. Wu, Heat condition over Qinghai-Tibet Plateau in Apr.-Jun. and its effect on east Asia precipitation in midsummer and abnormal atmospheric circulation, Chin. Sci. (D) 33(10) (2003) 997-1004.

3. P. Zhao and L. X. Chen, Climate characteristics of Qinghai-Tibet Plateau atmospheric heat source in 35 years and its relation to Chinese precipitation, Chin. Sci. (D) 31(4) (2001) 327-332.

4. S. R. Zhao, Z. S. Song and L. R. Ji, Heating effect of the Tibetan Plateau on rainfall anomalies over North China during rainy season, Chin. J. Atmos. Sci. 5 (2003) 881-893.

5. Y. Q. Lu and Y. F. Gong, Atmospheric heat source/sink change characteristics over Qinghai-Xizang Plateau and its vicinity region in summer of 2001 and 2003, Plateau Meteorol. 25(2) (2006) 195-202.

6. Y. Zhao and Y. Qian, Relationships between the surface thermal anomalies in the Tibetan Plateau and the rainfall in the Jianghuai area in summer, Chin. J. Atmos. Sci. 31(1) (2007) 145-154.

7. Q. G. Zhu and J. L. Hu, Numerical experiments on the influences of the Qinghai-Xizang Plateau topography on the summer general circulation and the Asian summer monsoon, J. Nanjing Instit. Meteorol. 16 (1993) 120-129.

8. J. H. He, J. Li and Q. G. Zhu, Sensitivity experiments on summer monsoon circulation cell in East Asia, Adv. Atmos. Sci. 6 (1989) 120-132.

9. X. Liu, G. X. Wu, Y. M. Liu and P. Liu, Diabatic heating over the Tibetan Plateau and the seasonal variations of the Asian circulation and summer monsoon onset, Chin. J. Atmos. Sci. 26(6) (2002) 781-793.

10. N. B. Jiang and H. B. Luo, Effects of the heating of the Tibetan Plateau on the onset of the east Asian summer monsoon, Acta Scientiarum Naturalium Universitatis Sunyatseni 35(supp1.) (1996) 194-199.

11. M. Q Jian and H. B. Luo, Heat sources over Qinghai-Xizang Plateau and surrounding areas and their relationships to onset of SCS summer monsoon in 1998, Plateau Meteorol. 20(4) (2001) 381-387.

12. Y. F. Gong, L. R. Ji and T. Y. Duan, Precipitation character of rainy season of Qinghai-Xizang Plateau and onset over east Asia monsoon, Plateau Meteorol. 23 (2004) 313-322.

13. M Yanai, C. Li and Z. S. Song, Seasonal heating of the Tibetan plateau and its effects on the evolution of the Asian summer monsoon, J. Meteorol. Soc. Jpn. 70(1) (1992) 319-350.

Advances in Geosciences Vol. 10: Atmospheric Science (2007) Eds. J. H. Oh and G. P. Singh © World Scientific Publishing Company


WONLEE NOUNMUSIG* and PRUNGCHAN WONGWISES The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Tungkru, Bangkok 10140, Thailand * [email protected] corn.

In this chapter, the yearly mean rainfall taken from the Thai Meteorological Department during 1972—2001 in each region of Thailand was analyzed comparing with 30 years' averaged rainfall in order to study the influence of ENSO. The rainfall in Thailand during ENSO 1997—2000 was selected as the case study. The results show that the amount of rainfall in most regions of Thailand during 1997/1998 (El Nino) is less than the long-term mean, while the amount of rainfall is strongly more than long-term mean for the whole Thailand in La Nina 1999/2000. The amount of rainfall during 1997-2001 shows the strong anomalies in early rainy season (May-June). Moreover, the Regional Atmospheric Model System (RAMS) version 6.0 is used to simulate the rainfall pattern in May and June 1997 and 1999. The trends of model results agree well with the observed data analysis.

1. Introduction

Thailand is situated in the southwestern part of Indo-Chinese Peninsula between latitudes 5° 37'N to 20° 27'N and longitudes 97° 22'E to 105° 37'E. The climate of Thailand is influenced by the southwest monsoon and northeast monsoon, which can be classified generally into three seasons: mid-October to mid-February of the next year is the moderate winter season, mid-February to mid-May is the summer season, and mid-May to mid-October is the rainy season. Her national economies are mainly the agricultural products, which largely depend on the weather and climate conditions. Favored by the southwest monsoon, plenty of rainfall is precipitated all over the tropical country of Thailand during the rainy season. This is the normal case. But in some abnormal years, stronger or weaker southwest monsoon may cause flood or droughty disaster, which affect the agricultural products.

The variation in rainfall is caused by many factors. One of the interesting factors is the ENSO phenomenon. Although the center of this event is in the equatorial Pacific, many researchers have indicated that the impacts of this phenomenon on the climates cover more than 75% of the earth.1'4'5'8 Some of the examples are the change in the pattern of floods, droughts, cyclone, and severe storm activity.3'6'13 There are two phases of ENSO: warm events (known as El Nino) and cold events (known as La Nina). In general, when El Nino event occurs, the first visible impact is an increase in rainfall in the eastern Pacific, including parts of South America, and a decrease in rainfall in the western Pacific locations such as Australia, Indonesia, Southeast Asia, and the Philippines. The amount of rainfall opposite to El Nino event, is on the La Nina event.2'7 That is, a decrease in rainfall in the eastern Pacific and parts of South America, and an increase in rainfall in the western Pacific.

Recently, the regional climate model is increasingly being used for simulating the characteristic of rainfall. In this study, the case studied in ENSO during 1997-2000 was chosen to investigate the characteristic of rainfall in each region of Thailand. There are two events of ENSO during this period: El Nino year 1997-1998 and La Nina year 1999-2000. Basically, the area average is used to summarize the rainfall during the rainy season of Thailand. The anomaly months were simulated by the RAMS model.

2. Methodology

2.1. The observed data analysis

The monthly rainfall data during 1 January 1972-31 December 2001 are taken from Thai Meteorological Department. These data were selected to study the amount of rainfall over Thailand during the ENSO year and the normal year. All the observed stations were scattered in six regions including 16 stations in northern, 16 stations in northeastern, 10 stations in central, 10 stations in eastern, 14 stations in southern (east coast), and 5 stations in southern (west coast) part of Thailand.

2.2. Model simulation

The regional climate model based on the RAMS9 version 6.0, developed at Colorado State University, was employed for these experiments. The basic equations were a set of nonhydrostatic compressive dynamic equations and a thermodynamic equation. A Kain-Fritsch cumulus parameterization scheme is used to simulate convective rainfall. Other parameterizations are standard for a simulation of this type. Surface fluxes of heat and moisture are represented through the Land Ecosystem Feedback land surface model.14 The model domain has horizontal dimensions of 82 x 82 grid points with a grid spacing of 60 km, encompassing the Thailand and some part of Indian Ocean. The model uses a vertically stretched grid with a maximum vertical grid spacing of 1000 m. The minimum vertical grid spacing is 100 m with a vertical stretch ratio of 1.2. There are 30 grid points in the vertical one. The polar stereo-projection is used, and the center of the domain is located at 13.5N, 100E (Fig. 1). The meteorological initial and boundary conditions were interpolated from the National Centers for Environmental Prediction (NCEP) daily reanalysis (available online at

Fig. 1. Domain and topography in the model.

3. Results and Discussion

3.1. Observed monthly rainfall during rainy season

The ENSO during 1997-2000 were considered. During 1997-1998 El Nino started in March 1997 when the highest sea surface temperature occurred in September 1997, and the El Nino event ended in June 1998. Later, La Nina occurred in June 1998 and persisted through 2000.4 The abnormal rainfall during the rainy season during 1997-2000 was calculated from the difference in the monthly rainfall with long-term mean as shown in Fig. 2. It can be seen that the abnormal rainfall during ENSO year 1997-2000 is strong during the early and end of the rainy season. During this period, the monthly rainfall during El Nino (1997) shows negative value (below normal), while positive value (above normal) was found in La Nina (19992000) in most of the regions in Thailand. The decrease or increase in rainfall during this period was caused by the movement of ITCZ, and is strong in monsoon. During El Nino (1997), it was found that the ITCZ moves rapidly within a short time in Thailand and becomes weak in monsoon, whereas in La Nina year (1999-2000) the ITCZ moves early to Thailand during the onset of early monsoon and reaches the peak in monsoon.10-12

250.00 200.00 150.00 100.00 -50.00 0.00 -50.00 -100.00 -150.00 -200.00 -250.00

Middle Rainy season

I North ■ Northern □ Central □ East □ South (east coast) □ South(west coast)

Fig. 2. The rainfall anomalies (mm) during rainy season 1997—2000.

Middle Rainy season

I North ■ Northern □ Central □ East □ South (east coast) □ South(west coast)

Fig. 2. The rainfall anomalies (mm) during rainy season 1997—2000.

3.2. Model simulation

The rainfall in each rain gauge stations was divided in each region of Thailand by using area-average. The choice of gauges was by selected stations of TMD which had complete data in May and June, 1997 and 1999. The time series of area-averaged daily total rainfall in six regions are compared with the observed value. The comparison is shown in Figs. 3(a)-3(f) for Northern, Northeastern, Central, Eastern, east coast

Northern Thailand

Northern Thailand

Northeastern Thailand

May, 1999

Northeastern Thailand

— Simulation --Observation

May, 1997

— Simulation --Observation

May, 1999



May, 1999

May, 1999



Central Thailand

May, 1997

Central Thailand

May, 1997 June, 1997 May, 1999 June, 1999

Fig. 3. The observed (dashed) and simulated (solid) area-averaged daily rainfall in 4 months for the six regions of Thailand.

May, 1997 June, 1997 May, 1999 June, 1999

Fig. 3. The observed (dashed) and simulated (solid) area-averaged daily rainfall in 4 months for the six regions of Thailand.

Eastern Thailand

Eastern Thailand

May, 1997 June, 1997 May, 1999 June, 1999

East coast of the Southern Thailand

East coast of the Southern Thailand

May, 1997 June, 1997 May, 1999 June, 1999

West coast of the Southern Thailand

West coast of the Southern Thailand

May, 1997 June, 1997 May, 1999 June, 1999

May, 1997 June, 1997 May, 1999 June, 1999

of Southern, and west coast of Southern Thailand, respectively. All the plots show that the simulated rainfall amounts in each region were always higher than the observed value. However, the trend of simulated rainfall agrees with the observed rainfall. Both simulated and observed rainfall of all regions of Thailand show that the amount of rainfall in La Nina 1999 during these 2 months is more than that in El Nino 1997.

4. Conclusion

The total rainfall in Thailand during 1997-1998 (El Nino) was less than normal, while in La Nina year 1999-2000, the total rainfall was more than normal. During El Nino 1997-1998, it can be seen that the anomaly of rainfall occurred during the early rainy season, while during La Nina 19992000, the abundance of rainfall started in April and the amount of rainfall was more than the long-term mean for the whole of Thailand except the Southern (east coast) part of Thailand. By using RAMS to simulate the rainfall in May and June in El Nino 1997 and La Nina 1999, the simulated monthly rainfall was overestimated, but the trend of simulated daily rainfall in the model agrees with the observational data. It indicates that the amount of rainfall in La Nina 1999 during these 2 months is higher than El Nino 1997.


This work was supported by the Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi. Special thanks are also due to Thai Meteorological Department (TMD) and the European Center for Medium Range Weather Forecasting (ECMWF) for the data supported.


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5. P. D. Jones, The influence of ENSO on global temperatures, Climate Monitor (1988) 80-89.

6. R. H. Kripalani and A. Kulkarni, Climatic impact of El Nino/La Nina on the Indian monsoon: A new perspective, Weather 52 (1997) 39-46.

7. K. K. Kumar, B. Rajagopalan and M. A. Cane, On the weakening relationship between the Indian monsoon and ENSO, Science 284 (1999) 2156-2159.

8. Z. X. Long and C. Y. Li, GCM modeling of the impacts of the ENSO on east Asian Monsoon activities, Acta Meteorologica Sinica 57(6) (1999) 663-671.

9. R. A. Pielke et al., A comprehensive meteorological modeling system — RAMS, Meteor. Atmos. Phys. 49 (1992) 69-91.

10. TMD, Rainy Season of Thailand for 1998 (Thai Meteoroogical Department, Thailand, 1998).

11. TMD, Rainy Season of Thailand for 1999 (Thai Meteoroogical Department, Thailand, 2000).

12. TMD, Rainy Season of Thailand For 2000 (Thai Meteoroogical Department, Thailand, 2001).

13. M. C. Wu, W. L. Chang and W. M. Leung, Impacts of El Nino; southern oscillation events on tropical cyclone landfalling activity in the Western North Pacific, J. Climate 17 (2004) 1419-1428.

14. R. L. Walko et al., Coupled atmosphere — biophysics-hydrology models for environmental modeling, J. Appl. Meteorol. 39 (2000) 931-944.

Advances in Geosciences Vol. 10: Atmospheric Science (2007) Eds. J. H. Oh and G. P. Singh © World Scientific Publishing Company



Faculty of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Japan


Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Japan

AKIHIRO SONE and HIROFUMI KAN Hamamatsu Photonics, 5000 Hirakuchi, Hamakita-ku Hamamatu-shi, Shizuoka-ken, 434-8601, Japan

SUEKAZU NAITO Chiba Prefecture Environmental Research Center, 1-8-8 Iwasakinishi, Ichikawa-shi 290-0046, Japan

A portable automated lidar (PAL) system, which conducts full-time operation and all-weather observation through the laboratory window, has been developed. Observations of long-term temporal and spatial dynamics of the atmosphere are described and the advantage of full-time operation is discussed.

1. Introduction

Atmospheric convection has an effect on cloud formation, and it leads to heavy rain or lighting strike. It also affects diffusion of suspended substances. Changes in the atmospheric convection due to the climate change may influence the large- and local-scale transformation of particles such as the yellow sand. In this context, it is essential to understand the temporal and spatial dynamics of the atmosphere, which cannot be monitored with conventional, fixed-point observation systems or meteorological satellites.

Lidar is an appropriate tool for monitoring time and spatial dynamics of the atmosphere, especially aerosols and clouds. Although various kinds of lidar systems have so far been developed, observations are limited in terms of time spans. Besides, observation directions are usually fixed both horizontally and vertically. These limitations originated from the system stability as well as the complication of system maintenance including the laser device.

A micro-pulse lidar (MPL), developed by Spinhirne in 1993, is a compact lidar system that provides easy operation and long-term observation.1 Using a laser-diode pumped laser of micro-joule output energy, MPL ensures the eye-safety features. Signal-to-noise ratio was improved by narrowing the receiver field-of-view (FOV). However, this makes it difficult to adjust the laser beam within the receiver's FOV. Since the same telescope is used to both transmit and receive the laser beam, a small amount of the emitted beam back-reflected from the beam splitter often damages the detector.

In this chapter, we describe a portable automated lidar (PAL) system, which we have developed to conduct full-time operation and all-weather observation through the laboratory window.2-4 The PAL system has an automated correction mechanism for misalignment of the overlap between the transmitted laser beam and the receiver FOV. Hence the system is able to operate in a stable and stand-alone way. In addition, we have recently installed the scanning mechanism by attaching a horizontal stage to the PAL system. This improvement contributes greatly to monitoring the two-dimensional structure of the atmosphere nearly instantaneously.

2. PAL System

The PAL system is a variation of MPL system. The system configuration is shown in Fig. 1 and its specifications are summarized in Table 1. Since the transmitted energy is 15 ^J, the system is nearly eye-safe at the expense of weak signals (lidar echo). To attain enough signal-to-noise ratio, the background light due to sky radiance must be eliminated with a narrow-bandwidth filter (0.5 nm) and a narrow FOV of 0.2mrad. At the same time it is essential to keep the good overlap between the laser beam and the telescope FOV. Misalignment of the overlap, however, sometimes occurs from changes in the ambient temperature and accidental disturbances. The system has the auto-alignment mechanism, in which the laser beam is scanned vertically and then horizontally within the receiver's FOV and the maximum in the return signal (a certain range near the peak of the A-scope) is sought every 15min.

The detector is a photo-multiplier operated in the photon counting mode (Hamamatsu photonics K.K. R1924P). The lidar echoes are

Fig. 1. System configuration of portable automated lidar.

Table 1. Specification of PAL.


LD pumped Nd:YAG Laser

Pulse power 15 /iJ

Wavelength 532 nm


Photo-multiplier (photon counting mode)



Aperture 20 cm diameter

Field of View 0.2 mrad


Resolution 24 m

Range 24 m

Averaging 10 or 20 s

accumulated by a scaler (Stanford Research Systems SR430). The spatial resolution is 24 m and the maximum observation range is 24 km (altitude 15 km). The observation is made though the vertical window of the laboratory, leading to the capability of measurement under all weather conditions. The observation data have been accumulated since the year 2004. The system status can be checked and the data can be downloaded through the Internet.

A built-in rotation stage for horizontal scanning has recently been installed. As the PAL system is fabricated as a monolithic structure including a laser head and a detector, the rotation stage was "inserted" under all the optical systems. The scanning observation of a range of ±25 degrees is conducted every hour, interrupting the continuous measurement for about 6min. The PAL system is operated in Chiba Prefecture Environmental Research Center, with its beam pointed northward at the elevation angle of 38 degrees. The center is located on the east of Tokyo bay, about 10 km south of Chiba University. There is an industrial area and a busy load on the seaside (west of the center).

3. PAL Observation

The main advantage of the continuous and long-term observation is capturing the local weather change that takes place in a time scale of several hours. Especially, the system can monitor the onset and recovery of bad weather conditions and changes in polluted airs. These features are largely dependent on the site locations and conditions (urban/rural/mountains/ waters). Two examples of characteristic results from the viewpoint of long-term cloud observation are shown in the following.

Figure 2 shows the result observed during 0-12 h local time on October 7, 2006. The weather map of Fig. 2(a)5 shows that the low

Time [hour]

Fig. 2. (a) Weather map over Japan on October 7, 2006. (b) 12-h cloud long-term observation result: October 7, 2006. Temp. 21°C, Hum. 35%.

pressure has moved northward passing along the east coast of Japan, involving stationary and cold fronts. The PAL data in Fig. 2(b) also show that the long-lasting rain from the day before stopped and the cloud gradually gained altitude. The PAL data shown here are all corrected by the squared distance. Relatively, large echo appeared under the cloud till 7:00 (local time) in the morning. On that day, temperature and humidity largely changed at 7:00 (local time). Wind direction was northwest, and its speed was 8m/s. The 10-h cloud elevation indicates the passage of highly developed low pressure. Figure 3 is the result observed during 0-12 on September 18, 2006. It was a windy day. Low clouds of less than 1 km altitude appeared during 0-5 h. They raised the altitude up to 2 km during 5-8 h. The lidar echo from these clouds was sparse and largely fluctuating in altitude. Sharp downturn of the cloud altitudes during 8-10 h was due to the rainfall.

Examples of long-term temporal motion of the atmosphere are shown in Fig. 4. Figure 4(a) is the result observed during 0-12 h local time on

Time [hour]

Fig. 3. 12-h cloud long-term observation result: September 18, 2006. Temp. 24°C, Hum. 82%.

Fig. 3. 12-h cloud long-term observation result: September 18, 2006. Temp. 24°C, Hum. 82%.

Time [hour]

Time [hour]

Fig. 4. 12-h atmosphere long-term observation results. (a) September 21, 2006. Temp. 24.7°C, Hum. 59%, (b) December 23, 2006. Temp. 11.9°C, Hum. 57%.

Fig. 4. 12-h atmosphere long-term observation results. (a) September 21, 2006. Temp. 24.7°C, Hum. 59%, (b) December 23, 2006. Temp. 11.9°C, Hum. 57%.

September 21, 2006. The atmospheric boundary layer and cloud were captured at the altitude of 2 km and 4 km, respectively. The structure was stable and showed little change till 9:00, while the relatively large echo appeared and raised its altitude from the ground during 9-12 h. In accordance with the change, cloud appeared at the altitude of 1.5-2 km. This condition continued till 16:00. Figure 4(b) shows the result observed during 0-12 h on December 23, 2006. The cloud appeared at the altitude of 6 km and lowered its altitude gradually from 0 to 6h. Another cloud appeared on the boundary layer at the altitude of 1.5 km starting from 6:00. The boundary layer reduced the altitude down to 0.3-0.5 km. Furthermore during 8-10 h, another cloud appeared on the lowered boundary layer. Obviously, those results demonstrate the benefit of long-term observation. The change in temperature, wind, and the local-climatological influence of the site location will also be reflected in the observation data.

The result of temporal and horizontal-scanning observations on July 2, 2007 is shown in Fig. 5. On the day, the cloudy weather from the preceding day gradually worsened and started to rain in the evening. Time-height indication result of Fig. 5(a) shows that cloud moved slowly in the altitude range of 1-1.5 km during 0-8 h. The cloud altitude lowered in 8-12 h, while another thin echo appeared under the cloud layer. It rained in 15-19 h (Chiba city).

Temperature-humidity variation shown in Fig. 5(c) and pressure-wind speed variation in Fig. 5(d) also indicate the same change in the atmosphere activity, particularly the change of humidity in 0-12 h and 15-19 h, and the change of pressure/wind speed in 15-19 h. The spatial distributions of

Fig. 5. (a) 24-h long-term observation result (b) 24-h horizontal scanning (c) temperature and humidity (d) pressure and wind speed data: 2 July, 2007.


Fig. 5. (a) 24-h long-term observation result (b) 24-h horizontal scanning (c) temperature and humidity (d) pressure and wind speed data: 2 July, 2007.


the lidar echo obtained by the horizontal scanning are shown in Fig. 5(b). Although the scanning data are also corrected for the squared distance, it is not corrected for the elevation angle. Thus, the graphs are plotted in the beam propagation distance. The basic features of cloud echoes agree well with the temporal variation in Fig. 5(a), while the spatial structures of cloud are clearly detected in 9-12 h and 12-15 h by virtue of the horizontal scanning for the first time. The advantage of the horizontal scanning in understanding the local atmosphere will be fully exploited by deducing three-dimentional spatial information. In Fig. 5(a), the horizontal scanning time periods of 6 min are shown with blanks. Alternatively, the scanning data can also be used as part of the temporal data, filling those blanks.

4. Summary

The PAL system has continued the uninterrupted, autonomous observations for nearly 4 years. The additional inclusion of the horizontal scanning capability enables us to apply the system to new types of targets: spread of industrial smokes and dust distributions from busy roads are good examples of such applications. The system will also be useful to elucidate yellow dust activity and the pollen density distributions. In the near future, we are planning to install multi-wavelength and multi-polarization capabilities to the PAL system.


1. Spinhirne, Micro pulse lidar, IEEE Trans. Geosci. Remote Sens. 31(1) (1993) 48-55.

2. N. Lagrosas et al., Correlation study between suspended particulate matter and portable automated lidar data, Aerosol Sci. 36 (2005) 439-454.

3. G. Bagtasa, N. Takeuchi, S. Fukagawa, H. Kuze, T. Shiina, S. Naito, A. Sone and H. Kan, Mass extinction efficiency for tropospheric aerosols from potable automated lidar and /-ray SPM counter, Proc. of 23rd International Laser Radar Conference 3P-30 (2006) 499-502.

4. G. Bagtasa, C. Liu, N. Takeuchi, H. Kuze, S. Naito, A. Sone and H. Kan, Dual-site lidar observations and satellite data analysis for regional cloud characterization, Opt. Rev. 14 (2007) 39-47.


Advances in Geosciences Vol. 10: Atmospheric Science (2007) Eds. J. H. Oh and G. P. Singh © World Scientific Publishing Company


YONGSHENG ZHANG International Pacific Research Center, SOEST, University of Hawaii at Manoa, POST Bldg. 401, 1680 East-West Road, Honolulu, Hawaii 96822, USA


International Pacific Research Center and Department of Meteorology, SOEST, University of Hawaii at Manoa, POST Bldg 401, 1680 East-West Road, Honolulu, Hawaii 96822, USA

In this chapter, water vapor and air temperature profiles observed by the Atmospheric Infrared Sounder (AIRS), sea surface temperature (SST) and rain rate observed by TRMM Microwave Imager (TMI), and QuikSCAT surface wind for 2003—2006 are used to identify the 3D moisture structure and air—sea interaction processes during the onset of the South China Sea summer monsoon (SCSSM). Our analyses document an enhanced moisture accumulation in the atmospheric boundary layer co-existing with the surface easterlies preceding to the monsoon convection. Further analysis points out that, compared to the warming of SST, the boundary layer convergence plays a more important role in producing a warm and wet atmospheric boundary layer ahead of the monsoon convection, which contributes greatly to the development and maintenance of the northward propagation of the monsoon convection.

1. Introduction

As a semi-enclosed tropical sea surrounded by the Southeast-East Asian landmass, the South China Sea (SCS) plays an important role in modulation of climate anomalies in Asia. In middle May, accompanied by a switch of the prevailing zonal wind from easterly to westerly, the onset of the SCS summer monsoon (SCSSM) is characterized by an abrupt increase of precipitation and an associated tropical convergence zone northward propagating from the equator to northern SCS. The rainfall belt continues to move northward and controls the central China and South of Japan in late May and early

June.1-3 On an interannual time-scale, the year-to-year variability of the SCSSM onset date and intensity in May has a strong projection onto the summer rainfall anomalous pattern in the East China and West Pacific Ocean, foreshadowing the development of the full-scale Asian summer monsoon during the subsequent months.2,4-8 These scientific issues have called for a multi-national atmospheric and oceanographic observational and research plan, the SCS Monsoon Experiment (SCSMEX), which was aimed to a better understanding of the onset, maintenance, and variability of the SCSSM (for a overview, see Ref. 7).

In the past decades, efforts have been made to explore various aspects of the SCSSM and led to significant progress. However, some open issues still remain. For example, what is the driving mechanism of the SCSSM onset? While many studies focused on the large-scale environmental condition favorable for the SCSSM onset, the role of the local air-sea interaction and the three-dimensional water vapor profile has rarely been addressed, partially due to shortage of reliable observations in an appropriate timespace resolution. Using ship observations, Chu and Chang9 identified the development of a warm-core eddy in the central SCS immediately before onset of the SCSSM attributing to the radiative warming and the downwelling driven by the surface anti-cyclonic flows, which helps lowering atmospheric surface pressure. However, their data analysis is limited to 1966 only. The air-sea heat exchanges during different stages of the SCSSM onset have also been explored by using the station observations in the SCS.10-12 Though considerable air-sea flux exchanges were identified during the monsoon onset, the direction of the heat transportation varies from one study to another, partially because of the difference of the observation location and time.12 So far there is no conclusive result on how the air-sea interaction and water vapor profile may affect the in situ thermodynamic condition which leads to the onset and northward propagation of the SCSSM.

The recently available satellite observations of the sea surface temperature (SST), precipitation, humidity, air temperature, and surface wind provide accurate and high-resolution coverage in the ocean regions where the conventional observation is rare. This provides an unprecedented opportunity to investigate the complicated physical processes relevant to the SCSSM onset. Among satellite sensors, the Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the NASA's Earth Observing System (EOS) polar-orbiting platform and is the most advanced moisture and air temperature sounding system. It constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors for measuring atmospheric water and temperature profiles with a twice-daily, 1-2 km vertical, and 45 km horizontal resolutions. The accuracy of the humidity and air temperature profiles derived from AIRS have been recognized as improving forecasts from meteorological prediction models.a The advantages of using the AIRS in describing the air temperature and moisture structures of the tropical Madden-Julian oscillation (MJO) have been demonstrated by a couple of studies.13 (Yang et al., 2006). The instruments carried by the Tropical Rainfall Measuring Mission (TRMM) satellites provide useful information of the tropical rain rate and SST. Also, NASA's Quick Scatterometer (QuikSCAT) offers the information of the surface wind.

In general, the goal of this chapter is to reveal the role of in situ hydrological cycle in driving the northward movement of the tropical convection during the SCSSM onset by analyzing the three-dimensional water vapor and the underlying air-sea interaction using the aforementioned satellite observations during 2003-2006.

2. Datasets

The level-3 AIRS data used in this study include the atmospheric moisture and temperature profiles at 12 levels from 1000 to 100 hPa with a spatially 1.0 degree longitude-latitude and a temporally twice-daily resolutions since 1 August 2002. Detail description of this dataset can be obtained at: In this chapter, 10-day mean data are constructed from the original twice-daily data. We also used the 3-day running mean rain rate and SST observed by the TRMM Microwave Imager (TMI) and surface wind observed by the QuikSCAT. Both TMI rain rate, SST, and QuikSCAT wind have a resolution of 0.25 x 0.25 longitude/latitude and the information is provided at: Other data used in this study include the daily reanalysis of the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), and the global surface and upper air analyses at the European Centre for Medium-Range Weather Forecasts (ECMWF). The later is a 2.5 x 2.5 degree grid output from the ECMWF operational model provided through NCAR.

aNOAA administrator Lautenbacher has reported that "the AIRS instrument has provided the most significant increase in forecast improvement in this time range of any other single instrument." (

3. Results

Previous studies have documented the weakness of the humidity in the reanalyses, particularly, at the lower troposphere. Zhang14 compared the monthly humidity from the NCEP/NCAR and ECMWF 40-year reanalyses with the station observations in the East China in 1990s, and he found that the humidity in both the NCEP/NCAR and ECMWF 40-year reanalyses in the lower troposphere is much larger than that from the station observation, concurrent with a cold bias in the air temperature. Tian et al.15 identified that the lower-troposphere moisture and temperature structure related to MJO is much less well defined in NCEP than in AIRS in the Pacific and Indian Oceans. In Fig. 1, we compare the 10-day mean humidity at 1000 hPa in May for 2003-2006 from AIRS observations with the NCEP/NCAR reanalysis. Overall, the magnitude in NCEP/NCAR reanalysis is larger than those of AIRS observations. In the SCS region, the moisture maximum in the AIRS observation locates in the ocean but in the NCEP/NCAR

AIRS Observations

NCEP/NCAR Reanalysis

AIRS Observations

NCEP/NCAR Reanalysis


1 20E


80E 100E 120E 140E

Fig. 1. 10-day mean specific humidity from AIRS observations (left panels) and from the NCEP/NCAR reanalysis (right panels) at 1000 hPa for 2003-2006 in unit of g/kg. Shaded areas denote the values lager than 17g/kg for AIRS and 19g/kg for NCEP/ NCAR data.


1 20E


80E 100E 120E 140E

Fig. 1. 10-day mean specific humidity from AIRS observations (left panels) and from the NCEP/NCAR reanalysis (right panels) at 1000 hPa for 2003-2006 in unit of g/kg. Shaded areas denote the values lager than 17g/kg for AIRS and 19g/kg for NCEP/ NCAR data.

reanalysis it mainly appears in the land region. In the northwestern Pacific region to east of Philippines, the AIRS observations show an independent maximum, but this does not occur in the NCEP/NCAR reanalysis. The results from AIRS observations also show a remarkable sub-seasonal change with decrease of the humidity from early to late May, consistent with a low-level moisture loss associated with a development of the strong convection activities in the SCS. However, this feature does not appear in the NCEP/NCAR reanalysis.

The 3D structure and evolution characteristics of the water vapor during the SCSSM onset have not been well documented. Figure 2 depicts the vertical distributions of the humidity, the QuiSCAT surface wind, and TMI precipitation averaged between 105°E-120°E in early, middle, and late May in 2003, 2004, and 2005, respectively. We did not discuss the case of 2006 simply because the onset of SCSSM in 2006 is strongly controlled by the circulation associated with Typhoon CHANCHU in middle May.

2003 2004 2005

2003 2004 2005

Fig. 2. The vertical-latitude distributions of the 10-day mean AIRS humidity at 1000 hPa (shading, scales are shown in the bar in unit of g/kg), QuiSCAT surface zonal wind (blue line, Y-coordinate scales are marked in right side in unit of m/s) and TMI rain rate (red line, Y-coordinate scales are marked in left side in unit of mm/day) averaged between 105°E and 120°E. For the humidity, Y-coordinate shows the pressure level (hPa) and the domain average over 105° E—120° E and eq.-20°N is removed at each pressure level.

Fig. 2. The vertical-latitude distributions of the 10-day mean AIRS humidity at 1000 hPa (shading, scales are shown in the bar in unit of g/kg), QuiSCAT surface zonal wind (blu

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