6. THE MARITIME CONTINENT MONSOON

ANDREW W. ROBERTSON i

I International Research Institute for Climate and Society Columbia University, New York, USA

E-mail: awr@iri.columbia.edu

VINCENT MORON i , 2

2CEREGE, UMR 6635 CNRS, Universite d 'Aix-Marseille Aix en Provence, France

JIAN-HUA QIAN i

CHIH-PEI CHANG3,4

3 Department of Meteorology Naval Postgraduate School, Monterey, California, USA

4Department of Atmospheric Sciences National Taiwan University, Taipei, Taiwan

E-mail: cpchang@nps.edu

FREDOLIN T ANGANG5

5 School of Environmental and Natural Resource Sciences National University of Malaysia, USA

EDVIN ALDRIAN6

6 Agency for Assessment and Application of Technology, Jakarta, Indonesia

TIEH YONG KOH7

7School of Physical and Mathematical Sciences Nanyang Technological University, Singapore

LIEW JUNENG5

The Maritime Continent is situated between the Asian and Australian summer monsoons, with monsoon rainfall generally peaking during boreal winter. The seasonal asymmetries are geographically complex and reflect multiscale interactions. Monsoon rainfall exhibits pronounced variability on all time scales from diurnal to interannual and longer, and is well correlated with ENSO during the dry and transition (i.e. June-November) seasons. Monsoon onset is substantially delayed during EI Nino years, while the monsoon retreat is less impacted. Regional model simulations reveal increased monsoon rainfall intensities over orography during EI Nino events, tied to strengthened diurnal land- sea and mountain breeze circulations, associated with weaker large-scale winds conditions during EI Nino events. On intraseasonal and synoptic scales the region is heavily influenced by the MJO and cold surges, which can interact with each other as well as with in situ synoptic systems such as the Borneo vortex, often leading to torrential rainfall, flash floods, and severe storms, including one rare case, a typhoon.

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86 Andrew Robertson et al.

1. Introduction

This paper reviews Maritime Continent monsoon research and related topics from selected publications in the past five years. The complex topography of the Maritime Continent (MC) region leads to wind terrain interactions that cause variations in the weather and climate on all scales, while differential land-sea contrasts lead to pronounced diurnal land-sea breeze circulations. We highlight how such local and regional-scale processes interact with seasonality and modulate the impact of the EI Nifio-Southern Oscillation (ENSO), and draw attention to unanswered questions, especially regarding longer-term MC lTIOnSOOn variability.

2. Annual Cycle and Seasonal Transitions

The Maritime Continent experiences a marked seasonal cycle in precipitation characteristic of a monsoon climate, especially south of the equator with the principal rainy season centered on December-February (DJF), and the dry season peaking in July-August (Aldrian and Susanto 2003; Giannini et al. 2007). The main rainy season is associated with the Australian monsoon, i.e. the large-scale shift of the Inter-Tropical Convergence Zone (ITCZ) to the southern hemisphere with a northwesterly monsoon flow at low levels south of the equator (i.e., Chang et al. 2004a, 2005a; Wheeler and McBride 2005). The islands north of about 1-2°S have a more complex or less-pronounced seasonal cycle with a decrease of rainfall rather than a real dry season around June-August (JJA) (i.e., Aldrian and Susanto 2003; Aldrian et al. 2005, 2007).

Figure 1. Differences of TRMM Precipitation Radar data and QuikSCAT winds between boreal winter and boreal summer (DlF minus JJA). Warm colors are the boreal summer monsoon regime and cool colors are the boreal winter monsoon regime. (from Chang et al. 2005a)

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The Maritime Continent Monsoon

100E

-5 -3

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-1

and 850hPa winds

120E -10

3 5

87

Figure 2. Same as Fig. 1 but for RegCM3-simulated low-level winds (sigma=0.865, unit: m/s) and precipitation (lmn/day). The model was driven by NCEP-NCAR reanalysis boundary conditions over 1971-2000 at 25-km resolution.

Chang et al. (200Sa) showed that the annual cycle of the greater Asian-Australian monsoon region is characterized by two important asymmetries between boreal summer and winter and between boreal spring and fall, both due to complex wind- terrain interactions. Figure 1 shows the DJF - JJA difference fields in Tropical Rainfall Measuring Mission (TRMM) precipitation and Quick Scatterometer (QuikSCAT) winds that define the partition of the boreal summer and winter monsoon rainfall regimes. The regimes intertwine across the equator, with the boreal winter regime (positive anomalies in Fig. 1) extending northward to SON, and beyond along the eastern flanks of the Philippines and the Malay and Indochina peninsulas. On the other hand, the boreal summer regime (negative anomalies in Fig. 1) is mostly confined within the northern hemisphere. This asymmetry is due to the strong East Asian winter monsoon (EA WM) baroclinicity that produces stronger northeasterly winds than the southeasterly winds in the southern hemisphere during boreal summer, and the fact that very few coastal areas south of the equator face the prevailing wind in boreal summer. The observed seasonal asymmetries over the Maritime Continent can largely be captured by regional circulation models when driven by reanalysis fields, as illustrated in Fig. 2 for the case of the RegCM3 model, a regional climate model, with 2S-km resolution, driven with the National Centers for Environmental Prediction - the National Center for Atlnospheric Research (NCEP-NCAR) reanalysis over the period 1971-2000. This Inodel tends to overestimate land precipitation compared to the seas, while the opposite situation was found by Aldrian et al. (2004) in the Max Planck Institute regional model (REMO). Aldrian et al. (200S) subsequently found a reduction of the REMO model's overestiInated oceanic precipitation when the Inodel was coupled to a dynamic ocean model. Qian (2008) showed

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88 Andrew Robertson et al.

from RegCM3 simulations and satellite estimates that MC rainfall is mostly concentrated over islands, especially over mountains due to sea/valley breeze convergence and cumulus merger processes. The diurnal cycle of winds associated with islands and mountains is an important process in forming the maximum rainfall and latent heating center over the MC from the perspective of the global circulation (Qian 2008).

(a) CMAP : 90-130E

(b) CTRL: 90-130E

(c) SYMM: 90-130E

Figure 3. Annual variations of precipitation over 90oE-130oE from (a) Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), (b) GCM control run, and (c) GCM run with symmetric annual cycle of SST, unit: mrnlday. (from Wang and Chang 2008)

During the transition seasons, Chang et al. (2005a) showed that the boreal fall rainfall dominates north of the equator and west of central Borneo, and the boreal spring rainfall dominates south of the equator, Borneo and east of Borneo. Away from the equator, significant boreal fall monsoon rainfall can also be found in the South China Sea and east of the Philippines, but there is no similar southern hemisphere austral fall regime. The distribution reflects an asymmetric seasonal march: the maximum convection during boreal fall moves gradually from the Indian summer monSoon southeastward and across the equator

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The Maritime Continent Monsoon 89

towards the Australian summer monsoon, but during boreal spring the maximum rainfall remains mostly south of the equator followed by a sudden onset of the Asian summer monsoon. Thus, the annual march of monsoon is not dominated by a sun-following displacement of convection, but is modulated by the interaction between a broad-scale seasonal reversal of winds, together with the local complex terrain.

Chang et al. (2005a) hypothesized that this spring-fall asymmetry is due to a global-scale redistribution of mass between land and ocean areas during the transitions seasons whose signals can be detected over most of the earth's surface. The redistribution results from different land-ocean thermal memories and atmosphere-ocean interactions. Because of the orientation of the Asian and Australian land masses, it produces sea-level pressure patterns that lead to asymmetric wind-terrain interactions throughout the region, and a low-level divergence asymmetry that facilitates the southeastward march of maximum convection from the Asian summer monsoon to the Australian summer monsoon, and hinders the reverse march in boreal spring; the associated seasonal asymmetry in meridional displacements is depicted in Fig. 3a. This hypothesis was tested by Wang and Chang (2008) with a general circulation model (GCM) experiment. When the model is driven by a SST with a symmetric annual cycle (no thermal memory in the ocean), the northwestward march of the maximum convection in boreal spring becomes more gradual, resulting in an overall near-symmetric pattern for the monsoon seasonal transition (Fig. 3c) compared to the control driven with observed seasonally varying SSTs (Fig. 3b).

3. Interannual Variations and Seasonal Predictability

It is well known that interannual rainfall variability over Indonesia is strongly related to ENSO with anomalously low rainfall during warm events (Hamada et al. 2002; McBride et at. 2003; Giannini et al. 2007), associated with anomalous large-scale subsidence, a weakened Walker circulation (Klein et al. 1999) with anomalous surface easterlies, and anomalously cool SST around Indonesia. The relationship, however, is generally stronger in the dry and transition seasons and is quite weak during the boreal winter, as reflected in the leading extended empirical orthogonal function (EEOF) of seasonal rainfall in Fig. 4, which shows the typical evolution of rainfall anomalies during an El Nino event. These relationships have been simulated in GCMs (Aldrian et al. 2007; Giannini et al. 2007; Zhou et al. 2009), and there is pronounced seasonality of seasonal prediction skill over the region, with higher skill in the "dry" and transition seasons and rather low skill for the peak of the rainy season (Aldrian et al. 2005, 2007; Juneng and Tangang 2007; Vi mont et al. 2010). Hendon (2003) proposed that the weakening of the relationship between ENSO and Indonesian rainfall from JJA to DJF is linked to the seasonal transition between surface southeasterlies to northwesterlies. The easterly anomaly superimposed on the climatological mean winds during a warm ENSO event acts to increase wind speed during the "dry" season, but to reduce it during the monsoon season, differentially increasing and then reducing evaporation, leading to evanescent pre-monsoon local cold SST anomalies during El Nino years, and vice versa in La Nina years.

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90 Andrew Robertson et al.

a b

~ l BN

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q

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95E l 15E l 20E l 25E 130E 105E

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~ ~ / \)~, l2N ~ -, / ~

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~~~ L/

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-2.5 -2. 0 -1.5 -1.0 1.0 1.5 2.0 2.5

Figure 4. The spatial patterns of the leading extended EOF (~ 20% of total variance) of Southeast Asia precipitation anomalies (a) JJA(O), (b) SON(O), (c) DJF(OIl), and (d) MAM(1). The modal time series is correlated strongly with ENSO (r = 0.86). The year 0 and year 1 denote EI Nino developing and mature years, respectively. (adapted from Juneng and Tangang 2005)

0.300

0.225

~ 0.150 ~

0.075

o Onset 15-day 3~-day 60-day gO-day SOND SOND-resid

Rainfall quantity

Figure 5. Spatial coherence of interannual anomalies of rainfall quantities over Indonesia, in tenns of variance of a standardized anomaly inde?,'- (SAl) defined by the spatial average of anomalies standardized to have zero mean and unit variance; higher variance indicates higher spatial coherence between 57 stations across Indonesia. First bar indicates onset date, defined to be the first wet day of the first 5-day sequence receiving at least 40 mm that is not followed by a dry 10-day sequence receiving less than 5 lllin within the following 30 days from the onset date, computed from 1 August. The following four bars denoting rainfall averaged in periods after local onset date; SOND = 4-month total, with right-most bar showing the residual with onset­date contribution linearly removed. (adapted from Table 2 of Moron et al. 2009)

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The Maritime Continent Monsoon 91

The large spatial coherence of seasonal rainfall anomalies during September-November (SON) has been attributed to large spatial coherence of anomalies in monsoon onset date (Moron et al. 2009). Figure 5 shows the spatial coherence of station rainfall anomalies across Indonesia in terms of the interannual variance of a standardized anomaly index (SAl) averaged across the stations (Katz and Glantz 1986). Larger variance of this SAl illlplies COlnmon in-phase variation of the seasonal anomalies between stations, because spatially out­of-phase anomalies will tend to cancel, reducing the SAl amplitude. The SAl variance is plotted in Fig. 5 for the monsoon onset date, computed locally at each station using an "agronomic" definition (see caption), together with post-onset averages of rainfall, the September-December (SOND) seasonal total, as well as the residual of the seasonal total with the local-scale onset contribution linearly subtracted. This regional scale analysis deillonstrates that the spatially coherent, and thus lllore potentially predictable, component of SOND seasonal rainfall over Indonesia is associated with changes in the onset date, and that post-onset rainfall is much less potentially predictable (cf. Haylock and McBride 2001).

5N

EQ

5S

lOS

100E 110E 120E 100E 110E 120E 19 - 22 LT, Clim 19-22 LT, EN-Clim

5N 5N

EQ EQ

5S 5S

lOS lOS

100E 110E 120E 100E 110E 120E ~

0,5

0,5 1 2 4 6 B 10 15 20 25 -5 -3 -1 3 5

Figure 6. Snapshots of the simulated diurnal cycle of precipitation (mmlday) and low-level winds (sigma=O,865, unit: mls), Left-hand panels show the climatological DJF mean fields at (a) 13-16 h, and (b) 19-22 h local time; right-hand panels show the differences between El Nino and neutral years (as in Fig, 5), with the daily averages of each subtracted at (c) 13-16 h, and (d) 19-22 h local time.

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Although spatial coherence of ENSO rainfall anomalies over land is much reduced after monsoon onset, recent work using high-resolution regional climate models has shown that higher rainfall intensities over the topography during the monsoon of EI Nino (+ 1) years are caused by an enhanced diurnal cycle that is promoted by the weakened large-scale monsoon flow (Moron et al. 2010; Qian et al. 2010). In other words, island topography and the related diurnal land-sea and mountain-valley breeze cycle tend to break-up the regional-scale signal related to large-scale low-level divergence (Klein et al. 1999). Snapshots of the RegCM3's climatological diurnal cycle of rainfall and near-surface winds together with its anomalies during EI Nino years are plotted in Fig. 6 (Qian, personal communication). Simulated rainfall peaks in the early afternoon over lower-lying western Borneo and eastern Sumatra, and is maintained and strengthened into the evening over the topography by sea-breeze circulations. The latter are clearly enhanced during EI Nino years over the mountains in Java and Sumatra (Fig. 6d), and the daily weather typing analysis of Moron et al. (2010) attributed this to the increased prevalence of large-scale quiescent wind conditions during El Nino years. The small-scale orographic wet anomalies may not be resolved in the extended EOF of observed gridded rainfall in Fig. 4 during DJF, but they are clearly visible in the EI Nino station composites for Java constructed by Giannini et al. (2007) and Qian et al. (2010). The observed wet anomalies over relatively flat southwest Borneo in Fig. 4 during DJF and March-May (MAM) of warm ENSO years are also reflected in the RegCM3 simulation, and are hypothesized to be due to changes in the propagation of the daily maximum of rainfall associated with ENSO-induced wind anomalies (Qian, personal communication).

4. Subseasonal Variability

The multiple timescales of rainfall variability during the SOND season are illustrated in Fig. 7 for a small district on the north coast of West Java, in terms of the sequence of daily rainfall states identified with a hidden Markov model (HMM) from 17 station records (Robertson et al. 2009); the HMM assumes that a small number of discrete weather states govern the station rainfall distribution parameters, with daily Markovian transitions between the states (Fig. 7). The sequence of the four progressively wetter rainfall states provides a graphic illustration of the rainfall variability at the district level, in terms of its seasonality, subseasonal variability, as well as interannual variability. The driest state predominates during September, with spells of the wetter states becoming more prevalent in November- December, while there is considerable variability of the sequences from year to year, and within each season. The local monsoon onset was clearly substantially delayed during the EI Nino events of 1982, 1987, 1994, and 1997. The HMM also provides a method to downscale seasonal climate forecasts to produce stochastic daily rainfall sequences at each station, by allowing the state transition probabilities to depend on the GCM seasonal forecast values. The skills obtained for onset date and rainfall frequency were often found to exceed those of the seasonal rainfall total (Robertson et af. 2009), and the former two statistics may be more useful than seasonal total to agricultural decision makers.

The Madden-Julian Oscillation (MJO) from the west, northeasterly surges from the north,

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The Maritime Continent Monsoon 93

and cross-equatorial flow from south provide the dominant intraseasonal and higher frequency forcing of the MC monsoon. Excess rainfall is brought during wet MJO phase, when the convective activity reaches its maximum with enhanced low-level wind convergence over the MC. In addition, the impact ofMJO tends to be more profound over the surrounding seas than over the large islands (Hidayat and Kizu 20 I 0). Ichikawa and Yasunari (2007) studied the propagation of diurnal disturbances across the islands of the MC, which they connected to the sudden shift of the convection center from the western part of the islands to the east and the overall propagation of the MJO from Indian Ocean to the Pacific. Shibagaki et al. (2006) studied satellite and station observations in a active phase of MJO and found various spatial scale (from less than 10 km to 4000 km) convective precipitation and cloud clusters with smaller scale clusters embedded in larger scale clusters with opposite direction of propagation. Moron et al. (2010) used a set of 5 discrete daily "weather types" (WTs) identified from a k-means cluster analysis of unfiltered daily 850-hPa reanalysis wind fields, to interpret interannual variability over Indonesia. "Transitional" and "quiescent" WTs were found to predominate over "westerly" and "strong-westerly" types during El Nino years, with the diurnal cycle of rainfall being more pronounced during the "quiescent" WT, especially along the mountainous southern and western coast of Java and Sumatra (see Fig. 13 of Moron et al. 2010). The occurrence of these WTs is also modulated by the MJO.

calander day

Figure 7. Daily observed rainfall state sequence from an HMM applied to 17 stations over Indramayu district, Java. The grey scale indicates the state number ordered from driest (state I) to wettest (state 4). (from Robertson et al. 2009)

The Borneo vortex is the most prominent synoptic-scale stationary closed circulation in the tropical atmosphere (e.g., Chang et al. 2003, 2005b). In the absence of a Borneo vortex, strong cold surge winds suppress deep convection over the southern South China Sea due to their dryness, while they enhance convection over the Malay Peninsula and Sumatra once they are warmed and moistened and interact with the downstream terrain. This effect can also affect Java. When a Borneo vortex is present during strong surge events, the reverse

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distribution occurs. The surge interacts with the vortex causing enhanced convection over the southern South China Sea. This interrupts the moisture supply downstream and slows down the wind terrain interaction leading to reduced convection over the Malay Peninsula and Sumatra, as well as Java. The vortex induced wind turning leads to more convergence towards Borneo and further increases the convection associated with the vortex.

An extreme case of cold surge-vortex interaction led to the formation of Typhoon Vamei on 26 December 2001 at I.S0N off Singapore (Chang et al. 2003; Chang and Wong 2008). In this case a strong and persistent cold surge that was strengthened by the narrowing South China Sea created a large background cyclonic vorticity at the equator. The interaction of the surge and a Borneo vortex that persisted over the narrow sea region for several days led to the tropical cyclogenesis. The process resembles a solution of the cold surge theory of Lim and Chang (1981), who showed that in a barotropic equatorial framework, geostrophic adjustment and potential vorticity conservation following a cross-equatorial surge spin up counterclockwise rotation to the east of the surge axis, where the Borneo vortex is located in the real world. A number of groups have successfully simulated Typhoon Vamei using mesoscale models (Chambers and Li 2006, Juneng et al. 2007a, Tangang et al. 2007).

Chang et al. (2005b) showed that cold surges occur equally frequent during non-MJO periods and the wet and wet-to-dry phases of MJO periods. During the dry and dry-to-wet phases the frequency is reduced by half, so the net effect of MJO is a reduction of the surge frequency. The Borneo vortex is least likely to occur when the inactive convective portion of the MJO extends to the Maritime Continent with large-scale low-level divergence that acts to restrict the impact of cold surges on convection in the southern South China Sea. This complex relationship among MJO, cold surges and the Borneo vortex and the effects of the topography contribute to the variability in convection patterns over a variety of space and time scales that can lead to stormy weather and flash floods (e.g., Johnson and Chang 2007; Tangang et al. 2008).

5. Interdecadal Variability, Trends and Projected Climate Change

On interdecadal time scales, modulations in the strength of the correlation between different regions of Indonesian rainfall and ENSO have been noted (Chang et al. 2004b; Aldrian and Djamil 2008), together with interdecadal modulation of ENSO's impact on the EA WM according to the phase of the Pacific Decadal Oscillation (PDO) (Wang, L. et al. 2008). A strengthening of the correlation between the Asian- Australian monsoon variability and ENSO has been found since the late 1970s (Chang et al. 2004b; Wang, B. et al. 2008). Teak tree ring chronologies from Java and Sulawesi together with coral oxygen-isotope records from Lombok have been used to reconstruct Indonesian warm pool SSTs (D'Arrigo et al. 2006a) and Palmer Drought Severity Index (PDSI) for Java (D'Arrigo et al. 2006b) over the past two centuries, and to relate these to records of ENSO and Indian Ocean SST variability. Significant correlations are found with ENSO and monsoon indices in interannual to decadal frequency bands. Negative reconstructed SST anomalies are found to coincide with major volcanic eruptions, while other noteworthy extremes are at times synchronous with Indian

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The Maritime Continent Monsoon 95

and Indonesian monsoon drought, particularly during major EI Nino events. O'Arrigo et al. (2008) found that coral proxies from regions near or within the two poles of the Indian Ocean dipole (100) (Saji et al. 1999), defined by the regions (500E-70oE, lOoS-100N) and (900E-11OoE, lOoS-EQ), show good agreement with Java POSI extremes over the past 150 years. In particular, the EI Nino of 1877, in conjunction with a positive 100, was one of the most intense and widespread drought episodes of the past two centuries.

Climate change projections for Maritime Continent monsoon precipitation are particularly difficult due to the large impact of ENSO on the region, and the current uncertainty as to how ENSO may evolve under climate change. Naylor et al. (2007) have considered the projected impact of climate change on the mean seasonal cycle of precipitation over Indonesia from the Intergovernmental Panel on Climate Change, the Fourth Assessment Report (IPCC AR4) suite of climate models, using statistical downscaling models. Their results foresee a marked increase in the probability of a 30-day delay in monsoon onset in 2050, as a result of changes in the mean climate, from 9-18% today (depending on the region) to 30-40% at the upper tail of the multi-model ensemble distribution. Predictions of the annual cycle of precipitation suggest a substantial decrease (up to 75% at the tail) in precipitation in the dry season (July-September), with an increase in precipitation at the end of the MC monsoon (April-June) of -10%, suggesting a shift in the monsoon period.

6. Conclusion

The studies of the MC monsoon over the past five years reviewed here highlight the broad spectrum of spatio-temporal variability, and the interrelations between the strong diurnal cycle, subseasonal weather types and ENSO-related interannual variability. Wind-terrain interactions together with modulations in the strength of the diurnal cycle appear especially important in understanding sub-regional spatial scales in this region of complex island topography. High-resolution regional model simulations are beginning to play an important role in understanding the dynamics of sub-100-km scale variability over the region. Of particular potential societal relevance, the onset date of the monsoon exhibits an ENSO­related predictable component, while climate change and interdecadal variations may also substantially impact it. Monsoon onset strongly influences crop planting dates, which also influence the second planting of rice at the end of the rainy season (Naylor et al. 2007) .

Attempts to manage fire activity (and the related carbon emissions) over Kalimantan based on ENSO forecasts are currently being explored (Ceccato et al. 2010).

While many studies have focused on ENSO teleconnections, there have been fewer studies of relationships with the Indian Ocean, and on variability on longer time scales and climate change. Regional numerical modeling in the Maritime Continent monsoon region faces the challenges of a very complex terrain and inadequate observational network (Koh and Teo 2009), but the development of NWP programs in several regional centers has made progress. Experiments of the COAMPS and MM5 models have led to reasonable simulation of mesoscale phenomena including sea breeze and thunderstorms (Joseph et al. 2008; Koh and Ng 2009) and extreme rainfall (Juneng et al. 2007b).

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Acknowledgements

The authors were supported by their respective institutions. A WR and JQ were also supported by the US National Oceanic and Atmospheric Administration through a block grant to the IRl.

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