Observation and numerical experiments of orographic precipitation in the Mae
Chaem watershed in Southeast Asia

*Koji Dairaku1, Seita Emori2, Taikan Oki3
(1: Disaster Prevention Research Group, National Research Institute for Earth Science and Disaster Prevention,
2: Atmospheric Environment Division, National Institute for Environmental Studies,
3: Institute of Industrial Science, the University of Tokyo)* National Research Institute for Earth Science and Disaster Prevention, Japan,
3-1, Tennodai, Tsukuba Ibaraki, Japan. e-mail: dairaku@bosai.go.jp
Abstract
A dense tipping-bucket rain gauge network was established in the Mae Chaem watershed in themountains of northwestern Thailand as part of the Global Energy and Water Cycle Experiment (GEWEX)
Asian Monsoon Experiment-Tropics (GAME-T). Investigations of rainfall amounts, intensities, durations,and frequencies in the rainy season revealed strong orographic rainfall enhancement in the region. The larger
amount of high-altitude rainfall was attributed to duration and frequency rather than intensity. By
investigating orographic precipitation using a regional atmospheric model, the duration and frequency ofrainfall at high altitudes were found to be caused by two mechanisms: 1) convective clouds over mountains
activate in the evening, and 2) seeder-feeder mechanism caused by strati formed by radiative cooling or
heating in the middle troposphere and orographically triggered low-level convective clouds. The results of thenumerical simulations were quite consistent with the measurements acquired from the tipping-bucket rainfall
gauges.
Keyword: Orographic Precipitation, Regional Atmospheric Model, Tropical Mountainous region, Southeast Asia.

1. Introduction
Many researchers have examined temporal and spatial
precipitation distributions. These studies are important forwater resource and flood control management, and fordesigning and planning various engineering projects(Smith, 1992; Palmer and Rälsänen, 2002). Precipitation
generally increases with elevation, and mountain ranges
can create leeward-side “rain shadows”. This implies someunderlying mechanisms which play an important role inorganizing precipitation systems in a mountainous region.
However, worldwide, only an irregular, coarse grid ofprecipitation measurement data exists and it is not
sufficient to say that mechanisms are fully understood.
Only about 6,600 stations worldwide archive rain gauge
data through the Global Telecommunications Network orother regional or national data collection centers (Rudolf etal., 1994). Both precipitation data and data archives arealso mostly located in mid- or high-latitude developed
countries. Consequently, most studies, however, havefocused on mid-latitude developed countries, which are
more likely to have the necessary high temporal resolution
rainfall records. In recent years, hydro meteorologicalnetworks are installed in some ungauged high elevation
basins. Tropical countries typically have sparse operationalrain gauge networks and extremely limited high temporal
resolution (one hour or less) precipitation records.
Point-to-area and gauge gaps are the biggest problems
for gauge analysis. Huffman et al. (1995) noted that rain
gauge analysis at the Global Precipitation Climatology
Centre (GPCC; Rudolf et al., 1994) revealed important
quantitative differences from GPCP satellite estimates,
particularly in Southeast Asia and Central America. Xie
and Arkin (1997) also noted large biases and random errors
in a GPCC gauge, positioned in an area of Thailand

affected by significant mountain-induced precipitation
variations.
This paper investigates rainfall frequency, amount,intensity, and duration on an event basis in the Mae Chaem
watershed, a tropical mountainous region using hightemporal resolution tipping-bucket rain gauge data and astate-of-the-art regional atmospheric model to understandthe statistical precipitation structures in the rainy season
that induce significant bias and spatial sampling error. The
sections that follow describe the study area, dataset,methodology, and results.

2. Study Area, dataset, and relationships among
the quantity, intensity,
frequency of rainfall

duration,andSince 1998, Global Energy and Water Cycle Experiment(GEWEX),
Asian
MonsoonExperiment-Tropics(GAME-Tropics) researchers have conducted hightemporal resolution ground-based rainfall observations inmountainous areas of Thailand. Fifteen tipping-bucket typerain gauges with sub-hourly (one minute) time resolutions
were installed at sites 380 to 2496 m above sea level in the
Mae Chaem watershed (Fig.1). The watershed covers an
area of 3853 km2 (Dairaku et al., 2000, 2004; Kuraji et al.,2001).
The Mae Chaem watershed is located in the northwest
Chao Phraya River basin. The highest peak in thewatershed, Doi Inthanon, rises 2,535 m above sea level.
Data logger records the time by the second when 0.5mm
rainfall quantity is observed. Rainfall records used in thisstudy were obtained from 12 sites from 1 June to 31
October, 1998 and from 13 sites from 1 June to 31 October,1999 in order to investigate the relationships among thequantity, intensity, duration, and frequency of rainfall
during the rainy season.

the higher quantity of rainfall at higher elevation stations,shown in Figure 2a, is not attributed to the higher intensityof rainfall. The average duration of the rainfall increases
with the elevation in Figure 2c. As shown in Figure 2d, thenumber of rainfall events also depends on the elevation. In
other words, increased duration and higher frequency
contribute to orographic rainfall.
Regardless of the large differences in the quantity ofrainfall between 1998 and 1999, the orographic
characteristics of rainfall, which are attributed to higher
duration and frequency rather than to intensity, arecommon to both 1998 and 1999.

3. Model and Experiments

Fig. 1: GAME-T rainfall observation stations in the Mae
Chaem watershed, Thailand.

Fig. 2: Relationships between elevation and (a) rainfall
amount, (b) average rainfall intensity, (c) average
rainfall duration, and (d) frequency in 1998 and 1999. In
this legend, “6-10” denotes the months June-October.
As shown in Figure 2a, there was a significant regionalvariation in the rainfall recorded by the rain gages. Since
the final quality of merged analysis is primarily determinedby the quality of input data sources, improving thequantitative estimation and the individual data sources isessential. It is clear that the quantity of rainfall varies withelevation in this watershed throughout the rainy season andthe inter-annual variation in the quantity of rainfall is quite
large. As shown in Figure 2b, the average intensity of therainfall is not correlated with the elevation. In other words,

As described in the section 2, the rainfall characteristics
in the watershed are strongly influenced by the topography.
For effective management and planning of water resourcesin such a region, it is necessary to investigate theinfluences of mountains on rainfall characteristics andprocesses.
Two-dimensional
numerical
experiments
were
conducted. A Regional Atmospheric Modeling System(RAMS) developed by Colorado State University was usedin this study (Pielke et al., 1992). A two-dimentional,
nonhydrostatic compressible dynamic-equations model
was configured in the present study. A liquid- andsolid-phase bulk microphysics parameterization is used tosimulate cloud activity. The two-stream radiation scheme(Harrington et al., 1999) is used as a radiation
parameterization. LEAF-2 (Walko et al., 2000) is used as asoil and vegetation model. Topography data with a 30”
longitude resolution (GTOPO30) is used and incorporated
in this simulation. Land-use data that is estimated fromsatellite data (AVHRR) by USGS is incorporated. The sea
surface temperature was set at the monthly climatological
average for July. Because of two-dimensional simulations,the Coriolis force is excluded.
We used two interacting nested grids. The 1st horizontal
domain (east-west direction) is 1,680 km wide and isrepresented by 420 grids with 4km grid spacing. The 2nd
horizontal domain is 401.6 km wide with 502 grid points of
800 m horizontal resolution and is nested within the 1st
domain to take synoptic features into account. On the other
hand, the vertical grid setting is identical between the two
grids. The vertical grid is a terrain-following σz coordinate,
and the gird space is stretched from 30 m to 500 m
maximum. The vertical grid points are set to 60(approximate 22.3 km above sea level). The solid line in
Figure 3 shows the specified 1st simulation domain with thecoarse grid interval. The thick solid line indicates thenested 2nd simulation domain with the fine grid interval.
As an initial condition, thermodynamic and windconditions are averaged in the area of 1.5˚×1˚(17.75N-19.25N, 97.75E-98.75E, solid rectangle shown in
Fig. 3) over the watershed (dashed rectangle shown in Fig.
3) for the period of three months from June to August using
the GAME-reanalysis data (Yamazaki et al., 2001). The
averaged vertical profile of the initial condition indicates amoist condition in the lower troposphere, westerly winds inthe lower troposphere and easterly winds in the upper
troposphere. The lifted condensation level of thethermodynamic condition is 877.2hPa. The low saturationlevel implies that clouds tend to be formed by a dynamic
orographic upward motion (Fig. 4).
A numerical experiment incorporating the averaged
condition as the initial condition and several sensitivity
experiments were conducted. Each experiment was
integrated for 5days. The first day of the experiments were
excluded as a spin-up period that is required forsufficiently adjusting the initial condition to the complex
terrain in the simulation grids. Precipitation tends to be
triggered and that enhanced in mountainous regions andprecipitation systems move eastward.
As shown in Figs. 5, accumulated surface precipitation
for four days excluding the 1st day of the experiment over
the watershed in the nested grid shows the enhancement of
the precipitation according to the terrain. The rainfall
duration of the accumulated precipitation for four days
over the watershed in the nested grid tends to be greater athigher altitudes and the average intensity of the rainfall
does not seem to be correlated with the topography. The
results are obtained from the two-dimensional numericalsimulation for 5 days. These simulation results cannot becompared directly to the results of the statistical analysis
using data from rain-gage observations for a 5-month
period in 1998 and 1999; however, the results of thesimulation are quite consistent with the fact that a larger
quantity of rainfall recorded at a higher altitude can be
attributed to a greater duration and higher frequency rather
than to a greater intensity of rainfall.
In this numerical experiment, mainly two mechanisms
were observed. First, if there are no clouds above theground, convective clouds are triggered at mountainous
regions in the evening in unstable atmospheric conditions
that occur as the result of the ground heating due to strong
insolation. Then, due to the hydraulic jump (Durran, 1986)
and upslope wind produced by thermal local circulation,
the clouds are organized into squall lines and propagate
eastward. This mechanism is identical with the recent
research (Satomura, 2000).
Second, a seeder-feeder mechanism (Smith, 1977) was
observed. At night, some of the decaying convective
clouds triggered in the evening were reorganized into
stratus clouds around 5 or 6 km above sea level due to theradiative cooling at the top of the clouds. In addition,
convective clouds around 2 or 3 km above sea level occur
in the pure orographic flow produced by westerly moist
monsoon wind in the lower troposphere and complex
terrain. In this two-layer cloud structure, convective clouds
were triggered from the stratus clouds aloft by radiative
cooling during the night or by radiative heating during theday. These convective clouds produced precipitation thatwas locally enhanced by low-level convective clouds overthe mountains. Because the low-level cloud base wasaround 2 km, clouds tended to frequently occur and stayover the mountainous regions around 2 km above sea level.

Fig. 3: The solid line indicates the numerical simulation
domain. The thick solid line indicates the nested
simulation domain. The solid rectangle shows the areaused for calculating an initial condition for the numericalsimulation from GAME-reanalysis data. The dashedrectangle corresponds to Mae Chaem watershed.

Fig. 4: Initial condition for the numerical simulation
calculated from GAME-reanalysis data. The broken line
indicates the temperature. The dotted line indicates thedew-point temperature. The solid line on the right shows
the surface parcel trace. The solid line on the left shows
the relative humidity
The clouds on both layers moved eastward in association
with the westerly moist monsoon wind in the lower
troposphere. When the sky cleared, the convective clouds
were activated again in the mountainous regions.

References

Fig. 5: Comparison of simulated results (solid lines) tothe observations (circles) over the watershed.
Topography used in the model and the elevation of thestations (Upper left). Accumulated surface precipitationof simulation for 4 days over the watershed in the 2nd
domain and observed surface precipitation for 5 months,
June-October (Upper right). Average rainfall duration ofsimulation and observations (lower left). Average
rainfall intensity of simulation and observations (lower
right).

4. Conclusions
The conclusions are as follows:
1) Rainfall quantity varies significantly according toelevation in this watershed throughout the rainy season.
The fact that a larger quantity of rainfall is recorded at
higher altitudes is attributed to greater duration and higher
frequency rather than to higher intensity of rainfall.
2) In the numerical experiment, greater rainfall durationand higher frequency at high altitude are caused by two
mechanisms; 1) convective clouds are activated by
mountains in the evening, and 2) stratus clouds formed byradiative cooling at the top of clouds and low-level
orographically triggered convective clouds cause aseeder-feeder mechanism.

Acknowledgements
This study was conducted as a GEWEX/GAME activity
supported by a grant No.11201202 (representative: Prof.
Katumi Musiake) and Grant-in-Aid for JSPS Fellows fromThe Ministry of Education, Science, Sports, and Culture,
Japan. It was directed by one of the GAME-Tropics
sub-committees. Rainfall observation field works in the
Mae Chaem watershed was conducted by Prof. Suzuki and
Dr. Kuraji and in cooperation with the Royal Forest
Department and Kasetsart University, Thailand. The
authors gratefully acknowledge the helpful arrangement of
GAME-T observation data by Dr.Yasushi Agata and thehelpful discussions with Prof. Takehiko Satomura, Prof.
Jun Matsumoto, Mr. Thada Sukhapunnaphan.

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