Numerical simulation of precipitation phenomena during the monsoon
season on the Tibetan Plateau
Shiori Sugimoto1, Weiming Sha1, Ken’ichi Ueno2(1: Graduate School of Science, Tohoku University, 2:The University of Shiga Prefecture)
Graduate School of Science, Tohoku University, Japan, 980-8578, Sendai, Aoba-ku.
e-mail : sugimoto@wind.geophys.tohoku.ac.jp
Abstract
During the monsoon season, the precipitation variation on Tibetan Plateau is associated withsynoptic-scale front passage or the meso-scale convection when the Tibetan High is dominated. Our
purpose in this study is to simulate these precipitation phenomena and try to make the mechanisms
clear.
In this work, our simulation results have been compared with the GAME-reanalysis data. For
the two different situations (case 1 and 2), geo-potential height and wind, temperature and specifichumidity at 250 and 500 hPa are consistent well with the GAME-reanalysis data. We also compared thesimulated clouds with IRdata from METEOSAT-5.

Keyword: numerical simulation, precipitation on Tibetan Plateau, monsoon study

1. Introduction
DuringAnd we also discussed characteristic of clouds
themonsoon

season,thefrom the detailed data.

precipitation variation on the Tibetan Plateau isremarkable because it is strong affected by

2. Numerical Overview

different dominate synoptic-scale meteorological

The model we use is MRI/NPD-NHM

systems in the middle latitude (Ueno et al., 2001).

(non-hydrostatic model developed by Institute of

One is associated with the synoptic-scale front

Meteorologicalpassage of the Plateau (hereafter, referenced asAgency). Initial data and boundary data were

case1) and the other is associated the meso-scaleconstructed from 0.5degree space resolutionconvection on the Plateau over which large

GAME-reanalysis data. Topography data is from

Tibetan High covered (hereafter, referenced as

GTOPO30. The model has 200×200 grid points incase2). But physical mechanism of these two

the horizontal directions and 38 layers in theprecipitation is not clear. So our purpose in thisvertical direction. The grid spacing in thestudy is to simulate the different precipitation

horizontal directions is △ x= △ y=30km. One ofphenomenaacalculation period we chose is during July 5 tonon-hydrostatic numerical model, and try to make

July 8 in 1998 (case1), and the other is during

the mechanisms clear. In this work, we simulated

July 29 to July 31 in 1998(case2).

the two different situations (case1 and case2) and3. Geography(case1andcase2)

withcompared the results with GAME-reanalysis data.

Research/Japan

MeteorologicalFigure 1 shows the topography utilized

in the numerical simulation and GAME‑reanalysis

south part of Himalaya Mountains. For case2 (Fig.

data,5), the high temperature zone was bigger in therespectively.

Fromthefigure,weunderstand for the simulation study that: (1)simulation

becausetheTibetanHigh

wasTian Shan mountains are located north‑east of theoverestimated as compared with the re-analysis

Plateau and well representative; (2) Himalaya

data. For the specific humidity it was higher

Mountains, especially for its south part, are also

around the Plate but lower on the Plateau.

sharply representative.

Generally, the temperature and specific humidity
were realistically simulated.

4. Results and Discussion
4.2 Characteristic of the clouds

4.1. Characteristic of the flows
Oursimulation

results

have

been

Here, we examined clouds. First, wecompared with the GAME-reanalysis data.

checked convergence zone of the water vapor flux

First, we compared about geo-potential

for two cases. Black zone in Fig.6 is convergenceheight and surface wind for two cases (Fig.2, 3).

zone. Figure 7 shows the vertical integral cloudFig.2 shows for case1 that the trough in theice content for two cases. It is seen that formation

simulated result was clearer than that inof clouds is consistent with convergence zone.

GAME-reanalysis data. The obtained pressure

Next,

wecomparedthissimulated

gradient in simulation was stronger, so the wind

results with IR data from METEOSAT-5. For

speed was larger. Fig.3 indicates that thecase1, Fig.8 indicates that there were many

simulated Tibetan High was stronger and theclouds on or around Tibetan Plateau both inwind speed was larger as same as in case1. In

simulation and IR observation. It was shown that

addition of the wind speed, the wind direction in

the simulated clouds are realistic. But, for case2

reanalysis data was easterly while for the(seen in Fig.9) it was obvious that the simulated

simulation it was westerly. Although there areclouds were not realistic, as IR data showed muchsome difference between the simulation andclouds on the Plateau but we couldn’t get any

re-analysis data, the flow patters were very

cloud in simulation.

similar.

Reference

Next, we compared the temperature andspecific humidity for two cases (Fig.4, 5). It is seen

Ueno, K. Fujii, H. Yamada, H. Liu, L.:in case1 of the simulated result (Fig.4) that theFrequent Monsoon Precipitation over the Tibetan

temperature was higher about 2 K on the Plateau,

Plateau. J. Meteor. Soc. Japan, 79-1B, 419-434,

and the specific humidity was also higher at the2001

45N

45N

（a）

Fig.1

(b)

Weak andTopography

utilized in simulation
(a)
35N

35N

25N
75N

85N

95N

25N
105E

and

GAME‑reanalysis (b).

75N

500 1500 2500 3500

85N

4500 5500

95N

(m)

105E

45N

250hPa面のジオポテンシャル高度、500hPa面の風向風速

500hPa面のジオポテンシャル高度、風向風速
Case1
5760
(a)
(b)

45N

Case2

10700

(a)

(b)

10800
10900

5760

10700

10800
10900

5800

5800

11000

30N

30N
11100

11100
5840
5840
5860

15N
70E80E

5860

90E

100E

70E

80E

90E

15N
100E

70E

80E

90E

100E

30m/s

70E

80E

90E

100E

20km/s20m/s

Fig.2 Geo-potential height and wind at 500

Fig.3 Geo-potential height at 250 hPa, windhPa at 00z on July 6 in 1998. (a) simulated

at 500 hPa at 00z on July 31 in 1998. (a)result and (b) GAME-reanalysis data.

simulated result and (b) GAME-reanalysisdata.

45N (a)262266 270(b) 45N (a)274 (K)

30N

45N

260

264 268272 (K)

0.001

0.004

0.007 (kg/kg)(b)

30N(c)

0.001

0.004(d)0.007(kg/kg)

30N

45N

(c)

(d)

30N

70E

90E

70E

90E

70E

90E

70E

90E

Fig.4 Temperature and specific humidity at 500

Fig.5 Temperature and specific humidity at 500

hPa at 00z on July 6 in 1998. Top figure is forhPa at 00z on July 31 in 1998. Top figure is fortemperature while bottom figure is specifictemperature while bottom is for specific humidity.

humidity. (a),(c) simulated results; (b) ,(d)(a) and (c) are computation results, (b) and (d) are

GAME-reanalysis data.

GAME-reanalysis data..

(a)-7×10 -6-1×10 -6(kg/kg) ・(m/s)

(b)

45N

Fig.6 Convergence zone of thewater vapor flux. The watervapor flux is estimated byintegrating

thedatefromsurface to about 2000 m. (a)at 12z on July 8 in 1998 for30N

case1, (b) at 12z on July 29 in1998 for case2.

15N

70E

85E

100E

70E

85E

100E

(a)

5×10-5

2.5×10-4

5×10-4(b)(kg/kg)

Fig.7

45N

Cloudicecontent

integrated from about 1500 m
to top (about 20 km).

(a) at12z on July 8 in 1998 for case1,(b) at 12z on July 29 in 1998
for case2.

30N

15N

70E

85E

100E

70E

85E

100E

40N

40N

(a)

(a)
35N

35N

30N

30N

25N

25N
75N
40N

(b)

85N

5×10-595N

2.5×10-4

75N

105N

5×10-4

95N

105N

40N

(b)

35N

35N

30N

30N

25N

85N

5×10-5

2.5×10-4

5×10-4

25N
75N

85N

95N

105N

75N

85N

95N

105N

Fig.8 Characteristic of cloud at 12z on July 8 in

Fig.9 Same as fig.8 except for at 12z on July 8 in

1998. (a) IR data from METEOSAT-5, and (b)1998.

calculated cloud same as fig.6 and 7.