109Intercomparison of the Relationship between Precipitation andElevation among Gridded Precipitation Datasets over
the Asian Summer Monsoon Region
Osamu ARAKAWA* and Akio KITOH
Meteorological Research Institute
1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan*e-mail: oarakawa@mri-jma.go.jp

Abstract
The authors conducted an intercomparison of the elevation dependency of precipitation over the Asiansummer monsoon region among 16 gridded precipitation datasets that consisted of rain-gauge-based (GAUs),
satellite-based (SATs), and gauge-satellite combination datasets (MRGs). The elevation dependencies of themean terrestrial precipitation in the gridded precipitation datasets were qualitatively consistent. The SATs
were generally less than the GAUs at low and middle altitudes, but comparable at high altitudes. The scatter ofthe differences among the GAUs was smaller than that of the SATs. The elevation dependency of the mean
intensity of the heavy terrestrial precipitation, defined as the 95th percentile of daily precipitation, was similar
to that of the mean precipitation. The scatter of the differences in the elevation dependency among the SATs
was greater than that among the GAUs. These results imply that differences in spatial interpolation methods
have little impact on the differences among GAUs but that differences in the rainfall retrieval algorithm havea large impact on the quantitative differences among SATs.
Key words: elevation dependency, gridded precipitation, rain-gauge, satellite

1. Introduction
Precipitation is one of the most fundamental andimportant physical variables for describing weather andclimate. Gridded precipitation datasets have been used instudies on variations with time scales ranging from lessthan a day to decades. In addition to averages, recent
growing concern has focused on extreme events closely
related to natural disasters (e.g., floods and droughts).
Precipitation is also important to numerical weather
and/or climate models, which are powerful tools forlong-term climate research as well as short-term weather
forecasts. Therefore a gridded precipitation dataset withfiner temporal resolution as well as higher spatial resolution is required.
This study performs an intercomparison among gridded precipitation datasets to examine differences among
rain-gauge-based datasets, and among satellite-based
datasets. It also examines the differences between
rain-gauge-based datasets and satellite-based datasets.
The target area is the Asian summer monsoon region(Fig. 1). Through intercomparison, it analyzes the features of and differences between rain-gauge-based andsatellite-based datasets.
The authors focus on the elevation dependency of
Global Environmental Research
15/2011: 109-118
printed in Japan

precipitation. Water is essential to all life on the Earth.
Most sources of the water that is essential to humanactivities are rivers, ground water and lakes. Their origin
is precipitation over mountainous regions. Intercomparison of gridded precipitation datasets would be useful inclarifying the characteristics of precipitation at high altitudes in the present-day climate and in validating the
reproducibility of precipitation at high altitudes in climate models.
The authors chose the Asian summer monsoon region,
including the Tibetan Plateau, because the Tibetan
Plateau and surrounding mountainous regions are theheadwater regions of large rivers (e.g., Yellow, Yangtze,
Red, Mekong, Brahmaputra, Ganges, Indus, Tarim,
Amudar’ya, and Syrdar’ya Rivers), with more than one
third of the Earth’s population living in the catchment ofthese rivers (World Bank, 2010). The water usage
demands of the people who live in arid or semi-arid regions depend largely on rivers whose headwater regions
are far from where they live.
Many studies have compared gridded precipitation
datasets around the globe (e.g., Adler et al., 2001) or incertain regions (e.g., Rajeevan et al., 2006 for India; Zao& Fu, 2006 for China; Roongroj & Chiu, 2008 for
Thailand; Schiemann et al., 2008 for Central Asia;©2011 AIRIES110

O. ARAKAWA and A. KITOH

Kubota et al., 2009 for Japan). Most previous studies on
the elevation dependency of precipitation have used rain
gauge observations and focused on a relatively small
spatial scale (e.g., Sasaki & Kurihara, 2008 for Japan;
Gouvas et al., 2009 for Greece; Kuraji et al., 2009 fornorthern Thailand; Suprit & Shankar, 2008 for the Indian
west coast). No study has focused on the elevation dependency of precipitation in gridded precipitation datasets.
The rest of this paper is organized as follows. Section
2 briefly describes the gridded precipitation datasets usedin this study. Section 3 describes the analyzed region andtopography. Sections 4, 5, and 6 present the results. Sections 7 and 8 provide a discussion and concluding
remarks.

2. Datadaily, and the others are monthly. One of the SATs ismonthly, and the others are daily or sub-daily. For the
sub-daily datasets, the authors use their daily accumulation in this study. No GAU used in this study distinguished rainfall from snowfall. The period analyzed inthis study was 1979 through 2004 for the GAUs, except
for that of CRU_TS2.1, which was 1979 through 2002,
due to the limitation of that rain gauge dataset. For the
SATs, the authors used their time series for as long as
they were available due to their shorter time in existencecompared with the GAUs and MRGs. Inconsistency inthe periods of time series among the SATs may have hadsome impact on the intercomparison among the SATs.
For the MRGs, the analyzed period was the same as thatof the GAUs.

3. Region and Topography

Gridded precipitation datasets are roughly classified
into three categories: rain-gauge-based (GAUs), satellite-based (SATs), and gauge-satellite combination datasets (MRGs). Rain-gauge observations and an objective
analysis technique are utilized to construct GAUs.
Production of SATs utilizes information acquired by
sensors installed in satellites and a physical-model-based
retrieval algorithm that has been actively developed forthe last few decades. Rain gauge observations and information from satellites are synthesized to construct MRGs.
In this study, the authors used 16 gridded precipitation
datasets (Table 1) obtained via the Internet. Six are GAUs,
eight are SATs, and two are MRGs. Two of the GAUs are

Figure 1 shows the spatial distribution of the region
analyzed in this study (65° E to 130° E, 5° N to 35° N).
The topography dataset used in this study was GTOPO30,
developed by the US Geological Survey (USGS). The
horizontal resolution is 1/120°. The topography in thetarget region is characterized by highlands mainly occupied by the Tibetan Plateau and by some mountain
ranges.
Our focus was on the elevation dependency of precipitation. Figure 2 illustrates the elevation dependenciesof the land area in six different horizontal resolutions,one of which is the grid spacing of the topographic data

Table 1 Summary of the gridded precipitation datasets used in this study.
DatasetResolution

APHRO_MA0902

0.5°

CPC_UNIGLB

0.5°

CRU_TS2.1

0.5°

Period (*1)

Interval (*2) Source

Reference

1979–2004

Daily

Rain gauge

Yatagai et al., 2009

Globe

1979–2004

Daily

Rain gauge

Chen et al., 2008

Globe

1979–2002

Monthly

Rain gauge

Mitchell and Jones, 2005

Region60° E–150° E
0° N–55° N

GPCCv4_full

0.5°

Globe

1979–2004

Monthly

Rain gauge

Schneider et al., 2008

PREC/L

0.5°

Globe

1979–2004

Monthly

Rain-gauge

Chen et al., 2002

UDEL

0.5°

Globe

1979–2004

Monthly

Rain gauge

Matsuura and Willmott, 2009

1998–2008

Monthly

Satellite

Iguchi et al. 2000

1998–2006

HourlySatellite

Aonashi et al., 20092004–2008

3-hourly

Satellite

Turk and Miller, 2005

2001–2008

3-hourly

Satellite

Sorooshian et al., 2000

1997–2008

Daily

Satellite (*3)

Huffman and Bolvin, 2009a

1998–2008

3-hourly

Satellite (*3)

Huffman et al., 2007

2003–2006

HourlySatellite

Ushio et al., 2009

2003–2008

3-hourly

Satellite

Joyce et al., 2004

TRMM_PR3A25V6

0.5°

GSMaP/MWR

0.25°

NRL−blended

0.25°

PERSIANN

0.25°

GPCP1dd v1.1

1.0°

TRMM_3B42V6

0.25°

GSMaP/MVK

0.1°

CMORPH

0.25°

0° E–360° E
36° S–36° N
0° E–360° E
60° S–60° N
0° E–360° E
60° S–60° N
0° E–360° E
60° S–60° N
Globe
0° E–360° E
50° S–50° N
0° E–360° E
60° S–60° N
0° E–360° E
60° S–60° N

GPCPv2.1

2.5°

Globe

1979–2004

Monthly

CMAP

2.5°

Globe

1979–2004

Monthly

Gauge+
Satellite
Gauge+
Satellite

Huffman and Bolvin, 2009b
Xie and Arkin, 1997(*1) The period analyzed in this study is presented.
(*2) This column indicates the temporal resolution in each datasets.
(*3) These datasets are calibrated by rain gauges so that their monthly mean is consistent with the monthly mean of rain gauges.

Intercomparison of Elevation Dependency of Precipitation

111

Fig. 1 Spatial distribution of the elevation of the surface [m] over the analyzed region, 65°E to
130°E, 5°N to 35°N.

Fig. 2 Elevation dependency of the land area in the target region presented as the ratio of thearea in each bin to the total land area [%]. The width of each bin is 500 m, except for
both the lowermost and the uppermost bins. The lowermost bin includes altitudes of
500 m or less and the uppermost bin includes altitudes above 5,000 m. The colors andbars are the same as those in Fig. 1. (a) The elevation dependency of the land area in
1/120°×1/120° grids; (b), (c), (d), (e) and (f) the same as (a) except that the grids are(b) 0.1°×0.1°, (c) 0.25°×0.25°, (d) 0.5°×0.5°, (e) 1.0°×1.0°, and (f) 2.5°×2.5°.

used, GTOPO30; the others are the horizontal resolutionof gridded datasets used in this study. This figure indicates that 20% of the land in the target region is locatedat altitudes above 2,000 m, with 10% at altitudes above
4,500 m, due to being on the Tibetan Plateau. It also
indicates that there are small differences among topographic data with different horizontal resolutions.

4. Spatial Characteristics of Mean Terrestrial
Precipitation
Before analyzing the elevation dependency of precipitation, this section describes the spatial characteristics of the mean precipitation in boreal summer(June−July−August, JJA). Figure 3 presents the spatial
distribution of the JJA mean precipitation of 16 gridded

112

O. ARAKAWA and A. KITOH

Fig. 3 Spatial distribution of the June-July-August (JJA) mean terrestrial precipitation of 16 gridded precipitation datasets [mm d-1]
over the target region. ■, ■, and ■ indicate rain-gauge-based, satellite-based and combined datasets, respectively.

8
7

5
4

2.5°[Combined]

2.5°[Combined]

1.0°[Satellite]

0.25°[Satellite]

0.25°[Satellite]

0.25°[Satellite]

0.25°[Satellite]

0.1°[Satellite]

0.25°[Satellite]

0.5°[Satellite]

0.5°[Gauge]

0.5°[Gauge]

00.5°[Gauge]

1

0.5°[Gauge]2

0.5°[Gauge]

3

0.5°[Gauge]

Precipitation [mm d-1]

6

Fig. 4 Area-weighted average of JJA mean terrestrial precipitation [mm d-1] over the target
region. The error bars denote the standard deviation for interannual variability. Bars
with red, pink, light blue, blue, and green are daily rain-gauge-based datasets,monthly ones, monthly satellite-based datasets, daily or sub-daily ones, andgauge-satellite combinations.

Intercomparison of Elevation Dependency of Precipitation

precipitation datasets. Strong precipitation is found over
the windward slope of mountain ranges such as thewestern side of the Western Ghats. The amplitude of suchorographic precipitation in the SATs was smaller than in
the GAUs, even though the spatial resolution of most of
the SATs was finer than that of the GAUs. The spatial
distribution of precipitation in the combined datasets(MRGs) was generally consistent with that of both the
GAUs and the SATs. However, they have poor reproducibility of precipitation distribution associated with
topography because their horizontal resolution is toocoarse to resolve topography with small spatial scales orasymmetrical shapes, i.e., mountain ranges. From theviewpoint of large-scale features, all gridded precipitation datasets used are generally consistent, taking into
consideration the fact that their horizontal resolutions
differ.
Figure 4 shows the area-weighted averaged JJA mean
precipitation over the target region in Fig. 1. The error
bars indicate the standard deviation for interannual
variability of JJA mean precipitation. The area-averaged
mean values of JJA precipitation in the SATs were
smaller than those in the GAUs, but with greater scatter
among the SATs than among the GAUs. For interannual
variability, six (TRMM_PR3A25V6, TRMM_3B42V6,
GSMaP/MWR, GSMaP/MVK, GPCP1dd v1.1, andNRL-blended) had standard deviations smaller thanthose of both the GAUs and the MRGs. The others,
CMORPH and PERSIANN, had standard deviations
greater than those of the GAUs. It is noteworthy that thestandard deviation for interannual variability of some of
the SATs may be overestimated because the length of thetime series was much shorter than those of the GAUs and
MRGs. There was little dependency of mean precipitation on the horizontal resolution of the datasets indicating

113

that the difference resulted from other factors. One suchfactor may be differences in retrieval algorithms used toconstruct the SATs.

5. Elevation Dependency of Mean Terrestrial
Precipitation
This section focuses on the elevation dependency ofmean precipitation. Since precipitation over mountainous
regions is essential to human societies in arid andsemi-arid regions, a gridded precipitation dataset shouldhave precise spatial distribution not only horizontally but
also vertically. In order to describe differences in theelevation dependencies of mean precipitation among
gridded precipitation datasets, the authors used two
diagnostics: (1) the elevation dependency of mean precipitation and (2) the elevation dependency of accumulated precipitation. The former provides information on
the elevation dependency of mean precipitation intensity,and the latter indicates how precipitation amounts are
distributed with altitude.
Figure 5 indicates the elevation dependency of JJA
mean precipitation. Generally speaking, the mean precipitation has its maximum at low altitudes and decreases
with altitude. The GAUs had their maxima at altitudes
below 1,000 m, and secondary peaks at altitudes of
3,500 m to 4,000 m. Values in monthly-based datasets(CRU_TS2.1, PREC/L, GPCCv4_full, and UDEL)
tended to exceed those in daily-based datasets (APHRO_
MA0902 and CPC_UNIGLB) at all altitudes. The mean
precipitation of CPC_UNIGLB is clearly less than that of
the other GAUs at low altitudes. The scatter among the
GAUs was at most about 2 mm d-1 at all altitudes. The
SATs also had their maxima at altitudes below 1,000 m,
and secondary maxima at altitudes of 3,500 m to 4,500 m,

Fig. 5 Elevation dependency of JJA mean terrestrial precipitation [mm d-1]. The spatial resolution
is shown in parentheses in a legend for each dataset.

114

O. ARAKAWA and A. KITOH

slightly higher and more prominent than those of
the GAUs. The elevation dependency of TRMM_
PR3A25V6 was similar to those of the others; however,its secondary peak was relatively weak. Its mean
precipitation was less than that of the GAUs at all
altitudes. Its elevation dependency seemed to be similar
to those of the GAUs rather than the other SATs.
GSMaP/MWR and GSMaP/MVK indicated less precipitation than the others at all altitudes, especially at low
altitudes, and their maxima at altitudes below 500 m wereas great as their secondary peaks at altitudes of 3,500 m
to 4,000 m. The mean precipitations of GPCP1dd v1.1
and TRMM_3B42V6 were comparable to or slightly lessthan those of the GAUs at all altitudes. The mean
precipitations of PERSIANN, CMORPH, and NRL−
blended were less than those of the GAUs at low andmiddle altitudes but comparable at high altitudes.
Although the SATs had elevation dependencies of mean
precipitation qualitatively comparable to those of theGAUs, the scatter of the differences among them wastwice as great as that among the GAUs. The elevation
dependencies of the MRGs were quite bumpy at middle
altitudes, because their grid size of 2.5° was too coarse toresolve the topography at a regional scale in the target
region. GPCPv2.1 exceeded CMAP at all altitudes. The
difference between GPCPv2.1 and CMAP was greater athigh altitudes than at low altitudes. Except for middle
altitudes, their mean precipitations were comparable tothose of the GAUs. Comparison of the GAUs and theSATs indicated that the mean precipitation of the GAUs
exceeded that of the SATs at altitudes below 3,000 m, andthat the GAUs were comparable to the SATs at altitudes
above 3,000 m, with a greater scatter among the SATs
than among the GAUs.
Since the land area varies with altitude, it is worthwhile to examine how accumulated precipitation is
distributed with altitude. Figure 6 shows the elevation
dependency of accumulated precipitation in JJA, as theratio of accumulated precipitation in each altitude bandto the area-averaged accumulated precipitation. The
16 gridded precipitation datasets generally indicatedsimilar elevation dependencies. Eighty percent ofaccumulated precipitation occured at altitudes below
1,000 m, and 10% at altitudes above 2,500 m, most ofwhich occurs on the Tibetan Plateau. The GAUs had
quite similar elevation dependencies, although the ratioof UDEL at high altitudes was relatively larger than thoseof the others. The elevation dependencies of the SATs
were similar to those of the GAUs. However, thedifferences in elevation dependency among the SATs
exceeded those among the GAUs, with the difference inhorizontal resolution between them taken into consideration. For the MRGs, the elevation dependency was
similar to that of the GAUs, with their coarse horizontal
resolutions taken into consideration.

Fig. 6 Elevation dependency of JJA accumulated terrestrial precipitation indicated as the ratio of accumulated
precipitation in each bin to total precipitation [%]. Colors and bins are the same as in Fig. 1.

Intercomparison of Elevation Dependency of Precipitation

6. Elevation Dependency of Heavy Terrestrial
Precipitation
Concern about extreme weather such as heavy precipitation and droughts has been growing year by year
(IPCC, 2007). Therefore, gridded precipitation datasets
are expected to have the ability to handle extreme events.
In this study, our concern with regard to extreme eventswas the elevation dependency of heavy precipitation
because the precise representation of heavy precipitation
is important to the understanding of the elevation dependency of any strength of precipitation, as well as mean
precipitation. In this study, the authors refer to the 95th
percentile or more of daily precipitation as heavy precipitation. They used two GAUs (APHRO_MA0902 and
CPC_UNIGLB), and seven SATs (TRMM_3B42V6,
GPCP1dd v1.1, CMORPH, PERSIANN, NRL−blended,
GSMaP/MWR, and GSMaP/MVK). These are the gridded datasets whose daily precipitation is available for
examining the elevation dependency of heavy precipitation.
Figure 7 indicates the elevation dependencies and thearea averages of heavy precipitation in JJA. For the
GAUs, the mean intensity of heavy precipitation generally decreases with altitude, similar to the mean precipitation, and has its maximum at altitudes below 500 m and asecondary maximum at altitudes of 3,500 m to 4,000 m.
The differences among them are at most 2 to 3 mm d-1 at
all altitudes; therefore, the area averages are almost thesame. The elevation dependencies of the SATs are similar
to those of the GAUs: their maxima are at altitudes below
500 m, and their secondary peaks are at altitudes of

115

3,500 m to 4,500 m, and they are more prominent than in
the GAUs. The differences among them, however, range
from 10 mm d-1 to 25 mm d-1, several times those of the
GAUs. The range of the area averages is about 20 mm d-1,
which is comparable to the mean intensity of the area
averages in the gridded precipitation datasets used.
TRMM_3B42V6 has heavy precipitation that is muchstronger than those of the GAUs, while its mean precipitation is comparable to those of the GAUs at all
altitudes. GPCP1dd v1.1 has the same tendency as
TRMM_3B42V6, but with less heavy precipitation at
low altitudes and more at middle and high altitudes
than TRMM_3B42V6. In CMORPH, PERSIANN,
NRL-blended, and GSMaP/MWR, the heavy precipitation is comparable to or exceeds those of the GAUs,
while the mean precipitation is generally less than thoseof the GAUs. GSMaP/MVK has much less heavy precipitation than the GAUs at all altitudes, except for ataltitudes of 3,500 m to 4,000 m, where it has its
secondary maximum. Comparison of the GAUs with theSATs indicates that the mean intensity of heavy
precipitation in the GAUs is slightly less than that in the
SATs, whereas the scatter of the differences among the
GAUs is much smaller than that among the SATs.

7. Discussion
The elevation dependency of mean precipitationshows that the secondary maximum is more prominent in
the SATs than in the GAUs (Fig. 5). There are a small
number of rain gauge observations at high altitudes
because of the difficulty of maintaining rain gauge

Fig. 7 Elevation dependency of the mean intensity of heavy terrestrial precipitation [mm d-1] in JJA.
Nine datasets whose daily precipitation is available are used. Heavy precipitation is defined asdaily precipitation of the 95th percentile or more. Symbols on the right end are thearea-averaged mean intensity of heavy terrestrial precipitation. The value in parentheses is thehorizontal resolution of datasets.

116

O. ARAKAWA and A. KITOH

observation networks at high altitudes. Thus, the precipitation detected in satellite observations may not be
observed in rain gauge observations. This may be associated with the secondary maximum being more prominent in the SATs than in the GAUs.
The mean intensity of heavy precipitation in the SATs
is generally more than that in the GAUs (Fig. 7). However, the mean precipitation in the SATs is comparable toor less than that in the GAUs (Fig. 5). There is a difference in the temporal characteristics of precipitation
observations between rain gauges and satellites. Precipitation derived from satellite observation is a temporal
average of a rainfall rate which sensors installed in satellites observed. On the other hand, precipitation acquired
from rain gauge observations is an accumulated precipitation during a period (e.g., a day). In addition, the number of wet days (defined as a day of precipitation of morethan 0.1 mm d-1) in the SATs is smaller than that in the
GAUs (fig. not shown). These results may explain the
difference between the GAUs and the SATs in mean
precipitation and heavy precipitation. The difference in
number of wet days between the GAUs and SATs may be
associated with the following. (1) There are the differences among GAUs in the number of rain gauge observations, their spatial distribution and their temporal
resolution. (2) Among the SATs, there may be differencesin rain/no-rain determination in rainfall retrieval algorithms, (3) Some sensors cannot reasonably detect certain
kinds of rainfall such as rainfall from low clouds. (4) The
frequency of observation by low-orbit satellites is not
uniform either temporally or spatially.
The regional averaged mean precipitation shows asmaller standard deviation for interannual variability in
the SATs than in the GAUs (Fig. 4). The difference
between the GAUs and SATs may be partly affected bydifferences in analysis period between the GAUs andSATs. A comparison during the period of 1998 through2004, in which the majority of datasets in this study wereavailable, was performed (fig. not shown). The standard
deviation for interannual variability was comparable
among the SATs. Among the GAUs, CPC_UNIGLB,
UDEL, and GPCCv4_full had standard deviations comparable to the SATs. On the other hand, APHRO_
MA0902 and PREC/L had standard deviations larger
than the SATs. The differences in standard deviation
among the GAUs may have resulted from differences inrain gauge observations (number of observations andtheir spatial distribution) because the spatial interpolation
method used to construct GAUs in this study was
independent of time. In summary, the differences instandard deviation for interannual variability among thedatasets shown in Fig. 4 may result from (1) differencesin analysis period among all precipitation datasets and (2)differences in rain gauge observations (number of gauge
observations and/or their spatial distribution) used in the
GAUs. The differences in standard deviation for interannual variability among the GAUs may be associated withdifferences in the temporal variation of rain gauge
observations used in the GAUs. In APHRO_MA0902,

numbers of rain gauge observations in the year of 1998
and 1999 were considerably greater than those in theother years during the analysis period in this study(Yatagai et al., 2009). Kamiguchi et al. (2010) showedthat the temporal variation in rain gauge observations
affected the long-term trend of annual maximum daily
precipitation as well as that of annual mean precipitation.
Therefore, differences in standard deviation of regional
averaged mean precipitation for interannual variability
imply that caution is needed regarding the impact of thetemporal variation of rain gauge observations on longterm climate analysis.

8. Concluding Remarks
No rain-gauge-based gridded precipitation datasets
used in this study distinguished rainfall from snowfall.
Since the impact on rain gauge observations of the undercatch by evaporation loss and drift of drops and snow
particles by the wind is greater for snowfall than for
rainfall, adjustment of the undercatch for snowfall would
require snow precipitation data to be included in gridded
precipitation datasets. The undercatch in rain gauge
observations has been examined with many types of rain
gauges (Goodison et al., 1998). Yang et al. (2005) applied bias correction for the undercatch to rain gauge
observations over the Northern Hemisphere high latitudes. Adam and Lettenmaier (2003) applied the correction for such bias to UDEL and estimated the impact of
the bias correction method on the climatology of a gridded precipitation dataset. However, there are no historicalrain-gauge-based datasets that use such a method to adjust the undercatch. The development of long-termrain-gauge-based datasets with adjustment of the undercatch is expected.
This study showed that satellite-based datasets havesome problems. Improvements are expected in the
mitigation of quantitative differences among them.
However, their advantage of wide-area, real-time observation is useful for monitoring heavy rainfall events that
often result in natural disasters such as floods andlandslides. The flash-flood monitoring system that wasrecently developed and is being operated by national
weather services in some nations and regions is based on
the fact that satellite observation enables the spatial
distribution of precipitation events to be grasped over awide area in near real-time by combining multiple sensors and/or multiple satellite observations (e.g., Scofield& Kuligowski, 2003). The Global Precipitation Measurement (GPM) project is planned as one of the next
generation of satellite-based precipitation observation
programs. This project will improve precipitation estimates by satellites and increase the accuracy of satellite-based gridded precipitation datasets.
Finally, the authors suggest in this study that gridded
precipitation datasets should be used with considerationof both their own values and their error values. Gridded
precipitation datasets have uncertainties that originate
from observations and spatial interpolation. The authors

Intercomparison of Elevation Dependency of Precipitation

propose the use of the ensemble mean of gridded precipitation datasets and the standard deviation of theiranomaly from the ensemble mean as observed precipitation and its error estimation. Considering uncertainty as
well as precipitation itself would enable us to validate theperformance of precipitation in climate models morequantitatively.

Acknowledgements
This study was conducted as a part of the research
program “Asian Precipitation − Highly-Resolved Observation Data Integration Towards Evaluation of Water
Resources” (APHRODITE project) supported by the
Global Environmental Research Fund (A0601) of the
Ministry of the Environment, Japan. The authors express
their gratitude to U.S. Geological Survey for providing
the digital elevation map, GTOPO30. The authors arethankful to Mr. Kenji Kamiguchi, MRI/JMA, for his
useful discussion and comments. The authors would liketo express their gratitude to two anonymous reviewers fortheir valuable comments.
References
Adam, J. C. and D. Lettenmaier (2003) Adjustment of global
gridded precipitation for systematic bias, Journal ofGeophysical Research, 108, doi: 10.1029/2002JD002499.
Adler, R. F., C. Kidd, G. Petty, M. Morrissey and H. M. Goodman(2001) Intercomparison of global precipitation products: The
Third Precipitation Intercomparison Project (PIP−3), Bulletin of
the Americal Meteorological Society, 82: 1377-1396.
Aonashi, K., J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. Liu, S.
Shige, S. Kida, S. Seto, N. Takahashi and Y. N. Takayabu (2009)
GSMaP passive microwave precipitation retrieval algorithm:
Algorithm description and validation, Journal of the
Meteorological Society of Japan, 87A: 119-136.
Chen, M., P.-P. Xie and J. E. Janowiak (2002) Global land
precipitation: A 50-yr monthly analysis based on gauge observations, Journal of Hydrometeorology, 3: 249-266.
Chen, M., W. Shi, P.-P. Xie, V. B. S. Silva, V. E. Kousky, R. W.
Higins and J. E. Janowiak (2008) Assessing objective techniques for gauge-based analyses of global daily precipitation,
Journal of Geophysical Research, 113, D04110, doi:10.1029/
2007JD009132.
Goodison, B. E., P. Y. T. Louie and D. Yang (1998) WMO solid
precipitation measurement intercomparison, WMO/TD, 872,
212pp., World Meteorological Organization, Geneva.
Gouvas, M., N. Sakellariou and F. Xystrakis (2009) The relationship between altitude of meteorological stations and averagemonthly and annual precipitation, Studia Geophysica etGeodaetica, 53: 557-570.
Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P.
Bowman, Y. Hong, E. F. Stocker and D. B. Wolff (2007) The
TRMM multi-satellite precipitation analysis: Quasi-global,
multi-year, combined-sensor precipitation estimates at fine scale,
Journal of Hydrometeorology, 8(1): 38-55.
Huffman G. J. and D. T. Bolvin (2009a) GPCP one-degree daily
precipitation data set documentation.
<ftp://precip.gsfc.nasa.gov/pub/1dd-v1.1/1DD_v1.1_doc.pdf.>
Huffman, G. J. and D. T. Bolvin (2009b) GPCP version 2.1 combined precipitation data set documentation.
<ftp://precip.gsfc.nasa.gov/pub/gpcp-v2.1/doc/V2.1_doc.pdf.>
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka and K. Okamoto

117(2000) Rain profiling algorithm for the TRMM Precipitation
Radar, Journal of Applied Meteorology and Climatology, 39:
2038-2052.
Intergovernmental Panel on Climate Change (IPCC) (2007)
Climate Change 2007: The physical science basis. Contributionof Working Group I to the Fourth Assessment Report of the
IPCC edited by Solomon et al., 996pp, Cambridge University
Press, Cambridge, United Kingdom and New York, NY.
Joyce, R. J., J. E. Janowiak, P. A. Arkin and P. Xie (2004)
CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial
and temporal resolution, Journal of Hydrometeorology, 5:487-503.
Kamiguchi, K., O. Arakawa, A. Kitoh, A. Yatagai, A. Hamada andN. Yasutomi (2010) Development of APHRO_JP, the first Japanese high-resolution daily precipitation product for more than
100 years, Hydrological Research Letters, 4, 60-64.
Kubota, T., T. Ushio, S. Shige, S. Kida, M. Kachi and K. Okamoto(2009) Verification of high-resolution satellite-based rainfall
estimates around Japan using a gauge-calibrated ground-radardataset, Journal of the Meteorological Society of Japan, 87A:
203-222.
Kuraji, K. M. Gomyo and K. Punyatrong (2009) Inter-annual andspatial variation of altitudinal increase in rainfall over Mount
Inthanon and Mae Chaem watershed, Northern Thailand,
Hydrological Research Letters, 3: 18-21.
Matsuura, K. and C. J. Willmott (2009) Terrestrial precipitation:
19002008 gridded monthly time series.
<http://climate.geog.udel.edu/~climate/html_pages/Global2_Ts_2009/README.global_p_ts_2009.html>
Mitchell, T. D. and P. D. Jones (2005) An improved method of
constructing a database of monthly climate observations andassociated high-resolution grids, International Journal of
Climatology, 25: 693-712.
Rajeevan, M., J. Bhate, J. D. Kale and B. Lai (2006) High resolution daily gridded rainfall data for the Indian region: Analysis ofbreak and active monsoon spells, Current Science, 91: 296-306.
Roongroj, C. and L. S. Chiu (2008) Thailand daily rainfall
and comparison with TRMM products, Journal of
Hydrometeorology, 9: 256-266
Sasaki, H. and K. Kurihara (2008) Relationship between precipitation an elevation in the present climate reproduced by thenon-hydrostatic regional climate model, Science Online Letterson the Atmosphere, 4: 109-112.
Schiemann, R., D. Lüthi, P. L. Vidale and C. Schär (2008) The
precipitation climate of Central Asia − intercomparison ofobservational and numerical data sources in a remote semiarid
region −, International Journal of Climatology, 28: 295-314.
Schneider, U., T. Fuchs, A. Meyer-Christoffer and B. Rudolf(2008) Global precipitation analysis products of the GPCC,
Global Precipitation Climatology Centre (GPCC), Deutscher
Wetterdienst, Internet Publikation: 1-12.
Scofield, R. A. and R. J. Kuligowski (2003) Status and outlook of
operational satellite precipitation algorithms for extremeprecipitation events, Weather and Forecasting, 18: 1037-1051.
Sorooshian, S., K. Hsu, X. Gao, H. V. Gupta, B. Imam and D.
Braithwaite (2000) Evaluation of PERSIANN system satellitebased estimates of tropical rainfall, Bulletin of the American
Meteorological Society, 81: 2035-2046.
Suprit, K. and D. Shankar (2008) Resolving orographic rainfall on
the Indian west coast, International Journal of Climatology, 28:
643-657.
Turk, F. J. and S. D. Miller (2005) Toward improving estimates ofremotely-sensed precipitation with MODIS/AMSR-E blended
data techniques, IEEE Transaction on Geoscience and Remote
Sensing, 43: 1059-1069.
Ushio, K., K. Sasashige, T. Kubota, S. Shige, K. Okamoto, K.
Aonashi, T. Inoue, N. Takahashi, T. Iguchi, M. Kachi, R. Oki, T.
Morimoto and Z.-I. Kawasaki (2009) A Kalman Filter approach

118

O. ARAKAWA and A. KITOH

to the Global Satellite Mapping of Precipitation (GSMaP) fromcombined passive microwave and infrared radiometric data,
Journal of the Meteorological Society of Japan, 87A: 137-151.
World Bank (2010) World Development Report 2010 Development
and Climate Change, The World bank, Washington DC, USA.
Xie, P. -P. and P. A. Arkin (1997) Global precipitation: A 17-year
monthly analysis based on gauge observations, satellite estimates and numerical model outputs, Bulletin of the American
Meteorological Society, 78: 2539-2558.
Yang, D., D. Kane and Z. Zhang (2005) Bias correction of
long-term (1973−2004) daily precipitation over the northern regions, Geophysical Research Letters, 32, L19501, doi:10.1029/
2005GL024057.
Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto, M. I.
Nodzu and A. Hamada (2009) A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain
gauges., Science Online Letters on the Atmosphere, 5: 137-140.
Zao, T. and C. Fu (2006) Comparison of products from ERA-40,
NCEP-2, and CRU with station data for summer precipitation
over China, Advances in Atmospheric Sciences, 23: 593-601.

Osamu ARAKAWA
Osamu ARAKAWA is a Research Associate at theMeteorological Research Institute (MRI) of theJapan Meteorological Agency (JMA). He isinterested in atmospheric science and climatology. He has used climate models to study
climate variations from to intra-seasonal tointer-decadal timescales mainly over the tropics.

Akio KITOH
Akio KITOH is Director of the Climate Research
Department of the Meteorological ResearchInstitute (MRI), Japan Meteorological Agency(JMA). He has a Doctor of Science degree from
Kyoto University. He is leading the projects,“Research on Prediction of Climate and Environmental Change to Contribute to Mitigation Plan
Decision Making Against Climate Change” funded by the Japan
Meteorological Agency (JMA) (FY2010-2014) and “Projection of Change
in Future Weather Extremes Using Super-High Resolution Atmospheric
Models” funded by the Ministry of Education, Culture, Sports, Science andTechnology (MEXT) (FY2007-2011). He was lead author of the IPCC
Working Group I Second, Third and Fourth Assessment Reports and is leadauthor of the IPCC Working Group I Fifth Assessment Report.

(Received 21 May 2011, Accepted 31 August 2011)