GAME’s Contribution to
GEWEX Water and Energy Balance Study
Kooiti Masuda∗(Frontier Research Center for Global Change / JAMSTEC)* Frontier Research Center for Global Change,
3173-25, Showa, Kanazawa-ku, Yokohama 236-0001, Japan. e-mail: masuda@jamstec.go.jp

Abstract
Enhanced observations of GAME provide better data concerning the global energy and water cycle, especiallyover the Asian continent. Improvements achieved by utilizing GAME Reanalysis, operational rain gauge data
and AAN, among others, are discussed.
Keyword: water balance, energy balance, GAME Reanalysis, operational rain gauge data, AAN

1. Water and Energy Balance Study
1.1. The rationale
To understand how the global energy and water cycle
behaves, it seems a good way to look at variables appearing the equations of water balance and energy balance. These variables are well-defined in the context ofthe physical laws of conservation of mass and energy, andthe values of them obtained at different space/time orby different approaches can be compared within the same
framework in principle. The law of mass conservation does
not guarantee the constancy of mass of water substance(water vapor, liquid water and ice combined), but, fromthe past experience, we know that contribution of chemical changes between water substance and other substanceis relatively negligible. Water and energy balance study
is, in its narrower sense, either to estimate one componentof a balance equation that is not directly observed, or toassess the accuracy of our estimation when values for allcomponents are somehow given. In its somewhat broader
sense, it also means to recognize the actual magnitude andthe relative importance of each term in the equations, andhow they vary across the wide range of space and time.
Water and energy balance study in the global scale ispossible only with global scale data sets. Studies in thesmaller spatial scales than global are, however, also important to it. Global data sets inevitably depend on indirect estimation, either by remote sensing or by numerical modeling. Regional studies can utilize more direct
observations, and thus make the quality of evaluation of
global scale balance better. Even spatially and temporally limited experimental observations can be comparedwith global data sets and give suggestions about possible
improvement of global data sets.
Regional studies also contribute to understanding of the
global energy and water cycle by way of inter-regional
comparison. Even people who study something global
usually live in a limited region, and characteristics of climate and hydrology of the region strongly influence theirview of the world. We should know how similar and how
different they are in other regions of the world. In thiscontext it is again important that they are expressed in
the same framework. Attention to variables entering thebalance equations is a good point to start.

1.2. GEWEX Water and Energy Balance Study
Water and Energy Balance Study (WEBS) in thecontext of Global Energy and Water Cycle Experiment (GEWEX) was proposed as a concrete
study item by John Roads (the present chairperson of GEWEX Hydrometeorological Panel (GHP),
based at Scripps Institute of Oceanography, USA) (seehttp://ecpc.ucsd.edu/ghp/WEBS.html). It is one of theattempts synthesize the findings of GEWEX continentalscale experiments (CSEs). GAME is one of the CSEs.
Roads had played an important role in the synthesis of water and energy balance obtained during the
GEWEX Continental-scale International Project (GCIP)
conducted in the Mississippi river basin. He edited a CDROM for GCIP WEBS (Roads et al. 2002a), including
water and energy balance products both global-scale andregional-scale, and both observational and model-derived.
He participated in comparative studies for GCIP (e.g.
Maurer et al. 2001). On the other hand, he also discussed water and energy balance of all CSE regions with
global data only (Roads, 2002, Roads et al., 2002b).
In 2002, a working group for WEBS was organized under GHP with Roads’s initiative, and I became its member
representing GAME. The practical goal of the project is
to make an inter-regional comparison of water and energybalance, similar to Roads et al. (2002b), but with regional
data sets from CSEs in addition to a common set of global
data.
The target of this limited part of WEBS is the aspectsof water and energy balance that can be expressed bymonthly averages. The horizontal resolution is not explicitly specified, but it is implicitly determined from thecharacteristics of global data sets. Their horizontal resolution is nominally between 1.0 degree and 2.5 degrees
latitude/longitude, and some variables such as moisture
convergence C (see below) have actually coarser resolution than nominal. The variables to be assembled anddiscussed is also limited to those appearing in the vertically integrated water and energy balance equations. The
equations can be written as follows in my version of notation. Some of them are not exact but approximate.
• Mass balance of water on an area of land (preferably
a river catchment)dS=P −E−Rdt(1)• Mass balance of water vapor in an atmospheric columndW=E−P +C(2)
dt

• Energy balance at the surface
G = Rnet,sfc − H − LE(3)

• Total energy balance in an atmospheric columnd(ed + LW )= Rnet,top − G + (Cd + LC)
dt(4)• “Dry” energy balance in an atmospheric column
ded= Rnet,top − Rnet,sfc + H + LP + Cd
dt

(5)

where S is terrestrial water storage, W is water vapor content in the atmosphere, P is precipitation, E is evaporationfrom surface, R is runoff, C is convergence of horizontaltransport of water vapor in the atmosphere, ed is ”dry”
energy content in the atmosphere, Rnet,top is net downward radiation at the top of the atmosphere, Rnet,sfc is net
downward radiation at the surface, G is net downward energy flux at the surface (conduction below land surface),
H is net upward sensible heat flux at the surface, Cd isconvergence of horizontal transport of ”dry” energy in theatmosphere, and L is latent heat of vaporization of unit
mass of water.
Obviously, this limited target does not fully represent
the synthesis of activities of CSEs such as GAME. There
are many field campaigns, however intensive, which do
not cover one calendar month and thus cannot contributenumerical values to the intercomparison. There are manystudies that aim at three-dimensional distribution of heatsources in the atmosphere. One of such framework was“Q1 and Q2 ” first formulated by Yanai (1961) and appliedin the context of GAME by Ueda et al. (2003) among others. The vertically integrated monthly statistics is just thefirst step of WEBS, or “Initiative 1” as called by Masudaand Sohn (2003).

2. Atmospheric reanalyses
2.1 Introduction
Data assimilation has become familiar in the context
of meteorology and increasingly also of oceanography andhydrology. A rough description of it is to run a forecast
model (in a computer), continuously updating the forecast variables with observational data, to achieve gaplessgridpoint values which are good approximation of the realworld and physically consistent. Actually the insertion ofdata may be intermittent rather than continuous, and consistency of the values in the products is not ideal. Assimilation of meteorological data is routinely done in real-time
as a part of numerical weather prediction. They can incorporate experimental observations if they are reported in astandardized way in time. Actually, some of enhanced radiosonde observations in GAME were used this way. But
most of experimental observations become available manydays later. To utilize them, data assimilation should bedone in non-real-time manner.
Non-real-time assimilation projects or their products
are often called “reanalyses”. Some of reanalyses covers many decades. U.S. National Centers for Environmental Prediction (NCEP) as well as European Centre
for Medium-range Weather Forecasts (ECMWF) have issued long-term reanalysis data sets. These data setscomprise a major part of “global data sets” utilized in
global WEBS together with global synthesis based onsatellite observations. Also, Japan Meteorological Agency(JMA) and Central Research Institute of Electric Power
Industries (CRIEPI) jointly conduct a project “JRA”(see http://www.jreap.org/) to produce global reanalysis covering a period from 1979 to present. Later versionsof intercomparison of global WEBS will include JRA.

2.2 GAME Reanalysis
In GAME, two versions (1.1 and 1.5) of “GAME
Reanalysis”(GAME-RA) was produced by a team based
at Meteorological Research Institute of JMA (Yamazaki
et al. 2003). (There will be no more product in thisseries because the team is reorganized as a part of the
JRA team.) These are global reanalyses that cover a
7-month period from April to October 1998. The horizontal resolution is “T213” and its grid interval is about
5.6 degrees latitude/longitude. It used a somewhat modified version of JMA’s operational data assimilation system. The scheme for spatial interpolation is “optimal
interpolation (OI)”. It was planned to implement “threedimensional variational method (3D-VAR)” in the Version
2. But it was not successful to produce better results by
3D-VAR than by OI. Thus a revised version with OI is released as Version 1.5 instead of planned Version 2. (Later,
after more research work, 3D-VAR was implemented in
JMA’s operational data assimilation and it is also usedin JRA.) The main difference between GAME-RA andJMA operational global analysis is the richness of input
data. The period of Reanalysis covers the Intensive Observing Period of GAME-Tropics, GAME-Tibet, GAMEHUBEX and also SCSMEX (South China Sea Monsoon
Experiment), TIPEX (Tibetan Plateau Experiment) and
KORMEX (Korean Monsoon Experiment), and many experimental data are contributed in addition to operational
data transmission.
As I compared water balance of GAME-RA with otherglobal data sets (Masuda, 2003), it is found that monthly
mean, continental scale precipitation as derived from theforecast model of the reanalysis was close to observationbased data such as Global Precipitation Climatology
Project (GPCP) in cold region in summer where bothof the two versions of NCEP-RA give excessive precipitation. On the other hand, the balance of atmospheric water
vapor between P , E and C that should vanish, is worse
in GAME-RA than in NCEP-RA. Comparison of evaporation with observations (though they are local and notnecessarily representative of regional averages) suggeststhat forecast models of both GAME-RA and NCEP-RA
over-estimates evaporation. In the tropics, agreement ofprecipitation between GAME-RA and observation-based
data is good in some region and not so good elsewhere.
It is noteworthy that the regional average precipitation in
the Indochina Peninsula is represented well, presumably
due to enhanced data of GAME-Tropics. Distribution of
precipitation within this region is not so well reproduced,
however.
GAME-RA is used in many studies related to waterand energy balance. Noteworthy examples are threedimensional heat and moisture budgets around the Ti-

betan Plateau (Ueda et al. 2003) and transports of water
isotopes around the GAME-Tropics region (Yoshimura etal., 2003). It is also used as boundary and initial conditions for studies by using regional meteorological models.

2.3 Other atmospheric reanalysis activities in GAMEGAME-HUBEX Regional Reanalysis (Wakatsuki et al.
2003) was produced by using JMA’s Regional Spectral
Model (RSM) data assimilation system with input enhanced by experimental observations of HUBEX. It covers
a one month time frame from 19 June to 19 July 1998, at 6hour time interval and 20 km spatial resolution. GAMERA Version 1.1 was used as boundary and initial conditions.
Tanaka et al. (2004) combine JMA’s RSM data assimilation system with a land surface model “SiBUC”, andtry to produce an improved reanalysis of the coupled landatmosphere system.

3. Collection of operational data
3.1. Collection of precipitation data
In addition to experimental observations, agencies inmany countries contributed data of their operational observations to GAME. The coverage is uneven, due partly
to policies of the agencies and partly to interests of thescientists who collected the data.
Among them, the collection of daily precipitation data
in Southeast Asian countries is noteworthy. It is achievedby continuous collaborations between Southeast Asian
countries (especially Thailand) and Japan, enthutiastic
data collection by Jun Matsumoto (Univ. Tokyo), anddata management by Yasushi Agata (Univ. Tokyo) at
http://hydro.iis.u-tokyo.ac.jp/GAME-T/GAIN-T/.

3.2. Impact of precipitation data
Utilizing this GAME-T collection together with othersources (notably the collection by the Mekong River Commission, an international organization based now at Vientiane), Masuda et al. (2004) analyzed the spatial distribution and the seasonal cycle of precipitation in Indochina Peninsula. It is found that the contrast between
maxima in mountainous areas and minima in lowlands islarger than global data sets suggest. It is still difficultfor global forecast models to simulate the pattern of precipitation in this area. The problem of observation-based
global data sets is that few reports from such countries
as Myanmar, Laos and Cambodia arrive at meteorological agencies in Europe or North America where the global
archive is compiled. It happened, however, that the regional average precipitation in the middle Mekong river
basin (between Chiang Saen [Thailand] and Pakse [Laos])is almost the same whether observation-based global data
sets such as GPCP or the enhanced rain gauge data areused. In the upper Irrawaddy (above Sagaing [Myanmar])the difference is significant, though comparison with riverdischarge suggests that precipitation in this region is still
under-estimated with the enhanced rain gauge data.

4. Regional land surface fluxes
4.1. Asian AWS Network

In GAME, automated weather stations were installedin many locations on the Asian continent to continuously monitor surface energy balance components andrelated variables. Many of them are collectively called
Asian AWS Network (AAN) with the arrangement ofMichiaki Sugita et al. at the University of Tsukuba(http://www.suiri.tsukuba.ac.jp/Project/aan/
aan.html). Data of hourly or half-hourly observations
are available both on CD-ROMs and on the Internet.
Surface fluxes observed by AAN was used for evaluating
the fluxes calculated by the forecast model of GAME-RA(Yatagai et al. 2003).

4.2. Comparison between AAN andsatellite retrievals
AAN data is also useful for evaluating radiative fluxes
at the surface obtained by retrievals of satellite observations. For this purpose, more precise observations aredesirable. In Asia, however, stations of Baseline Surface
Radiation Network do not except except in Western Asia
and Japan. Several precision radiation stations were installed by GAME-Radiation activity and some of themcontinues as part of SKYNET, but, because of difficultyin maintaining precision instruments on the field, theirrecords are generally less continuous than AAN.
On the side of satellite retrievals, the Surface Radiation
Budget (SRB) data set produced by Paul Stackhouse
et al. at NASA Langley Research Center (LaRC, see
http://srb-swlw.larc.nasa.gov/GEWEX SRB homepage.html) is designated as the GEWEX SRB product, but
its coverage is July 1983 to October 1995 as has beenreleased and do not overlap the Intensive Observing
Periods of GAME. The production of SRB uses cloud
information from the “D series” of International Satellite
Cloud Climatology Project (ISCCP). Zhang and Rossow
at NASA Goddard Institute of Space Studies (GISS) who
produced ISCCP data has also made a data set (called“FD”) of radiative fluxes which extends to June 2001
http://isccp.giss.nasa.gov/projects/flux.html.
Rachel Pinker et al. at the University of Maryland
also made “Pathfinder” shortwave flux data which
extends to December 1998 (http://www.atmos.umd.edu/~
srb/pathfinder/). The FD and Pathfinder data sets
have lower spatial resolutions (2.5 deg. lat./long.) than
LaRC SRB (1 deg.), however.
Masuda (2004) compared monthly mean values of
downward shortwave and longwave fluxes at several AAN
stations with those of the nearest grid boxes of SRB, FD
and Pathfinder data sets. Interpretation of the difference
found is difficult, especially in the tropics where there
seems to be significant regional inhomogeneity of cloud
distribution. In some cases, however, the comparisonsreveal systematic problems of satellite retrievals. Downward shortwave radiation as given by SRB and Pathfinder,
which use the same Pinker-Laszlo algorithm, are underestimates in the Tibetan Plateau. It seems that thehigh land elevation and consequently thin atmosphericcolumn are not properly taken into account in the productions. FD over-estimates downward longwave radiation in Siberia in winter and perhaps under-estimates it
in Tibet in summer. It can be traced back to the differ-ence between the actual atmospheric temperature profile
based on TOVS retrievals which was used in the production of FD. SRB does not share this bias because LaRC
used temperature profile from an atmospheric reanalysis(GEOS by NASA), which is likely to better represent realistic vertical profile than TOVS retrievals.

4.3. Evaluation of regional land surface
fluxes
One way to know surface energy and water fluxes in
the regional scale is running a land surface model withnear-surface meteorological data as input.
For the HUBEX region, Tanaka et al. (2001) run
SiBUC on a 5 minute latitude/longitude (approximately
10 km) grid for May to August 1998. Among the input
meteorological data, downward radiation was estimatedby using Geostationary Meteorological Satellite observations. Their team continues improvement of the assimilation.
For the Tibetan Plateau, Xu and Haginoya (2001) and
Xu et al. (2004) use a simple model of energy and watertransfer in the soil layer (assuming that the effect of vegetation is negligible) and operational meteorological data
to obtain surface fluxes at the operational stations in theplateau for 1997 and 1998. The empirical formula used in
the study was calibrated with reference to experimental
observations. Also, the groups lead by Toshio Koike at
the University of Tokyo, Yaoming Ma at Institute of Tibetan Plateau Research, and Hirohiko Ishikawa at Kyoto
University utilize satellite data and land surface models.

5. Concluding remarks
I am afraid I have missed many studies relevant to thissubject. I welcome information about them.
Even though research funding under the name of
GAME is about to end, utilization of the rich information obtained by GAME has just started. Interpretationof it requires more thinking, more comparison, or maybe
modelling studies. We should continue water and energybalance studies and merge our experience of GAME intoa broader knowledge base.

References
Masuda, K., Evaluation of precipitation of GAME
Reanalysis
oncontinents(inJapanese).
Proc. 2003 Spring Meeting of Meteorol. Soc.
Japan, No. P467, 2003. (English version at
http://www.jamstec.go.jp/frcgc/research/p2/
masuda/gamerean/masuda2003/index.html).
Masuda, K., Surface radiation budget: comparison
between global satellite-derived products andland-based observations in Asia and Oceania.
Paper presented at International Radiation
Symposium 2004, Busan, Korea, 2004.
(See
http://www.jamstec.go.jp/frcgc/research/p2/
masuda/radcmp/index.html).
Masuda, K., J. Matsumoto, Y. Agata, B. Ailikun and T.
Yasunari, Precipitation climatology of continental
Southeast Asia. (English version to be linked under
http://www.jamstec.go.jp/frcgc/research/p2/

masuda/index.html).
Proc.
2004 Autumn
Meeting of Meteorol. Soc. Japan, No. A359, 2004.
Masuda, K. and B.J. Sohn, GAME Water and Energy Balance Study.
GAME Letter, No.
5, 8-13, 2003.
(or see Masuda’s draft at
http://www.jamstec.go.jp/frcgc/p2/masuda/
gamewebs/200306.html).
Maurer, E.P., G.M. O’Donnell, D.P. Lettenmaier and
J.O. Roads, Evaluation of the land surface water
budget in NCEP/NCAR and NCEP/DOE Reanalyses using an off-line hydrologic model. J. Geophys. Res., 106, 17841-17862. 2001.
Roads, J., Closing the Water Cycle. GEWEX News, 12,
No. 1, 1 and 6-8, 2002.
Roads, J. et al. eds., GEWEX Continental-scale International Project (GCIP) Water and Energy
Budget Synthesis (WEBS) 1996-1999.
CDROM. 2002a. (See http://ecpc.ucsd.edu/gcip/
gcipwebs.html).
Roads, J., M. Kanamitsu and R. Stewart, CSE waterand energy budgets in the NCEP-DOE Reanalysis II. J. Hydrometeorol., 3, 227 - 248, 2002b.
Tanaka, K., O. Kozan, T. Nakamura, M. Shiiba and S.
Ikebuchi, Land data assimilation in the GAMEHUBEX. 5th Int’l Study Conference on GEWEXin Asia and GAME, 697 - 702, 2001.
Tanaka, K., K. Souma, K. Tsuboki and S. Ikebuchi, Production of final dataset of GAME-HUBEX Regional 4DDA by JSM-SiBUC. This symposium,
2004.
Ueda, H., H. Kamahori and N. Yamazaki, Seasonal contrasting features of heat and moisture budgets between the eastern and western Tibetan Plateau
during the GAME IOP. J. Climate, 16, 2309 2324, 2003.
Wakatsuki, Y., K. Tsuboki, T. Kawahata and H. Nakamura, Production of GAME/HUBEX Regional Reanalysis data set (in Japanese). Proc. 2003 Spring
Meeting of Meteorol. Soc. Japan, No. P428, 2003.
Xu, J.and Haginoya, S., An estimation of heat and waterbalances in the Tibetan Plateau. J. Meteorol. Soc.
Japan, 79, 485 - 504, 2001.
Xu, J., Haginoya, S., Masuda, K. and Suzuki, R., An estimation of heat and water balances in the Tibetan
Plateau in 1997 - 1998. Submitted to J. Meteorol.
Soc. Japan, also in this symposium, 2004.
Yamazaki, N., K. Takahashi and A. Yatagai, Report on
the GAME Reanalysis. GAME Phase 1 Summary Reports (GAME Publ. No. 37), 81 - 87,
2003. (See also http://gain-hub.mri-jma.go.jp/
GAME reanal.html).
Yanai, M., A detailed analysis of typhoon formation. J.
Meteorol. Soc. Japan, 39, 187 - 214, 1961.
Yatagai, A., M. Sugita, N. Yamazaki, M. Oh’izumi,
A comparative study of the surface fluxes derived from meteorological four dimensional data
assimilation products (GAME Reanalysis) with
Asian Automatic Weather Station Network (AAN)
observations over the Tibetan Plateau. Proc.
APHW2003, Asia Pacific Association of Hydrology
and Water Resources, 722 - 727, 2003.
Yoshimura, K., T. Oki, N. Ohte, and S. Kanae, A quantitative analysis of short-term 18 O variability witha Rayleigh-type isotope circulation model. J. Geophys. Res., 108, 4647, doi:10.1029/2003JD003477,
2003.