GAME's contribution to GEWEX Water and Energy Balance Study
Kooiti Masuda (1)
(1) Frontier Research Center for Global Change
To study global energy and water cycle we mainly use global data sets, but we
also need to evaluate and improve them by using more data from regional and/or
experimental observations such as GAME. Here I review several achievements of
GAME relevant to global water and energy balance study.
(1) GAME Reanalysis is produced by a collaboration based at Meteorological
Research Institute of Japan
Meteorological Agency. It is global meteorological data assimilation taking
experimental observations of GAME, SCSMEX and others as well as routine
observations. It covers 7 months (April to October 1998). Monthly mean
precipitation of the product (model forecasts) is less biased in many regions
of the world than other reanalysis products. The data set was successfully
used in an atmospheric energy budget study around Tibetan Plateau
(Ueda et al.) and
a study of sources of water vapor in Southeast Asia (Yoshimura et al.)
(2)Daily precipitation data have been contributed to GAME from operational
agencies of many countries of Asia. With the enhanced coverage, spatial
features of precipitation in Southeast Asia related to orography are revealed
much better than in currently available global data sets. The difference of
data has large impact on water balance in some areas such as the upper
Irrawaddy river basin (though station coverage seems to be not yet sufficient
there).
(3) AAN (Asian Automated weather station Network) has made continuous
observations of surface energy fluxes at various locations in Asia.
GAME-Radiation and later SKYNET have made precise observations of radiative
fluxes at the surface. Comparison of these in-situ data with global data
products (either satellite retrievals or data assimilation products) is useful
for evaluation of global products. The difference may also due to
representativeness problems of in-situ observations, however. We need more
case studies to have better understanding and eventually improve the global
data sets.