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in 2-m temperature forecasts are closely related to the terrain configuration. The
simulated diurnal variation of near-surface temperature is much smaller than the
observed diurnal variation.
Second, to understand the impact of initial conditions on the accuracy of the
model forecasts, the satellite radiances are assimilated into the numerical model
through GSI data assimilation system. The results indicate that on average over
a 30-day experiment for the 24- and 48-h (second 24-h) forecasts, the satellite
data provides beneficial information for improving the initial conditions and the
model errors are reduced to some degree over some of the study locations. The
diurnal cycle of some forecast variables can be improved by using adequate initial
conditions with satellite radiance data assimilation.
25.1
Introduction
The assimilation of satellite radiance observations into a numerical weather pre-
diction (NWP) system is an important pathway for improving weather forecasts
by providing initial conditions representative of the true state of the atmosphere.
Preliminary impact studies of satellite data using satellite retrieved winds, and
humidity were focused on the global system. The results show a positive impact of
satellite data on numerical weather prediction forecasts, especially in the Southern
Hemisphere (e.g., Tracton et al. 1980 ; Halem et al. 1982 ; Andersson et al. 1991 ;
Mo et al. 1995 ; Derber and Wu 1998 ). The satellite data are a useful data source not
only in global models but also in regional-scale models. Bouttier and Kelly ( 2001 )
demonstrated that the impact of rawinsonde data on the forecast was extremely large
over regional areas, but the aircraft and satellite data seemed to have little effect.
There are two basic approaches to assimilate satellite information into a data
assimilation system (DAS). The first approach is to assimilate retrieved data from
radiances measured by satellite instruments. The satellite retrievals, such as humid-
ity and wind fields, usually were provided by the satellite data provider independent
of the data assimilation system. The second approach is to assimilate radiance
measurements directly into a DAS. Direct radiance assimilation is theoretically
superior to retrieval assimilation because the observational error statistics are
more justified in direct radiance assimilation than in retrieval assimilation ( Eyre
et al. 1993 ; Derber and Wu 1998 ; McNally et al. 2000 ). This approach differs
from the traditional practice of transforming the observations into analysis variables
and requires an observation operator be built into the DAS to transform model
variables into radiances. The linkage between forecast model state variables, such
as temperature and humidity, and observed radiances is expressed mathematically
by a forward radiative transfer model (RTM), which calculates radiance from model
state vertical profiles.
To maximize the benefit of assimilating satellite data, it must be assimilated
in the regional models in addition to the global models. Regional models have
lagged global models to some extent, due to the complications from local and
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