GSI/WRF Regional Data Assimilation System and Its Application in theWeather Forecasts over Southwest Asia - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 602

work reasonably well except for precipitation (personal communication). Often, we
run three outer iterations so we can see the fit to the observations at the end of the
second outer iteration. The background error variances, which vary by wavenumber
and vertical mode, are fixed in time and estimated from scaled differences between
24- and 48-h forecasts valid at the same time (see Parrish and Derber 1992 ). For the
regional system, the background error statistics use the same vertical grid structure
as the first guess. The background error covariance matrix is extracted through the
interpolation of NCEP's Global Forecast System (GFS) counterpart.
The observation error covariance matrix (R) should not only contain information
on the observational error but also errors in representativeness ( Lorenc 1986 ).
Thus, this matrix should include the error in the radiative transfer modeling, but
the specification of this matrix is difficult. It is clear that the errors are probably
correlated spatially because of the errors in the radiative transfer, instrument
errors and errors arising from imperfect cloud clearing, emissivity correction, and
other components. However, these correlations are probably quite different from
the spatial correlations found in the temperature and moisture retrievals and are
currently not well known. For this reason, the GSI system has chosen these errors
to be spatially uncorrelated. In addition, because the microwave inter-channel error
correlations are not known, they have been set equal to zero.
For the radiance data, the transformation is more complicated. The temperature,
moisture and pressure on the Gaussian grid are bilinearly interpolated in the
horizontal to the observation region to create a temperature and moisture profile.

25.3 Observed and Analyzed Datasets

Observed precipitation. The observed precipitation data are taken from the Climate
Prediction Center (CPC) Famine Early Warning System (FEWS) program, which
is derived from geostationary satellite retrieved precipitation merged with rain
gauge and model analysis. The merging technique has been shown to significantly
reduce bias and random error compared to individual precipitation data sources, thus
increasing the accuracy of rainfall estimates ( Xie and Arkin 1996 ). Geostationary
satellite data is utilized for the determination of cloud top temperature. METEOSAT
5 thermal Infrared (IR) digital data at 5 km pixel resolution is accessed every

30 min

0:1 ı resolution. The

and then reformatted and converted to a geographic grid with a

20 ı E, 10 ı N and ends grid is 751 501 points, which begins with point (1, 1) at

0:1 ı was chosen for
the estimated computations to correspond with the absolute positioning error for the
satellites of approximately 10 km. Arrays are used to accumulate the occurrences of
cloud top temperatures below

95 ı E, 60 ı N. A horizontal resolution of at point (751, 501) at

275 ı K. Rain gauge reports transmitted
via the Global Telecommunications System (GTS) are received every 6 h and are
utilized in the CPC Climate Assessment Data Base (CADB) for monitoring of
climate anomalies. Automated quality control of these GTS observations within the
CADB is done prior to the processing of precipitation estimates.

235 ı Kand