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In-Depth Information
a
Temperature 2m Diurnal Cycle Variability (Maximum-Minimum)
18
16
GTS
DA
CTRL
14
12
10
8
6
4
2
0
A
B
C
D
E
F
G
I
averaged
b
Wind Speed 10m Diurnal cycle variability
9
8
7
ANAL
DA
CTRL
6
5
4
3
2
1
0
A
B
C
D
E
F
G
h
I
averaged
Fig. 25.18 Diurnal variation over the sub-regions of Southwest Asia shown in Fig. 25.1 for
( a ) 2-m temperature ( ı C) and ( b ) 10-m wind speed (ms 1 ) (Updated from Xu et al. ( 2009 ))
model forecasts of these three surface variables increases slightly after the satellite
data assimilation for 24- and 48-h forecast. For a 30-day average in 24-h forecasts
(Fig. 25.17 a-c), the correlation coefficient in the CTRL gets to 0.973, 0.268 and
0.575 for 2-m temperature, 10-m wind speed and precipitation, respectively. The
corresponding values for the DA experiment are 0.975, 0.280 and 0.581. The 48-h
forecasts have show results (Fig. 25.17 d-f). The results indicate that the forecast
pattern improvement is very limited although the correlation coefficient increases in
the DA experiment.
25.6.2.4
Diurnal Variation of Near Surface Temperature and Wind Field
The analysis of near surface temperature and wind field variability is based on the
eight selected sub-regions (the Arabian Sea (H) was omitted due to the lack of GTS
temperature data there). The diurnal variation of the 30-day mean 2-m temperature
is presented in Fig. 25.18 . It is apparent that the amplitude of the diurnal cycle
in model forecasts of temperature in the CTRL and DA are relatively lower than
in the GTS observations over seven of eight selected sub-regions (Fig. 25.18 a).
Note that the amplitude of the diurnal cycle in the DA experiment is closer to the
GTS observations than in the CTRL experiment. These results demonstrate that the
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