The Advances in Targeted Observations for Tropical Cyclone Prediction Based on Conditional Nonlinear Optimal Perturbation (CNOP) Method - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 579

a b c

CNOP CNOP CNOP

37N 12h 37N 24h 37N 36h

31N 10

8
6
4
2

31N 10

8
6
4
2

31N 10

8
6
4
2

25N 25N 25N

19N 19N 19N

13N 13N

13N

111E 120E 129E 138E 111E 120E 129E 138E 111E 120E 129E 138E

Fig. 24.7 TC Meari (2004). The vertically integrated energies of CNOP ( shaded , units: J/kg) for
the first approach. ( a ) for 12 h forecast, ( b ) for 24 h forecast, and ( c ) for 36 h forecast (From Zhou
and Mu 2012b )

a b c

CNOP CNOP CNOP

12h 24h 36h

39N 39N 39N

10
8
6
4
2

10
8
6
4
2

10
8
6
4
2

33N 33N 33N

27N 27N 27N

21N 21N 21N

15N 15N 15N

111E 120E 129E 138E 111E 120E 129E 138E 111E 120E 129E 138E

Fig. 24.8 Same as Fig. 24.7 , but for TC Matsa (2005) (From Zhou and Mu 2012b )

For the second approach, the sensitive areas of the linear case move to the
verification areas as the initial time is shifted closer to the forecast time (i.e., as
the optimisation period is shortened; Fig. 24.9 ). This result is consistent with the
results of previous studies that applied linear methods to cases that permitted linear
approximation ( Palmer et al. 1998 ; Kim et al. 2004 ; Wu et al. 2007 ). In such
case, the background field such as the subtropical high plays an important part
in the corresponding targeted forecasts. In the nonlinear case, the sensitive areas
fall in disrupted-ring patterns around the initial typhoon centres, and are mainly
located inside the typhoon circulation (Fig. 24.10 ). This indicates that the targeted
forecasts in this case are affected primarily by conditions within the typhoon,
while the background fields play a relatively smaller role. The results of these two
cases suggest that the deployment of targeted observations intended to improve the
forecast at a special time may depend strongly on the time of deployment. The time
at which the targeted observations are deployed is thus of crucial importance.
Generally, the results of this study have shown that the deployment of targeted
observations to improve a special forecast depends strongly on the time of deploy-
ment and it should be adaptive to achieve large improvements for different targeted
forecasts.