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 593

at other times. So it is suggested that in the nonlinear case, the deployment of
targeted observations should be adaptive to obtain the largest improvement for
different targeted forecasts, and should be more widespread to achieve the greatest
improvement in multiple time forecasts. When the forecast time is fixed, the CNOP
sensitive areas are much different when they are identified at different time period
ahead. So the deployment of targeted observations to improve a special forecast
depends strongly on the time of deployment.
Then the efficiency of the CNOP sensitive areas has been examined with a lot of
events. The influence of the initial errors in various areas on the targeted forecast
has been investigated based on two typhoons. Forty random initial errors have been
added to the initial conditions in 40 same-size areas, and it is found that generally
the initial errors in the CNOP sensitive areas would have the largest impact on the
forecast.
Next, the observing system simulation experiments (OSSEs) have been per-
formed to assess whether the CNOP sensitive areas can be considered as dropping
sites in real time targeting. It is demonstrated that the energy of prediction error
could be reduced by assimilating the ideal observation data in the CNOP sensitive
areas for three typhoon cases. Another study of seven typhoon events originated
in the western North Pacific during the 2009 season have showed that assimilating
the ideal observations in the CNOP sensitive areas resulted in the improvements
of 13-46 % in typhoon track forecasts, while the improvements of 14-25 % are
obtained by assimilating the ideal observations in the SV sensitive areas. Besides,
the improvements can be achieved for longer forecast times.
Finally, the observation system experiments (OSEs) using the DOTSTAR Data
have been carried out to further examine the efficiency of the CNOP sensitive areas.
Results show that the DOTSTAR data in the CNOP sensitive areas have a more
positive impact on the typhoon track forecast than that in the FSV sensitive areas.
All the above results demonstrate that the CNOP is a useful tool in the adaptive
observations to identify the sensitive areas.
Nevertheless, there are spaces remained to be further studied. For example,
there are cases that the CNOP sensitive areas have negative impact on the typhoon
forecast, analyze the reason, study more cases, and summarize the conditions when
the CNOP sensitive areas would be much effective is needed. Except for the studies
with horizontal resolution, the other studies have used a lower resolution of 60 km,
it is necessary to use higher resolutions to study once the computational resources
have been improved. The calculation of CNOP in current studies have been based
on MM5 model, and it is known that the MM5 is not to be developed, so using
new advanced models to calculate CNOP is urgent. Recently, Wang et al. ( 2011 )
have used the advanced model WRF to calculate CNOP, so using the CNOP based
on WRF to identify sensitive areas will be an important new study. Furthermore,
since in current formulation the cost function have been designed as the total dry
energy in the verification area, it is expected that new cost function would be better
designed thus it could directly relate to our intensions such as the track or intensity
forecasts of the typhoons and could guarantee the improvement of the typhoon
forecasts, and so on.