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 567

Then the influence of the initial conditions in the sensitive areas on the targeted
forecasts have been examined, and the observing system simulation experiments
(OSSEs) have been performed to assess whether or not the sensitive areas can be
considered as dropping sites in real time targeting. Also, the observation system
experiments (OSEs) have been carried out to demonstrate the utility of the CNOP
method. It is found that the impact of initial errors introduced into the CNOP
sensitive areas on the forecasts is greater than that of errors fixed in the SV
sensitive areas or other randomly selected areas. The OSSEs have shown that
assimilating the ideal observations in the CNOP sensitive areas results 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 have been achieved for longer forecast times.
The OSEs have shown that the DOTSTAR data in the CNOP sensitive areas has
a more positive impact on the typhoon track forecast than that in the SV sensitive
areas.
All the above results have demonstrated that the CNOP is a useful tool in the
adaptive observations to identify the sensitive areas.

24.1 Introduction

Based on predictability studies of tropical cyclones, it is realized that the forecasts
of tropical cyclone tracks and intensity could be improved when accurate initial
analyses are obtained ( Riehl et al. 1956 ; Bender et al. 1993 ; Zhu and Thorpe 2006 ;
Froude et al. 2007 ). Consequently, it is important to supplement observations in
data-sparse areas to obtain an accurate initial analysis. However, placing additional
observation stations in the data-sparse areas is unnecessary; studies have shown
that extensive observations obtained in the general region around the cyclone do not
conclusively improve forecasts over observations obtained only in particular regions
( Franklin and DeMaria 1992 ; Aberson 2003 ). Adaptive observations (also called
targeted observations) are intended for this purpose: observational capabilities are
intensified in areas where additional observations are expected to improve a forecast
largely. These areas are considered “sensitive”, in the sense that changes to the initial
conditions in these areas are expected to have a larger impact on the forecast than
changes in other areas. It is “adaptive” in the sense that the sensitive areas may
change from day to day and case to case ( Bergot 1999 ).
Currently, there are several strategies used for identifying the sensitive areas. One
strategy is based on the adjoint technique, such as singular vectors (SVs, Palmer
et al. 1998 ), adjoint sensitivities ( Ancell and Mass 2006 ), and the adjoint-derived
sensitivity steering vector (ADSSV) ( Wu et al. 2007 ). Another is ensemble-based,
for example, the ensemble transform ( Bishop and Toth 1999 ), the ensemble Kalman