The Advances in Targeted Observations for Tropical Cyclone Prediction Based on Conditional Nonlinear Optimal Perturbation (CNOP) Method

Abstract

In this chapter, we review the recent progresses in targeted observations for tropical cyclone prediction based on Conditional Nonlinear Optimal Perturbation (CNOP) method. The CNOP is a natural extension of the singular vector (SV) into the nonlinear regime and it has been used to identify the sensitive areas for tropical cyclone predictions.The properties of the sensitive areas identified by CNOP have been first studied, including the sensitivity to the horizontal resolution, the verification area design, and the optimization period. It has been found that the CNOP sensitive areas have similarities at different horizontal resolutions, and a small variation of the verification area has minimal influence on the CNOP sensitive areas. The CNOP sensitive areas identified for special forecast times when the initial time is fixed resemble those identified for other forecast times in the linear case, while the similarities among the sensitive areas identified for different forecast times are more limited in the nonlinear case. When the forecast time is fixed, the CNOP sensitive areas are much different when they are identified at different time period ahead.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.