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 566

Chapter 24
The Advances in Targeted Observations
for Tropical Cyclone Prediction Based
on Conditional Nonlinear Optimal Perturbation
(CNOP) Method
Feifan Zhou, Xiaohao Qin, Boyu Chen, and Mu Mu
Abstract In this chapter, we review the recent progresses in targeted observations
for tropical cyclone prediction based on Conditional Nonlinear Optimal Perturba-
tion (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.