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YAO Jiawei, DUAN Wansuo. 2022. Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 83−97. doi: 10.3878/j.issn.1006-9895.2103.20256
Citation: YAO Jiawei, DUAN Wansuo. 2022. Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 83−97. doi: 10.3878/j.issn.1006-9895.2103.20256

Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation

  • According to the Nonlinear forcing singular vector type (NFSV-type) sea surface temperature (SST) forcing errors of 12 tropical cyclones (TCs), the optimal target observation deployment of the sea surface temperature is identified through the observing system simulation experiments (OSSEs). The sensitive area of NFSV-type SST forcing errors occurs along the track of TCs and during the intensification phase of TCs. Results of OSSEs show that the additional observations deployed in the sensitive area with intervals of 90 km effectively improve the simulation of the TC intensity. Compared with the observations deployed in nonsensitive areas, the target observations in the sensitive areas of NFSV-type SST forcing errors can more effectively improve the simulation of the TC intensity. When the local target observation area in the nonsensitive area reaches the sensitive area, the additional observations will gradually improve the simulation of the TC intensity. In particular, further experiments show that the simulation of the TC intensity improves more significantly when the target observations deployed during the intensification phase correspond to the periods of the occurrence of the large-value region of NFSV. Therefore, the additional observations in the target area identified using NFSV-type SST forcing errors can improve the simulation of the TC intensity most effectively, and the suitable deployment of observations in the sensitive area during the sensitive periods is the most economical observation strategy.
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