| Abstract: | Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones, namely Higos, Nangka, Saudel, and Atsani, over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020. The conditional nonlinear optimal perturbation (CNOP) method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time. The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity, using the Weather Research and Forecasting model. It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent. However, assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast, either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data. Therefore, these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity. |
