| Summary: | 碩士 === 國立臺灣大學 === 大氣科學研究所 === 97 === In order to increase the atmospheric observations of typhoons over the ocean region, besides the satellite data, the data from GPS (Global Positioning System) dropwindsonde lunched by the surveillance aircraft are also important. Under the support of the National Science Council (NSC) and Central Weather Bureau (CWB), the synoptic surveillance missions to improve TC track forecasts has been conducted by DOTSTAR (Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region) in the western North Pacific Ocean since 2003.
Four different sensitivity methods have been employed as the guidance to design flight routes and deployment locations of GPS dropwindsondes for the typhoon‘s synoptic surveillance in DOTSTAR.. The two main strategies of the targted observations are ‘around-storm’ and ‘extra-targeted area’. In this research, the impact of dropwindsonde data on typhoon track simulations under different observing strategies is studied. MM5 3DVAR data assimilation system is adopted to assimilate the dropwindsonde data from DOTSTAR during 2004 to 2006, and to asses the statistical impact on typhoon track simulations.
It is shown that inclusion of all dropwindsonde data in MM5 3DVAR can effectively reduce the 6 to 72-h track forecast error by about 24%. By only assimilating the around-storm dropwindsonde data, the simulation of typhoon tracks can also be improved. On the contrary, assimilating the extra-targeted dropwindsonde data shows slightly improvement of typhoon track simulation.
Typhoon Shanshan (2006) is selected for the case study. Consistent with the results based on the statistics of multiple cases, assimilating all dropwindsonde data can effectively reduce the track forecast error. Overall, the improvement of track by assimilating the extra-targeted dropwindsonde data is more significant than that by assimilating the around-storm dropwindsonde data. Moreover, the combination of around-storm and appropriate extra-targeted dropwindsonde data shows the most track forecast improvement with the northward movement in the later period.
Assimilating only half of the around-storm dropwindsonde data shows similar results to the experiments assimilating all around-storm dropwindsonde data. Comparing to the results of assimilating the extra-targeted dropwindsonde data, experiments by assimilating four of the around-storm dropwindsonde data in each quadrant can have more improvement on the track simulations.
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