Application of ensemble data assimilation system and high-resolution coupled model in Tropical Cyclone prediction
| Autor: | Kuan-Jen Lin, 林冠任 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The main goal of this dissertation is to construct a coupled ensemble TC assimilation and prediction system to explore the challengess in regional coupled data assimilation for TC analysis and prediction. The coupled ensemble assimilation and prediction system is constructed by coupling a high-resolution coupled model UWIN-CM (Unified Wave Interface-Coupled model) and an ensemble data assimilation system LETKF (Local Ensemble Transform Kalman Filter). Under the UWINCM-LETKF framework, issues in TC ensemble data assimilation (EDA) and coupled model prediction are explored with a real TC study of Fanapi (2010). The first part investigates the problem of TC position uncertainty in current ensemble TC assimilation system. We have demonstrated the detrimental impact of TC position uncertainty on ensemble TC assimilation. The TC-centered (TCC) assimilation framework is adopted as a solution and evaluated with a real case study of Fanapi. Results show that with the TCC framework, the analyzed TC structure is in better agreement with independent observations. The improved TC analysis has alleviated the model shock during the early period of forecast, but the impact on intensity prediction is mixed with a better minimum sea level pressure and overestimated peak winds. We also examined the impact of two-way TC-ocean interaction on TC prediction. based on the coupled ensemble forecast from UWIN-CM. Results have demonstrated that TC-ocean coupled effect has led to weaker, smaller, more asymmetry TC, and have a northward track deflection. Analyze the coupled correlation between atmosphere and ocean variables provided us some insight of coupled model behavior in preparation for performing coupled data assimilation. In the end, the capability of UWINCM-LETKF on TC analysis is evaluated. The impact of adopting a coupled model in the forecast-analysis cycle during the atmosphere data assimilation is first discussed. Verified against the collocated atmosphere and ocean observations, the SST and near-surface temperature innovation can be more consistent when using the coupled model forecast (background field) under the TCC framework. This result has highlighted the potential of strongly coupled data assimilation, and also suggest that not only the TC assimilation but also the ocean analysis update should be performed under the TC-centered framework. The preliminary result of strongly coupled data assimilation, in which atmosphere observation is used to update the HYCOM temperature, has shown the mixed result, but the improvement can be identified in rear side of TC. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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