| dc.description.abstract | Ensemble mean is used to represent the best estimation of nature state and the deviation from the mean are used to approximate the forecast uncertainties. However, when dealing with the strong nonlinear dynamics, the ensemble mean and its evolution may not be as reliable as we hope. Thus, we try to solve this problem through the mean recentering scheme (MRS) with the typhoon Nanmadol (2011) case study.
This research consists of two parts. In the first part, we examine the track forecast uncertainties of Typhoon Nanmadol (2011) by the EC global ensemble prediction system from TIGGE database. The result from Good Group and Poor Group comparison indicates that the TCs in Good Group are stronger, moving faster and with larger TC size. In the sensitivity region experiment, we found that the saddle field between Typhoon Nanmadol and Typhoon Talas, the outer circulation of Typhoon Nanmadol and the interaction between Typhoon Nanmadol and Typhoon Talas are the sensitivity region for Typhoon Nanmadol’s sudden recurvature.
The second part is to examine the ability of mean recentering scheme with ensemble forecast system (EPS) and ensemble data assimilation system (EDAS). For the EPS, we hope MRS is able to improve the initial distribution of ensemble members so that the ensemble might keep the suitable distribution during its evolution. With the flow-dependent characteristics, we expect the EDAS could retain the improved ensemble information to ameliorate the ensemble dynamic evolution and prediction skill.
The results of EPS experiment show that MRS is capable of improving the TC track forecast and its ensemble spread can represent the forecast uncertainties reasonable. However, how to select the best member is critical for MRS. In the EDAS experiment, due to the lack of observation and incomplete TC structure of initial filed, it is extremely difficult to establish an accurate background error covariance in time. Nevertheless, with the MRS, we can estimate a more reasonable background error covariance and the better information would be carried by EDAS to the next analysis time to improve entire EDAS. Also, using the top members’ average as the best member provides a better result. | en_US |