| 摘要: | 一套全新的MPAS-GSI系統結合美國國家大氣研究中心（NCAR）所發展的全球跨尺度天氣預報模式（MPAS），與中央氣象局（CWB）和NCAR開發調整的兩種不同系集變分混成資料同化系統（CWB-GSI、DTC-GSI）用來改善颱風預報的初始場。由於熱帶氣旋的生命週期以海洋上為主，但海洋上經常缺乏傳統觀測資料，而模式同化的觀測資料通常仰賴傳統觀測。因此近年發展新的全球定位系統無線電掩星資料（GPSRO），利用無線電掩星觀測的技術，可以得到高解析的大氣垂直剖面線資料，在缺少觀測的海洋上是一大幫助。
本研究將以此新穎的方法（MPAS-GSI）並加入GPSRO觀測，來探討對2016年的強烈颱風尼伯特（Nepartak）進行個案模擬並探討RO觀測對於MPAS模式的影響。實驗分為兩大部分，第一部分使用CWB的DMS key來構成GFS/GSI及MPAS系統的架構，共分高低解析度兩組進行，一組使用60-15公里，另一組使用60-15-3公里的模擬，並加入渦漩初始化（bogus）的過程。從實驗中得知無論高低解析度的實驗有同化RO資料的實驗均能改善MPAS模式的預報路徑，也與ECMWF分析場的區域校驗較為接近。以上本實驗達成了GSI往MPAS單向的效果，在後期建置雙向系統時遇到了技術上的困難，因此引進第二套系統企圖解決問題。
第二部分使用NCAR提供的MPAS-GSI的全新架構，成功地解決技術問題並達成MPAS-GSI雙向溝通的系統，這部分的實驗同樣探討RO觀測對於MPAS模式的影響。實驗分為變分資料同化系統（3DVar）與系集混合變分資料同化系統（Hybrid），並比較兩者差異。五天的預報路徑結果顯示兩者方法均在沒有bogus的狀況下都能成功登陸台灣，且有同化RO資料的個案仍有改善颱風的預報路徑，但誤差不如第一部分準確。由於這套全新的雙向系統還在調整與測試階段，還有不少的改善空間，未來能期待更多的發展。
;Model for Prediction Across Scales-Atmosphere (MPAS-A) is a new global non-hydrostatic atmospheric model, which uses unstructured variable resolution meshes with smoothly varying mesh transitions, well suited for a higher-resolution mesoscale atmosphere simulation. For typhoon prediction, observations over ocean are important for a numerical model to better represent the real atmosphere. Global Positioning System (GPS) radio occultation (RO) data have characteristics of global coverage, high vertical resolution, and high accuracy, so they can provide useful information for the data-sparse region.
In this study, GPSRO data are assimilated with GSI, and the analysis is provided as the initial condition for the MPAS-A model using a new technique. The impact of RO observations to the track forecast of Typhoon Nepartak (2016) is investigated. The study consists of two parts: In the first part, the Central Weather Bureau Grid-point Statistical Interpolation (GFS/GSI) hybrid system is used for the assimilation, and the MPAS model forecasts are conducted with 60-15 and 60-15-3 km resolutions. The typhoon bogus is employed during the assimilation process. The experiments with RO data show better track forecast in both high- and low-resolution configurations, and the analysis fields are comparable with the ECMWF analysis. However, these results are obtained using one-way connection from GSI to MPAS; i.e., the data assimilation cycles are not conducted using the MPAS model itself. We tried to build the two-way assimilation system, but we encountered some serious technical problems.
To solve the two-way issue, in the second part, we successfully set up a brand new MPAS-GSI framework developed by NCAR. The impact of the GPSRO data to the typhoon forecasts are thus studied in both 3DVar and Hybrid methods using the MPAS/GSI system. The five-day forecast results show that the typhoon still makes landfall at Taiwan without the bogus process and that the cases with RO data exhibit better track forecasts. However, the track errors in these experiments are not as accurate as those in the first part. As this new two-way system is still at the testing stage, there is still a lot of room for improvement, and more developments can be expected in the future. |
