近年來混合資料同化(Hybrid)方法已成為天氣資料同化熱門研究，目前同化研究分為4大類，分別為3DVAR、4DVAR、卡爾曼濾波器、Hybrid，本研究選用3DVAR與Hybrid兩種方法進行研究。研究選取”尼伯特”颱風登錄過程為實驗個案，在WRF模式基礎上，針對7組實驗進行72h預報。
結果顯示探討流場相關分佈特徵，3DVAR分析增量分佈是以同化點為中心，與實際天氣形勢沒相關性，只與同化單點位置有關; Hybrid循環由於加入系集擾動場資訊，使背景誤差結構產生動態特徵，讓分析增量呈現流場相關特性。
在模擬路徑預報效果，無循環hybrid同化為7組實驗中誤差增長最慢，且最後模擬路徑與實際較為接近。從模擬與觀測比較，使用3DVAR和Hybrid同化，對中心氣壓和最大風速的改善有限。
另外無同化與3DVAR和無循環Hybrid三個的累積降雨量差異性並不大，3種實驗跟實際累積雨量分佈相似;但在雨量累積分佈6小時循環同化是Hybrid比3DVAR好，並且顯示與實際近似東半部的高累積雨量分佈範圍。

In recent years, the hybrid data assimilation has become a popular topic in the field of weather data assimilation. Nowadays there are four main techniques of as-similation: three-dimensional variational data assimilation (3DVAR), four-dimensional variational data assimilation (4DVAR), Kalman filter and Hybrid. This research implemented 3DVAR and Hybrid on the NEPARTAK typhoon regis-tration. 72h forecast based on the WRF model was conducted for 7 groups of ex-periments.
In the distribution of the flow-dependent, the results show that the incremen-tal distribution of the 3DVAR analysis is centered on the assimilation point, which is not related to the weather but the assimilation of the single point position; The cycle Hybrid shows the analysis of incremental flow-dependent characteristics because of information Background Error structure causing dynamic characteris-tics.
In the simulated path prediction effect, the errors growth of non-cycle Hybrid assimilation is the slowest in 7 experimental, and Its final simulated path is closer to the actual result. In the comparing of simulation and observation, improvements of pressure and maximum wind speed are limited by using 3DVAR and Hybrid as-similation.
In addition, there is no difference in the cumulative rainfall between 3DVAR and non-cycle Hybrid. Three experiments are similar to the actual cumulative rainfall distribution. However, in the 6 hours cumulative rainfall, the hybrid is better than 3DVAR, and shows the similar result as the high cumulative rainfall distribution range of eastern half.

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