研究中共使用四種微物理參數化方案，兩種單矩量參數化方案:Goddard(GCE)、WRF SM 6-category(WSM6)及兩種雙矩量方案:WRF DM 6-category(WDM6)、Morrison(MOR)，四種方案分別進行系集預報，藉此了解不同微物理參數化方案搭配系集預報的特性。研究中利用2008年6月16日台灣北部熱對流個案，探討對流成熟期時，強對流區的模式離散程度和背景誤差協方差，了解透過系集資料同化系統，觀測如何影響相關模式變數。
結果顯示，GCE有最強的冰相混合比，因此回波發展最高；在低層暖雨過程，雖然WDM6有最大雨混合比但回波卻是最弱的，而MOR混合比雖沒有特別大，然而回波卻是最強的，原因為WDM6預報出現大量的粒子數量，而MOR的粒子數量則是最少的，因此導致上述的結果。此結果顯示使用雙矩量微物理方案時，不可忽視粒子數量所帶來的影響，而雨滴粒子數量不只影響回波，降雨、蒸發效率、溫度甚至冷池都可能因為不同雨滴大小、數量而有不同表現。
根據不同微物理參數化設定，在方差分布也有不一樣的特徵，GCE在高層有較多的不確定性，WDM6在低層有最大的離散程度，而MOR在融化層附近有最大的方差。研究中發現GCE在冰相有較多不確定性，WDM6則在暖雨過程離散程度較大，因此在進行系集資料同化時，使用這兩種微物理參數化法，可在有限的系集個數下，有效地增加系集間的離散度。
研究中亦討論不同變數間的誤差相關性，在對流區，垂直風與潛熱釋放有高度相關性，可能原因為強風速提高粒子間相態轉換，隨之釋放的潛熱又提高風速；此外在MOR的回波自相關中也可以看到粒子數量所帶來的影響。

To understand the characteristics of different microphysics schemes and investigate the forecast error structure in very short-term forecast, four microphysics schemes are used in the study. They include two single-moment schemes: Goddard(GCE)、WRF SM 6-category(WSM6), and two double-moment schemes of WRF DM 6-category(WDM6) and Morrison(MOR). A thunderstorm case in northern Taiwan on June 16, 2008 is selected.
The results show that GCE has the most ice-related mixing ratio, so the reflectivity development is the highest. In the low-level warm rain process, WDM6(MOR) has the most(fewest) rain mixing ratio and the weakest(strongest) reflectivity due to large(small) number of rain total number concentration. It is found that when using the double-moment microphysics scheme, the influence of the total number concentration cannot be ignored.
According to different microphysics scheme settings, the variance also has different characteristics. With the same ensemble members (36), it is found that GCE(WDM6) has more uncertainty in ice-related processes (warm rain processes). Therefore, using combination of these two schemes can effectively increase ensemble spread and improve the benefits of data assimilation.
The error correlation between different variables is also discussed in the study. In the convective zone, the vertical wind and the latent heat release are highly correlated. The possible reason is that the strong vertical wind increases the phase transition between the particles, and the latent heat released enhances the vertical wind again. In addition, the reflectivity auto-correlation in MOR is greatly affected by the number of particles around melting layer.

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