本研究使用雙偏極化雷達資料模擬器(polarimetric radar data simulator, PRDS)將模式輸出轉換成雙偏極化雷達參數後，與NCAR S波段雙偏極化雷達之雙偏極化雷達參數進行分析場與極短期數值天氣預報的比較。此外，使用Goddard、WSM6、WDM6和Morrison四種雲微物理參數化方案進行模擬。進行短預報前，使用WRF-LETKF雷達資料同化系統同化雷達回波與徑向風，以獲取最佳分析場。目的在於掌握較佳雲動力結構後，利用雙偏極化雷達參數驗證數值模式在雲微物理方面的表現，並了解不同雲微物理參數化方案對於雙偏極化雷達參數之模擬狀況。
　　經資料同化後，系集平均分析場雨帶的強度與分佈和雷達觀測結果相近。後續則利用單一決定性預報與系集平均場短期預報進行同化效益的討論，以傳統列聯表分數之累積雨量校驗結果顯現同化後結果較佳。此外，利用contoured frequency by altitude diagrams (CFADs)進行系集平均分析場與每小時之累積預報結果，回波在四個方案中的CFAD分布和觀測相似，其中單矩量的Goddard和WSM6更接近觀測。而差異反射率在單矩量模擬中，表現較佳，但四種方案皆有高估的狀況。比差異相位差則是在對流區的掌握度較好，層狀區的改進差異不大。模式預報後第一小時結果與觀測相比後，顯示仍能維持雷達參數良好的垂直分布狀況，但隨著預報時間拉長至第三小時，垂直分布結果已和觀測大相逕庭，與未同化之單一決定性預報之分佈無異。研究結果顯示，利用雙偏極化雷達參數驗證模式表現，可以了解模式在雲微物理過程的掌握能力外，亦可了解到雷達資料同化的效益與同化傳統雷達資料對於雙偏極化雷達參數的影響。此外，也顯示同化雙偏極化雷達參數之必要，並可提供模式更佳的初始場。

In this research, a polarimetric radar data simulator (PRDS) is used to validate analysis results and short-term forecast outputs that have been converted to polarimetric radar data by comparing with the NCAR S-Pol dual-pol parameters. The WRF-LETKF system is utilized to assimilate radar reflectivity and Doppler wind to obtain the optimal analysis. And then the analysis is used as the initial condition of short-term forecasts. In addition, four different microphysics parameterization schemes are used in the study. After making sure the analysis resembles to true atmospheric state, we try to use polarimetric radar data to validate model performance, especially focusing on microphysics processes. Additionally, we also can understand the difference among microphysics schemes when it comes to simulating polarimetric radar data.
The analysis fields quite resemble to the observation. The traditional forecast skill scores prove that the short-term forecasts are much better after radar data assimilation. By examining contour frequency by altitude diagrams (CFADs), results of reflectivity (Z_H) are improved in four schemes. The single moment schemes such as Goddard and WSM6 perform better than the other two. The improvements of differential reflectivity (Z_DR) are obvious in single moment simulations, but all four schemes are overestimate the value. And specific differential phase (K_DP) distributions are better in convective areas. The first-hour forecast agrees with the observation, and the general vertical structure can be held well. But after a three-hour forecast, there is no resemblance between observation and model. To sum up, we can know that polarimetric radar data can help us validate the model and impact on polarimetric data when assimilating transitional radar data. Last, It is shown that polarimetric radar data are necessary to be assimilated for providing better analysis fields.

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