本研究使用WRF-LETKF雷達同化系統(WRF-LETKF Radar Assimilation System, WLRAS)進行資料同化實驗，除了同化已被廣泛使用在雷達資料同化的徑向風(Vr)與回波(ZH)，進一步同化雙偏極化參數，如差異反射率(ZDR)與比差異相位差(KDP)。另外，本研究設計了一種新的同化方法，利用平均粒徑與ZDR的高相關性從ZDR的觀測增量取得更多微物理變數的修正，當觀測資料被同化時更新由混合比以及總數量濃度診斷得到的標準化截距參數(Nw)以及質量權重平均粒徑(Dm)。同化實驗選用了兩種不同的中尺度系統以及四種不同的雲微物理參數化方案。其中一個系統是由西南風驅使的颮線系統，另外一個系統是局地產生的午後對流。四種不同的雲微物理參數化方案分別為GCE、WSM6、WDM6、MOR。本研究執行了一系列的實驗以評估同化雙偏極化參數對於分析場以及定量降水預報(Quantitative Precipitation Forecast, QPF)的影響。實驗結果顯示利用單矩量的雲微物理參數化方案同化額外的ZDR後，ZH與KDP的分析場反而會變得比較差。當使用雙矩量的雲微物理參數化方案同化ZDR與KDP時，兩者的誤差都能夠下降。此外，使用新方法同化雙偏極化參數可以明顯的改善ZDR的分析場，使ZDR的誤差下降更多。除了對雲微物理變數的修正之外，同化額外的雙偏極化參數亦能夠調整水氣分布以及加強對流區的垂直運動。在同化了雙偏極化參數之後，強降雨的表現有得到改善，即使在利用單矩量雲微物理參數化方案同化ZDR的實驗中也可以發現強降雨的機率變高。總結來說，利用單矩量雲微物理參數化方案同化額外雙偏極化參數存在著限制，而雙矩量雲微物理參數化方案有更多的彈性來調適雙偏極化參數對雲微物理變數造成的修正。此研究中證實新的方法能夠更有效的利用ZDR的觀測增量來降低ZDR的誤差。雲微物理變數的彈性調整以及同化雙偏極化參數對於動力與熱力場的調整有助於改善短時定量降水預報的表現。;This study applied WRF-LETKF Radar Assimilation System (WLRAS) to assimilate polarimetric parameters, i.e. differential reflectivity (ZDR) and specific differential phase (KDP), in addition to radial wind (Vr) and reflectivity (ZH) which is commonly used in radar data assimilation. Besides, a new approach is developed to make use of the high correlation between mean diameter and ZDR to extract more correction from ZDR innovation. It updates normalized intercept parameter (Nw) and mass-weighted mean diameter (Dm) diagnosed from original model variables, mixing ratio and total number concentration. Two real cases, including squall lines forced by synoptic southwestern wind and a local afternoon thunderstorm, are selected to conduct the assimilation experiments with four different microphysics parameterization (MP) schemes, GCE, WSM6, WDM6 and MOR. A series of experiments are conducted to evaluate the performance of the analysis and the quantitative precipitation forecast (QPF). The results show that assimilating additional ZDR with single moment schemes deteriorates the analysis field of ZH and KDP. Errors of ZDR and KDP can decrease simultaneously when all the polarimetric parameters are assimilated with double moment schemes. The new approach reduces more ZDR errors through the high correlation between Dm and ZDR. In addition to the correction in microphysical states, assimilating additional polarimetric parameters can adjust water vapor and enhance vertical velocity in the strong convective region. Heavy rainfall forecast performs better even in the experiments assimilating ZDR with single moment schemes. In conclusion, there is limitation in assimilating additional polarimetric parameters with single moment schemes, and double moment schemes have more flexibility to adapt the adjustment in hydrometeor variables from assimilating additional polarimetric parameters. It is confirmed that the new approach can extract more correction from ZDR innovation. The flexible correction in microphysical states and the adjustment in dynamical and thermodynamical fields help to improve the performance of short-term QPF.
