相較於暖雨雲物理過程的研究在過去取得很大成就，冷雨過程在大氣數值模擬仍是很大挑戰。對冷雨過程的無知很有可能會造成雲物理方案在冰相粒子粒徑分布(DSD)上的模擬錯誤。過去許多研究已經證明偏極化雷達變數和雲物理之間緊密的關係。本篇研究使用Morrison這個雙矩量雲物理方案模擬二零零八年六月十四號台灣西南的中尺度對流系統(SoWMEX-IOP8)。模擬的結果會以NCAR S-band雷達的偏極化觀測以及其雪(snow)和雨(rain)的粒徑分布反演來驗證。

Morrison方案模擬的回波(ZHH)被發現高估了觀測。分析發現，這起因於Morrison方案產生了過大粒徑(質量權重粒徑Dm>0.7mm)的雪。因此即使在低估了雪的混和比(q)情況下，模擬仍能產生更強的回波。在接下來的部分，本文討論不同冷雨過程對雪的混和比以及質量權重粒徑增量的貢獻。發現受雲滴霜化的雪(cloud-riming snow)轉換(auto-convert)成冰霰(graupel)這個過程與其他冷雨過程相比在粒徑增量上占很重要的腳色。接著針對雪的數目濃度(number concentration)和雪對雲滴(cloud)的收集效率係數(eci)設計與實行兩組敏感度測試。而結果顯示，這些測試對雪的粒徑分布模擬改善非常輕微，這暗示了微物理過程很有可能不是最主要的過程。

Compared to warm-rain processes which is well understood in decades of advancement, cold-rain microphysics of precipitation is still challenging task in numerical model simulation. The deficient knowledge in cold–rain processes may result in incorrect ice-phased drop size distribution (DSD) of various hydrometers simulated in microphysics scheme. Past studies have proven the inseparable relationship between polarimetric variables and storm microphysics. In the research, Morrison two moment scheme which is a double-moment (DM) scheme is selected to simulate a MCS located at southwest Taiwan on 14 June 2008 (SoWMEX-IOP8). The simulation is validated quantitatively with the NCAR s-band polarimetric measurements and DSD retrievals of raindrops and snow particles. Simulation results from Morrison scheme are found overestimating the reflectivity (ZHH) comparing to observation. The analysis reveals that stronger ZHH is due to the exaggerated mean snow particle sizes (mass-weighted diameter, Dm > 0.7 mm), even though model underestimates the snow mixing ratio (q). The increments of mixing ratio and Dm of snow particle which contributed from different cold-rain microphysical processes are analyzed. The autoconversion of graupel from cloud-riming snow is one of the dominating processes. Two sensitivity experiments including snow concentration and coefficient of collection efficiency of snow for cloud (eci) were performed. The results indicate only slightly improvements of the simulated snow DSD.

盧可昕，2018: 利用雙偏極化雷達及雨滴譜儀觀測資料分析 2008 年西南氣流實驗期間強降雨事件的雲物理過程，國立中央大學大氣物理所碩士論文，1-91 頁。

陳勁宏，2018: 不同微物理方案在雲可解析模式的系集預報分析: SoWMEX-IOP8個案，國立中央大學大氣物理所碩士論文，1-83頁。

游承融，2019: 利用雙偏極化雷達觀測資料進行極短期天氣預報評估: 2008 年西南氣流實驗 IOP8 期間颮線系統個案，國立中央大學大氣物理所碩士論文，1-92頁。

Berry, E., and R. Reinhardt, 1974: An analysis of cloud drop growth by collection: Part II. Single initial distributions. J. Atmos. Sci., 31, 1825–1831

Brandes, E. A., G. Zhang, and J. Vivekanandan, 2002. Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674–68.

Bukovcic, P., A. Ryzhkov, and D. Zrnic, 2020. Polarimetric Relations for Snow Estimation–Radar Verification. J. Appl. Meteor., 59, 991–1009.

Ciesielski, P. E., W. M. Chang, S. C. Huang, R. H. Johnson, B. J. D. Jou, W. C. Lee, P. H. Lin, C. H. Liu, and J. Wang, 2010: Quality-Controlled Upper-Air Sounding Dataset for TiMREX/SoWMEX: Development and Corrections. J. Atmos. Ocean. Technol., 27, 1802-1821

Cohard, J.-M., and J.-P. Pinty, 2000: A comprehensive two-moment warm microphysical bulk scheme. I: Description and tests. Quart. J. Roy. Meteor. Soc., 126, 1815–1842

Davis, C. A., and W.-C. Lee, 2012. Mesoscale Analysis of Heavy Rainfall Episodes from SoWMEX/TiMREX. J. Atmos. Sci., 69, 521-537.

Doviak, R., and Zrnic, D. (2006). Doppler radar and weather observations (2nd ed.). Reprint, Mineola, NY: Dover.

Ikawa, M., H. Mizuno, T. Matsuo, M. Murakami, Y. Yamada, and K. Saito, 1991. Numerical Modeling of the Convective Snow Cloud over the Sea of Japan -Precipitation Mechanism and Sensitivity to Ice Crystal Nucleation Rates. J. Meteorol. Soc., 69, 641-667.

Johnson, M., Y. Jung, D. T. Dawson II, and M. Xue, 2016. Comparison of Simulated Polarimetric Signatures in Idealized Supercell Storms Using Two-Moment Bulk Microphysics Scheme in WRF. Mon. Wea. Rev., 144, 971-996.

Jung, Y., G. Zhang, and M. Xue, 2008. Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables. Mon. Wea. Rev., 136, 2228-2245.

Kajikawa, M., 1974. On the Collection Efficiency of Snow Crystals for Cloud Droplets. J. Meteorol. Soc., 52, 328-336.

Kessler, E., (1969): On the Distribution and Continuity of Water Substance in Atmosphere Circulations. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

Mishchenko, M. I., L. D. Travis, and D. W. Mackowski, 1996. T-matrix computation of light scattering by nonspherical particles: a review. J. Quant. Spectrosc. Radiat. Transfer, 55(5), 535-575.

Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005. A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description. J. Atmos. Sci., 62, 1665-1677.

Morrison, H., and W. W. Grabowski, 2007: Comparison of bulk and bin warm rain microphysics models using a kinematic framework. J. Atmos. Sci., 64, 2839–2861

Morrison, H., and W. W. Grabowski, 2008. A Novel Approach for Representing Ice Microphysics in Models: Description and Tests Using a Kinematic Framework. J. Atmos. Sci., 65, 1528-1548.

Morrison, H., G. Thompson, and V. Tatarskii, 2009. Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes. Mon. Wea. Rev., 137, 991-1007.

Ryzhkov, A. V., and D. S. Zrnic (2019). Radar Polarimetry for Weather Observations. Switzerland: Springer.

Steiner, M., R. A. Houze Jr., and S. E. Yuter, 1995. Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data. J. Appl. Meteor., 34, 1978–2007.

Stensrud, D. J. (2007). Parameterization Schemes–Keys to Understanding Numerical Weather Prediction Models. New York: Cambridge University Press.

Straka, J. (2009). Cloud and Precipitation Microphysics –Principles and Parameterizations. New York: Cambridge University Press.

Xu, W., E. J. Zipser, Y. L. Chen, C. Liu, Y. C. Liou, W. C. Lee, and B. J. D. Jou, 2012: An Orography-Associated Extreme Rainfall Event during TiMREX: Initiation, Storm Evolution, and Maintenance. Mon. Wea. Rev., 140, 2555-2574.

Xu, W., and E. J. Zipser, 2015: Convective intensity, vertical precipitation structures, and microphysics of two contrasting convective regimes during the 2008 TiMREX. J. Geophys. Res. Atmos., 120, 4000–4016.

Ziegler, C. L., 1985: Retrieval of thermal and microphysical variables in observed convective storms. Part I: Model development and preliminary testing. J. Atmos. Sci., 42, 1487–1509