雲微物理參數化法及垂直解析度對降水模擬之影響: 以莫拉克(2009)颱風為例

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题名:	雲微物理參數化法及垂直解析度對降水模擬之影響: 以莫拉克(2009)颱風為例
作者:	林宜霖;Lin,Yi-lin
贡献者:	大氣物理研究所
关键词:	雲微物理參數法
日期:	2014-07-25
上传时间:	2014-10-15 14:29:05 (UTC+8)
出版者:	國立中央大學
摘要:	本研究以2009年莫拉克颱風為例，藉由改變模式的雲微物理參數化法及垂直解析度，設計三組實驗以進行討論，分別包含: i) 單矩量(single-moment;WSM6_31)和雙矩量(double-moment;WDM6_31)參數化法比較，ii) 垂直分層 [21層(WDM6_21)、31層(WDM6_31)和45層(WDM6_mix45)]比較，和iii)四種雙矩量微物理參數化法(WDM6_31, Thompson_31, Morrison_31, Milbrandt and Yau_31)比較共七組模擬。研究目的著重於改進過去模式高估莫拉克颱風期間的山區累積降雨量，並且與觀測的雷達回波、颱風路徑與強度比較；另外也進行定量的降水校驗，比較模式24小時、6小時累積降雨的預報能力。在實驗組i)中， WDM6_31實驗對莫拉克颱風的累積降雨極值、強度和登陸時間模擬，較WSM6_31實驗更接近觀測。降水校驗的結果以WSM6_31實驗的24小時累積降雨預報能力較WDM6_31實驗差。在實驗組ii)中，三組模擬陸上的平均降雨、颱風路徑和強度差異不大。降水校驗結果，不論是24小時或6小時累積降雨，WDM6_mix45實驗幾乎有最好的表現[預兆得分(TS)、公平預兆得分(ETS)最高；均方根誤差(RMSE)最小]。WDM6_mix45實驗由於垂直解析度較高，能避免同WDM6_21及WDM6_31實驗在模式頂層產生不合理的垂直速度和雷達回波分佈現象，另外透過垂直剖面分析也能看到，WDM6_mix45實驗的眼牆和眼有明顯崩塌；WDM6_21實驗因為中高層大氣的垂直解析度較差，於地形上容易產生不合理的垂直速度。整體而言，WDM6_mix45使得颱風的強度較為減弱，進而改善累積雨量的高估情形。在實驗組iii)中，後三組模擬能明顯改善WDM6_31實驗回波過強和降雨過高的偏差；降雨極值以Thompson_31和Milbrandt and Yau_31實驗兩組模擬改善最多。降水校驗的結果為，各組模擬的偏離係數(BS)在8月8日由低估轉成高估，並且8月8日以Milbrandt and Yau_31實驗各種得分表現最好。詳細討論各組模擬累積降雨情形，顯示研究使用的各降水校驗參數各有其缺失，甚至有時會誤導使用者對於模式降水預報能力的判斷，必須配合降雨分佈結果才能做較客觀的討論。從水相粒子之垂直分佈可知，WDM6方案產生較多的雨水，Thompson方案傾向產生較多的雪，Milbrandt and Yau方案則產生較多的冰晶，這些現象在台灣中央山脈地形的迎風面上更為顯著，主要是由於不同微物理參數法的原先設計環境條件而產生的系統性偏差。 ;An explicit simulation of Typhoon Morakot(2009) was studied. Three groups of numerical experiments were designed by varying microphysics parameterization schemes and vertical resolution, which were i) single-moment(WSM6_31) and double-moment(WDM6_31) schemes, ii) different vertical resolution(WDM6_21, WDM6_31, WDM6_mix45) and iii)different double-moment schemes(WDM6_31, Thompson_31, Morrison_31, Milbrandt and Yau_31). The study focused on the effects on the accumulated precipitation over mountainous areas, which was usually overestimated by model simulation. In addition to the comparison of radar reflectivity, typhoon tracks and intensity, precipitation maximum and patterns, a series of quantitatively statistics scores were evaluated between the observation data and model output. In experiment i), WDM6_31 revealed a better performance on typhoon intensity and landfall time than WSM6_31. Besides, WDM6_31 also had better TSs and ETSs in 24-hour accumulated precipitation. In experiment ii), varying the vertical resolution had a small effect on typhoon precipitation maximum, intensity and landfall time simulation. No matter for 24-hour or 6-hour accumulated precipitation, WDM6_mix45 had the best performance among three simulations. Because of the finer vertical resolution in mid-to-upper layers, WDM6_mix45 avoided the unreasonable vertical velocity and radar reflectivity in upper layers. At the same time, WDM6_mix45 revealed the break down process of typhoon eye, which reduced typhoon intensity efficiently then lowered the overestimation of precipitation maximum in other simulations. In experiment iii), Thompson_31, Morrison_31, Milbrandt and Yau_31 runs improved the positive bias for WDM6_31 run on radar reflectivity and precipitation maximum. On August 8th, Milbrandt and Yau_31 run had the smallest BIAS-1 value. While applying the statistics scores, the users should pay attention to the deficiencies of the scores, which could mislead the ability of model’s precipitation predicbility. On the vertical crosssection over terrain, WDM6_31 tended to produce more raindrops, Thompson_31 tended to produce more snows, whereas Milbrandt and Yau_31 tended to produce more ice flakes. It was the difference of original designs for every schemes that made the difference of precipitation simulations.
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