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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71147


    Title: 發展地面資料同化方法以改善都卜勒雷達變分分析系統之分析及預報能力;Development of a surface assimilation scheme in a Variational Doppler Radar Analysis System for improving the model analysis and forecast skill
    Authors: 陳依涵;Chen,I-Han
    Contributors: 大氣科學學系
    Keywords: 都卜勒雷達變分分析系統;四維變分資料同化;VDRAS;4DVAR
    Date: 2016-07-26
    Issue Date: 2016-10-13 12:09:05 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 雷達觀測資料具有高時空解析度的特性,對於中小尺度的天氣系統可以有較佳的描述。但由於台灣複雜地形及雷達 PPI 掃描策略影響,雷達觀測範圍受到諸多限制,其中觀測波束受到地形阻擋,且電磁波觀測高度隨離雷達距離增加而上升,使得低層觀測資料不足。前人研究指出,此資料缺乏的區域大多位於邊界層內,但邊界層內有許多物理機制牽動對流結構及發展,底層觀測資料不足亦會降低雷達資料同化的準確度,例如造成近地表冷池反演結果不佳。
    本研究使用工具為美國國家大氣研究中心(NCAR)發展的都卜勒雷達變分分析系統(VDRAS)。相較先前版本僅能同化雷達資料,本研究結合VDRAS與地面資料同化方法,探討使用四維變分方法同化地面觀測資料,是否可以改善雷達底層觀測資料不足的問題,並進一步提升模式分析及預報的能力。
    為驗證方法及模式修改的正確性,首先進行觀測系統模擬實驗(OSSE)。結果顯示,同化底層觀測資料可以有效降低分析場中底層風場及雨水混合比誤差,並藉由價值函數中平滑項及模式動力結構的作用,將底層資訊影響至低、中層。但若只同化底層雨水混合比觀測,對於分析場及預報結果皆有不利的影響。檢視一至三小時定量降水預報校驗結果,顯示同化底層觀測可以提升模式預報能力。
    吾人接續應用此方法分析2014年8月7日台灣南部強降水個案,受到西南氣流帶來水氣量影響及陸風局部輻合作用,當日在台南、高雄、屏東地區產生持續性對流系統消長,最高累積降雨達到130毫米。由中央氣象局地面自動測站觀測顯示,此系統發展過程中西南部沿海有風場輻合現象。研究結果指出,尚未進行地面風場同化的分析場中,底層風場為均勻的西南風及南風,無法反演出風場輻合結構。經由同化地面風場資料,可以有效提升模式解析地面資訊的能力。此外,同化地面溫度亦可得到與地面觀測較相近的溫度結構,而同化地面雨量觀測,直接提升底層回波場的強度。由於本研究使用的模式無地形解析能力,為此地面資料同化方法應用上的限制。
    ;The purpose of this study is to implement a surface assimilation scheme to the Variational Doppler Radar Analysis System (VDRAS) for further improving the model analysis and forecast skill. Due to the lack of low-elevation radar observations caused by standard Plan Position Indicator (PPI) scans and/or beam-blockage, the accuracy of low level analysis after data assimilation process could be significantly reduced.
    An observation system simulation experiments (OSSE) and a real case study are conducted to investigate the feasibility of the new surface data assimilation scheme and how it impacts the convective system. Surface observations including rainwater mixing ratio, liquid water potential temperature and horizontal wind components are selected to be assimilated into VDRAS.
    The results show that assimilation of low level wind information significantly improves the analysis and forecast. Assimilation of only rain water mixing ratio has negative impact on the accuracy of the analysis fields. However, errors can be corrected by assimilating low level wind field. The vertical structure of the analysis field demonstrates that the modification of surface observation can be spread to higher levels through model dynamics and smoothness terms embedded in the cost function.
    Application in a real case indicates that this data assimilation scheme successfully retrieves the low level convergence line with reduction of the wind speed inland, and helps to recover the low level temperature structure.
    Appears in Collections:[Department of Atmospheric Sciences and Graduate Institute of Atmospheric Physics ] Department of Earth Sciences

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