評估TAHOPE觀測實驗同化S-PolKa徑向風、回波與折射指數對短期降雨預報的影響：觀測系統模擬實驗(OSSE)之測試

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蔡博安(Bo-An Tsai) 查詢紙本館藏

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大氣科學學系

論文名稱

評估TAHOPE觀測實驗同化S-PolKa徑向風、回波與折射指數對短期降雨預報的影響：觀測系統模擬實驗(OSSE)之測試

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摘要(中)

過往研究指出低層水氣資訊與對流系統的發生與否有很高的相關性，而雷達折射率觀測資料對近地表的水氣變化非常敏感，因此可使用雷達低仰角掃描所觀測之折射指數作為低層水氣資訊的依據。本研究中使用系集資料同化系統探討同化雷達回波、徑向風和折射率資料的效果，並選擇於2012年6月11日為北臺灣帶來豐沛的降水，導致各地出現嚴重的淹水災情的梅雨鋒面系統作為研究個案。利用觀測系統模擬實驗進行一系列的同化測試，測試的項目包括：(1)將近地表(~50m)的折射率資訊錯位至0.5度仰角上進行同化，比較其和同化在正確位置上(近地表)的差異；(2)增加水氣局地化半徑的影響；(3)新增溫度場資料同化的效益；(4)檢視新增新竹S-PolKa雷達所觀測之回波、徑向風、折射率資料所帶來的效益；(5)比較在不同模式解析度(3公里與1公里)下同化雷達折射率的差異。
結果顯示同化雷達回波與徑向風資料能有效修正鋒面系統的位置，進一步同化雷達折射率資料能改善近地表濕度場，並提升局部地區的短期預報能力；若將近地表的折射率資料置於PPI上進行同化會產生錯誤的修正；增加水氣的局地化半徑可以修正更大範圍的水氣；同化溫度資料可以藉由溫度與濕度的交相關性修正濕度場，改善降雨預報；位於新竹的S-PolKa雷達海拔高度較低、位置較靠近鋒面帶，能提供更多鋒面底層以及上游的資料，對於重建以及維持鋒面結構有很大的幫助；提升模式解析度能模擬更小尺度的渦流系統，有助於增強折射指數的同化效益。

摘要(英)

Past studies show a strong link between low-level moisture information and convective initiation(CI). Radar refractivity data is sensitive to the change of near-surface moisture, so refractivity observed by radar low-elevation angle scan can be used as the information about low-level water vapor. In this study, ensemble data assimilation system was used to discuss the effect of assimilating radar reflectivity, radio wind and refractivity. 11 June 2012 Mei-Yu front system case was chosen for research. It brought heavy precipitation over the north western Taiwan, and cause flooding. OSSEs is utilized to conduct a series of sensitivity test, including: (1) Assimilating refractivity data from near surface at 0.5 elevation angle, and compare it with assimilating at right position(near-surface). (2) Impact of increasing localization distance. (3) Improvement of assimilating temperature data. (4) Benefit of additional radar data (reflectivity, radio wind and refractivity) observed by S-PolKa radar. (5) Compare the result between assimilating refractivity data at difference model resolution (3km and 1km).
Result shows that assimilating reflectivity and radial wind can correct the position of frontal system. Assimilating radar refractivity can modify near-surface moisture field. Assimilating refractivity at PPI plane will cause wrong adjustment. Increasing localization distance of water vapor field can modify larger area of water vapor field. Assimilating temperature data can modify moisture field by the cross-correlation between temperature and water vapor, and improve precipitation forecast. Since the altitude of S-PolKa radar is low, it can provide more information about the structure of frontal system at lower level. Increasing the resolution of model can simulate smaller scale turbulent convective features, and improve the benefit of assimilating refractivity.

關鍵字(中)

★ 折射率
★ 資料同化
★ 極短期天氣預報

關鍵字(英)

★ refractivity
★ data assimilation
★ very short-term forecast

論文目次

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 1
一、緒論 2
1-1 前言與文獻回顧 2
1-2 研究目的 3
二、個案介紹 5
三、研究方法 6
3-1 WRF模式設定 6
3-2 WLRAS系統介紹 6
3-3 觀測算符 8
3-4 模擬雷達觀測 9
3-5 校驗方式 10
四、 OSSE實驗結果與結果分析 12
4-1 實驗設計 12
4-2 真實場與背景場 13
4-3 同化徑向風觀測的影響 15
4-4 折射率同化位置差異比較 16
4-5 增加水氣局地化半徑 18
4-6 同化溫度場資訊 20
4-7 新竹S-POLKA雷達資料的重要性 21
4-7-1 加入S-PolKa雷達回波、徑向風資料 22
4-7-2 加入S-PolKa雷達折射率資料 23
4-7-3 累積降雨表現 23
4-8 小結 23
五、增加解析度之敏感度測試 26
5-1 實驗設計 26
5-2 實驗結果 27
5-3 修改同化流程 28
六、結論與未來展望 29
6-1 結論 29
6-2 未來展望 30
參考文獻 32
附圖 36
附表 94

參考文獻

吳品穎，2015：利用系集重新定位法改善對流尺度定量降水即時預報：2009 年莫拉克颱風個案研究，碩士論文，國立中央大學。
邵彥銘，2015：利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善短期定量降雨預報:SoWMEX IOP8個案分析，碩士論文，國立中央大學。
許修維，2019：2008年SoWMEX期間雷達折射率之特徵及應用，碩士論文，國立中央大學。
Bodine, D., and Coauthors, 2011: Understanding Radar Refractivity: Sources of Uncertainty. Journal of Applied Meteorology and Climatology, 50, 2543-2560.
Chang, W., K.-S. Chung, L. Fillion, and S.-J. Baek, 2014: Radar Data Assimilation in the Canadian High-Resolution Ensemble Kalman Filter System: Performance and Verification with Real Summer Cases. Monthly Weather Review, 142, 2118-2138.
Chung, K.-S., I. Zawadzki, M. K. Yau, and L. Fillion, 2009: Short-Term Forecasting of a Midlatitude Convective Storm by the Assimilation of Single–Doppler Radar Observations. Monthly Weather Review, 137, 4115-4135.
Crook, N. A., 1996: Sensitivity of Moist Convection Forced by Boundary Layer Processes to Low-Level Thermodynamic Fields. Monthly Weather Review, 124, 1767-1785.
Dabberdt, W. F., T. W. Schlatter, and w. c. f. t. r. o. t. PDT-2, 1996: Research Opportunities from Emerging Atmospheric Observing and Modeling Capabilities*. Bulletin of the American Meteorological Society, 77, 305-324.
Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses. Monthly Weather Review, 139, 272-294.
Emanuel, K., and Coauthors, 1995: Report of the first prospectus development team of the US Weather Research Program to NOAA and the NSF, 1194-1208.
Fabry, F., 2004: Meteorological Value of Ground Target Measurements by Radar. Journal of Atmospheric and Oceanic Technology, 21, 560-573.
Fabry, F., C. Frush, I. Zawadzki, and A. Kilambi, 1997: On the Extraction of Near-Surface Index of Refraction Using Radar Phase Measurements from Ground Targets. Journal of Atmospheric and Oceanic Technology, 14, 978-987.
Feng, Y.-C., and F. Fabry, 2018: Quantifying the Error of Radar-Estimated Refractivity by Multiple Elevation and Dual-Polarimetric Data. Journal of Atmospheric and Oceanic Technology, 35, 1897-1911.
Feng, Y.-C., F. Fabry, and T. M. Weckwerth, 2016: Improving Radar Refractivity Retrieval by Considering the Change in the Refractivity Profile and the Varying Altitudes of Ground Targets. Journal of Atmospheric and Oceanic Technology, 33, 989-1004.
Gasperoni, N. A., M. Xue, R. D. Palmer, and J. Gao, 2013: Sensitivity of Convective Initiation Prediction to Near-Surface Moisture When Assimilating Radar Refractivity: Impact Tests Using OSSEs. Journal of Atmospheric and Oceanic Technology, 30, 2281-2302.
Ge, G., J. Gao, and M. Xue, 2013: Impacts of Assimilating Measurements of Different State Variables with a Simulated Supercell Storm and Three-Dimensional Variational Method. Monthly Weather Review, 141, 2759-2777.
Huang, C.-Y., S.-Y. Chen, S. K. A. V. P. Rao Anisetty, S.-C. Yang, and L.-F. Hsiao, 2016: An Impact Study of GPS Radio Occultation Observations on Frontal Rainfall Prediction with a Local Bending Angle Operator. Weather and Forecasting, 31, 129-150.
Hunt, B. R., E. J. Kostelich, and I. J. P. D. N. P. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, 230, 112-126.
Koch, S. E., A. Aksakal, and J. T. McQueen, 1997: The Influence of Mesoscale Humidity and Evapotranspiration Fields on a Model Forecast of a Cold-Frontal Squall Line. Monthly Weather Review, 125, 384-409.
Liou, Y.-C., and Y.-J. Chang, 2009: A Variational Multiple–Doppler Radar Three-Dimensional Wind Synthesis Method and Its Impacts on Thermodynamic Retrieval. Monthly Weather Review, 137, 3992-4010.
Montmerle, T., A. Caya, and I. Zawadzki, 2002: Short-Term Numerical Forecasting of a Shallow Storms Complex Using Bistatic and Single-Doppler Radar Data. Weather and Forecasting, 17, 1211-1225.
Nicol, J. C., A. J. Illingworth, and K. Bartholomew, 2014: The potential of 1 h refractivity changes from an operational C-band magnetron-based radar for numerical weather prediction validation and data assimilation†, 140, 1209-1218.
Park, S., and F. Fabry, 2010: Simulation and Interpretation of the Phase Data Used by the Radar Refractivity Retrieval Algorithm. Journal of Atmospheric and Oceanic Technology, 27, 1286-1301.
Seko, H., E.-i. Sato, H. Yamauchi, and T. Tsuda, 2017: Data assimilation experiments of refractivity observed by JMA operational radar. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), Springer, 327-336.
Smith, E. K., and S. J. P. o. t. I. Weintraub, 1953: The constants in the equation for atmospheric refractive index at radio frequencies, 41, 1035-1037.
Sun, J., 2005: Convective-scale assimilation of radar data: progress and challenges, 131, 3439-3463.
Sun, J., and N. A. Crook, 1997: Dynamical and Microphysical Retrieval from Doppler Radar Observations Using a Cloud Model and Its Adjoint. Part I: Model Development and Simulated Data Experiments. Journal of the Atmospheric Sciences, 54, 1642-1661.
Tsai, C., S. Yang, and Y. J. T. Liou, Ser. A, 2014: Improving short-term QPFs with a WRF-LETKF radar data assimilation system: OSSEs on Typhoon Morakot (2009), 66, 21804.
Weckwerth, T. M., 2000: The Effect of Small-Scale Moisture Variability on Thunderstorm Initiation. Monthly Weather Review, 128, 4017-4030.
Weckwerth, T. M., and D. B. Parsons, 2006: A Review of Convection Initiation and Motivation for IHOP_2002. Monthly Weather Review, 134, 5-22.
Weckwerth, T. M., C. R. Pettet, F. Fabry, S. J. Park, M. A. LeMone, and J. W. J. J. o. A. M. Wilson, 2005: Radar refractivity retrieval: Validation and application to short-term forecasting, 44, 285-300.
Wu, P.-Y., S.-C. Yang, C.-C. Tsai, and H.-W. Cheng, 2020: Convective-scale sampling error and its impact on the ensemble radar data assimilation system: A case study of heavy rainfall event on 16th June 2008 in Taiwan. Monthly Weather Review.
Xiao, Q., and J. Sun, 2007: Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002. Monthly Weather Review, 135, 3381-3404.
Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2007: An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall. Journal of Applied Meteorology and Climatology, 46, 14-22.
Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, E. Lim, Y.-R. Guo, and D. M. Barker, 2005: Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case. Journal of Applied Meteorology, 44, 768-788.
Yang, S.-C., S.-H. Chen, S.-Y. Chen, C.-Y. Huang, and C.-S. Chen, 2014: Evaluating the Impact of the COSMIC RO Bending Angle Data on Predicting the Heavy Precipitation Episode on 16 June 2008 during SoWMEX-IOP8. Monthly Weather Review, 142, 4139-4163.
Yang, S.-C., Z.-M. Huang, C.-Y. Huang, C.-C. Tsai, and T.-K. Yeh, 2020: A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction. Monthly Weather Review, 148, 1075-1098.
Yang, S.-C., S.-H. Chen, K. Kondo, T. Miyoshi, Y.-C. Liou, Y.-L. Teng, and H.-L. Chang, 2017: Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system, 9, 1684-1702.

指導教授

鍾高陞(Kao-Shen Chung)

審核日期

2020-8-20

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