與槽化相關的GNSS-RO折射率資料品質控管策略及其對強降雨預報影響：2020/05/22大雨個案研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：69

、訪客IP：52.15.189.183

姓名

龍孟偉(Long, Meng-Wei) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

與槽化相關的GNSS-RO折射率資料品質控管策略及其對強降雨預報影響：2020/05/22大雨個案研究

相關論文

★ 利用WRF-LETKF同化系統探討掩星折射率觀測對於強降水事件預報之影響	★ 改善區域系集卡爾曼濾波器在颱風同化及預報中的spin-up問題-2008年颱風辛樂克個案研究
★ LETKF加速就位法於颱風同化預報之應用	★ 利用系集重新定位法改善颱風路徑預報-2011年南瑪都颱風個案研究
★ 利用局地系集轉換卡爾曼濾波器雷達資料同化系統改善定量降水即時預報：莫拉克颱風（2009）	★ 利用系集資料同化系統估算區域大氣化學耦合模式中trace物種之排放與吸收：以CO2為例
★ OSSE實驗架構下利用系集預報敏感度工具探討觀測對於颱風路徑預報及結構之影響	★ 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
★ 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	★ LAPS 短時(0-6小時)系集降水機率預報之評估與應用
★ 利用辛樂克颱風(2008)建立的觀測系統模擬實驗評估系集奇異向量在颱風系集預報之應用	★ 雷達資料同化於多重尺度天氣系統(梅雨)的強降雨預報影響：SoWMEX IOP#8 個案研究
★ 基於高解析度系集卡爾曼濾波器之渦旋初始化及其對於颱風強度預報之影響：2010年梅姬颱風個案研究	★ 系集轉換卡爾曼漸進式平滑器在資料同化之應用
★ 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	★ 利用正交向量改善系集卡爾曼濾波器之系集空間及其對同化與預報之影響

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-8-1以後開放)

摘要(中)

本研究旨在探討GNSS-RO折射率觀測中，因槽化所造成的低層資料負偏差。由於無法於觀測資料中直接得知槽化的發生與否，本研究採用全球模式預報資料作為判定的依據。經過比對，當預報中出現槽化時，對應的觀測資料在槽化層以下也出現顯著的負偏差。
檢視模式預報出現槽化的熱力剖面，槽化的發生主因有三：邊界層的混合逆溫、降雨系統冷池造成近地表劇烈降溫以及其他系統造成的沉降逆溫，垂直大幅度的溫、濕度梯度為形成槽化的主要因素。
為了檢視同化這些含負偏差的GNSS-RO資料對於區域模式預報的影響，吾人選取2020/05/22的鋒面大雨個案作為研究標的，進行四組實驗：1. 未同化GNSS-RO資料的CTRL、2. 同化所有GNSS-RO資料的REF_ALL、3. 使用LSW>35%作為資料檢定（QC）策略的REF_LSW、4. 移除模式槽化層以下資料的REF_NODUCT。經過三天半的資料同化循環後，比較各實驗分析場、預報場。並以其探討降雨預報差異的原因。
在分析中，可以歸納出同化GNSS-RO資料後可以顯著調整模式場中南海至中國華南區域的動、熱力環境為適合鋒面強度增加之環境。使用QC策略的兩組實驗結果顯示可進一步增加水氣量，但動力場的表現依QC策略而有所不同：剔除較多資料的REF_LSW低層輻合較CTRL減弱，導致鋒面強度下降；保留較多資料的REF_NODUCT則營造出最強的低層輻合與鋒面強度，顯示移除低層負偏差及盡可能保留低層資料的價值。
預報上，REF_NODUCT出現最強降雨，REF_ALL與REF_LSW接近，而CTRL最弱。但是技術得分則顯示REF_ALL表現最佳、CTRL次之，REF_NODUCT及REF_LSW的表現不彰，主要是由於採用QC策略的兩組實驗整體鋒面的雨帶南偏，這可能是因為移除低層資料所造成中國華南區域西南渦東移，使鋒面移速較原先更快。
增量分析實驗指出同化GNSS-RO低層資料會導致局地的水氣明顯減少，且會因為模式平流而移動，但對於水氣傳輸的影響仍要視其與動力場的交互作用而定。
本研究在槽化判定使用模式，可能因模式誤差而錯誤地刪除資料；且移除資料可能導致西南渦東移而使降雨出現位移，未來結合由GNSS-RO觀測資料直接判定槽化以及重建槽化層以下資料的技術，可望改善上述問題，並達成最大化使用GNSS-RO低層資料的目的。

摘要(英)

This study aims to explore the presence of negative bias in lower-level data in GNSS-RO refractivity data caused by ducting. Since ducting occurrences cannot be directly detected from observational data, this study relies on model data for assessment. Comparisons show that when ducting occur in the model, the significant negative bias exists in corresponding observational data below the ducting layer.
An examination of thermal profiles during ducting occurrences in the model identifies three primary factors contributing to ducting: mixing inversion in boundary layer, cold pools from rainfall systems causing severe near-surface cooling, and subsidence inversions induced by other systems like anticyclone, with sharp vertical temperature and humidity gradients acting as primary drivers.
To evaluate the impact of these negatively biased GNSS-RO data on the model, a heavy rainfall event on May 22, 2020, is selected as the focal case. The study divides experiments into four groups: 1. CTRL, without assimilating GNSS-RO data; 2. REF_ALL, assimilating all GNSS-RO data; 3. REF_LSW, using LSW>35% as a QC strategy; and 4. REF_NODUCT, removing data below the ducting layer. After three and a half days of data assimilation cycles, differences in analysis fields, forecast fields, and forecast rainfall are compared among the experiments.
Analysis reveals that assimilating GNSS-RO data significantly adjusts the dynamical and thermal environments in the region from the South China Sea to South China, making them more conducive to increased frontal intensity. Additionally, the utilization of QC strategies further enhances water vapor content. However, the performance of the dynamic field varies based on the QC strategy employed: removing more data in REF_LSW leads to weakened low-level convergence and decreased frontal strength, while retaining more data in REF_NODUCT yields the strongest low-level convergence and frontal strength, showing the importance of mitigating negative bias and preserving lower-level data.
As for forecast outcomes, REF_NODUCT exhibits the highest rainfall, REF_ALL is comparable to REF_LSW, and CTRL produces the weakest results. However, skill scores suggest that REF_ALL performs the best, followed by CTRL, while the performances of REF_NODUCT and REF_LSW are less satisfactory. This could be attributed to the eastward shift of the southwest vortex in South China caused by the removal of lower-level data, resulting in a faster movement of the front compared to its original speed.
Incremental analysis experiments indicate that assimilating GNSS-RO low-level data leads to a significant reduction in local water vapor and can cause its movement due to the model advection. However, the impact on water vapor transport depends on its interaction with the dynamic field.
This study′s reliance on model data for ducting determination may lead to erroneous data removal due to model errors. Furthermore, removing data could lead to the eastward shift of the southwest vortex, causing rainfall displacement. Future efforts should focus on integrating techniques for directly determining ducting from GNSS-RO observational data and reconstructing data below the ducting layer to address these issues and maximize the utility of GNSS-RO lower-level data.

關鍵字(中)

★ 掩星
★ 折射率
★ 槽化
★ 水氣傳輸

關鍵字(英)

★ GNSS-RO

論文目次

摘要 i
Abstract iii
一、緒論 1
1-1 研究動機及目的 1
1-2 GNSS-RO資料文獻回顧 2
1-3 研究個案簡介 5
二、研究方法與資料 7
2-1 WRF-LETKF資料同化系統 7
2-1-1 WRF模式 7
2-1-2 LETKF資料同化方法 7
2-2 使用資料 9
2-3 局地折射率觀測算符 9
2-4 資料稀化 10
2-5 品質控管 10
2-5-1 LSW QC 10
2-5-2 NODUCT QC 10
2-6 實驗設置 11
三、實驗結果與討論 13
3-1 GNSS-RO資料數量 13
3-2 模式與觀測資料的差異 13
3-2-1 所有atmPrf與echPrf比較 14
3-2-2 LSW QC後的atmPrf與echPrf比較 14
3-2-3 NODUCT QC後的atmPrf與echPrf比較 15
3-3 模式發生槽化時的熱力結構 15
3-4 模式分析場比較 16
3-4-1 同化GNSS-RO資料的影響 16
3-4-2 使用QC策略的影響 17
3-4-3 REF_LSW及REF_NODUCT比較 18
3-5 模式預報場比較 19
3-5-1 同化GNSS-RO的影響 19
3-5-2 使用QC策略的影響 19
3-5-3 REF_LSW及REF_NODUCT比較 20
3-6 預報降雨分布及校驗 21
3-6-1 預報降雨分布 21
3-6-2 預報技術得分 22
3-7 增量分析 23
3-7-1 850hPa重力位高度 23
3-7-2 850hPa可降水量 23
3-7-3 850hPa風場 24
3-7-4 850hPa總體水氣傳輸 24
四、結論與未來展望 25
4-1 結論 25
4-2 討論及未來展望 26
參考文獻 27
附表 32
附圖 33

參考文獻

黃椿喜、葉世瑄、呂國臣、洪景山（2016）。系集定量降水預報方法之探討與分析－系集平均、機率擬合平均與超越機率之定量降水預報。大氣科學，44(2)，173-196。
陳淡容、廖信豪、吳宜昭、于宜強（2020）。2020年5月19-24日梅雨鋒線豪雨事件氣象分析，國家災害防救科技中心災防科技電子報，第 180 期，共16頁。
Chang, CC., Yang, SC., 2022: Impact of assimilating Formosat-7/COSMIC-II GNSS radio occultation data on heavy rainfall prediction in Taiwan. TAO 33, 7, https://doi.org/10.1007/s44195-022-00004-4.
Chen, S.-Y.; Liu, C.-Y.; Huang, C.-Y.; Hsu, S.-C.; Li, H.-W.; Lin, P.-H.; Cheng, J.-P.; Huang, C.-Y, 2021: An Analysis Study of FORMOSAT-7/COSMIC-2 Radio Occultation Data in the Troposphere. Remote Sens. 2021, 13, 717. https://doi.org/10.3390/rs13040717.
Chen, Y.-J., Hong, J.-S., Chen, W.-J, 2022: Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model. Atmosphere 2022, 13, 1879. https://doi.org/10.3390/atmos13111879.
Chien, F.-C., and Y.-C. Chiu, 2021: Assessing the impact of dropsonde data on rain forecasts in Taiwan with observing system simulation experiments. Atmosphere, 12, 1672, https://doi.org/10.3390/atmos12121672.
Chien, F., and Y. Chiu, 2023: Factors Leading to Heavy Rainfall in Southern Taiwan in the Early Mei-Yu Season of 2020. Mon. Wea. Rev., 151, 1885–1908, https://doi.org/10.1175/MWR-D-22-0226.1.
Cucurull, L., 2015: Implementation of a quality control for radio occultation observations in the presence of large gradients of atmospheric refractivity, Atmos. Meas. Tech., 8, 1275–1285, https://doi.org/10.5194/amt-8-1275-2015.
Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.
E. K. Smith and S. Weintraub, 1953: The Constants in the Equation for Atmospheric Refractive Index at Radio Frequencies, Proceedings of the IRE, vol. 41, no. 8, pp. 1035-1037, https://doi.org/10.1109/JRPROC.1953.274297.
Hersbach, H., 2016: The ERA5 atmospheric reanalysis. 2016 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract NG33D–01.
Kuo, Y., L. Cheng, and J. Bao, 1988: Numerical Simulation of the 1981 Sichuan Flood. Part I: Evolution of a Mesoscale Southwest Vortex. Mon. Wea. Rev., 116, 2481–2504, https://doi.org/10.1175/1520-0493(1988)116<2481:NSOTSF>2.0.CO;2.
Guo, Y., R. Zhang, Z. Wen, J. Li, C. Zhang, and Z. Zhou, 2021: Understanding the role of SST anomaly in extreme rainfall of 2020 Meiyu season from an interdecadal perspective. Sci. China Earth Sci., 64, 1619–1632,
https://doi.org/10.1007/s11430-020-9762-0.
Korn, G. A., and T. M. Korn, 1961: Mathematical Handbook for Scientists and Engineers, McGraw-Hill, 943 pp.
Lien, G., and Coauthors, 2021: Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System. Mon. Wea. Rev., 149, 2171–2191, https://doi.org/10.1175/MWR-D-20-0267.1.
Liu, H., Y. Kuo, S. Sokolovskiy, X. Zou, Z. Zeng, L. Hsiao, and B. C. Ruston, 2018: A Quality Control Procedure Based on Bending Angle Measurement Uncertainty for Radio Occultation Data Assimilation in the Tropical Lower Troposphere. J. Atmos. Oceanic Technol., 35, 2117–2131, https://doi.org/10.1175/JTECH-D-17-0224.1.
Ralph, F. M., J. J. Rutz, J. M. Cordeira, M. Dettinger, M. Anderson, D. Reynolds, L. J. Schick, and C. Smallcomb, 2019: A Scale to Characterize the Strength and Impacts of Atmospheric Rivers. Bull. Amer. Meteor. Soc., 100, 269–289, https://doi.org/10.1175/BAMS-D-18-0023.1.
C. Rocken, R. Anthes, M. Exner, D. Hunt, S. Sokolovskiy, R. Ware, M. Gorbunov, W. Schreiner, D. Feng, B. Herman, Y.-H. Kuo, X. Zou, 1997: Analysis and validation of GPS/MET data in the neutral atmosphere, J. Geophys. Res., 102(D25), 29849–29866, https://doi.org/10.1029/97JD02400.
Pham, G. H., Yang, S.-C., Chang, C.-C., Chen, S.-Y., and Huang, C. Y. , 2024: Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere, Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024.
Schreiner, W. S., Weiss, J. P., Anthes, R. A., Braun, J., Chu, V., Fong, J., et al., 2020: COSMIC-2 radio occultation constellation: First results. Geophysical Research Letters, 47. https://doi.org/10.1029/2019GL086841.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., … Huang, X. -yu., 2021: A Description of the Advanced Research WRF Model Version 4.3 (No. NCAR/TN-556+STR). http://dx.doi.org/10.5065/1dfh-6p97.
Sokolovskiy, S., 2003: Effect of superrefraction on inversions of radio occultation signals in the lower troposphere, Radio Sci., 38, 1058, https://doi.org/10.1029/2002RS002728.
Sokolovskiy, S., and Coauthors, 2024: Detection of Superrefraction at the Top of the Atmospheric Boundary Layer from COSMIC-2 Radio Occultations. J. Atmos. Oceanic Technol., 41, 65–78, https://doi.org/10.1175/JTECH-D-22-0100.1.
Takaya, Y., I. Ishikawa, C. Kobayashi, H. Endo, and T. Ose, 2020: Enhanced Meiyu‐Baiu rainfall in early summer 2020: Aftermath of the 2019 super IOD event. Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL090671.
Turton, J.D., Bennetts, D.A., & Farmer, S.F., 1988: An introduction to radio ducting. Meteorological Magazine, 117, 245-254.
Wang, K.-N., Ao, C.O., de la Torre Juárez, M, 2020: GNSS-RO Refractivity Bias Correction Under Ducting Layer Using Surface-Reflection Signal. Remote Sens. 2020, 12, 359. https://doi.org/10.3390/rs12030359.
Wang, K.-N., de la Torre Juárez, M., Ao, C. O., and Xie, F, 2017: Correcting negatively biased refractivity below ducts in GNSS radio occultation: an optimal estimation approach towards improving planetary boundary layer (PBL) characterization, Atmos. Meas. Tech., 10, 4761–4776, https://doi.org/10.5194/amt-10-4761-2017.
Ware, R., and Coauthors, 1996: GPS Sounding of the Atmosphere from Low Earth Orbit: Preliminary Results. Bull. Amer. Meteor. Soc., 77, 19–40, https://doi.org/10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2.
William Emery, Adriano Camps, 2017: Chapter 6 - Remote Sensing Using Global Navigation Satellite System Signals of Opportunity, Introduction to Satellite Remote Sensing, Elsevier, Pages 455-564, ISBN 9780128092545, https://doi.org/10.1016/B978-0-12-809254-5.00006-3.
Xie, F., S. Syndergaard, E. R. Kursinski, and B. M. Herman, 2006: An Approach for Retrieving Marine Boundary Layer Refractivity from GPS Occultation Data in the Presence of Superrefraction. J. Atmos. Oceanic Technol., 23, 1629–1644, https://doi.org/10.1175/JTECH1996.1.
Xie, F., Wu, D. L., Ao, C. O., Kursinski, E. R., Mannucci, A. J., and Syndergaard, S, 2010: Super-refraction effects on GPS radio occultation refractivity in marine boundary layers, Geophys. Res. Lett., 37, L11805, https://doi.org/10.1029/2010GL043299.
Yang, S., S. Chen, S. Chen, C. Huang, and C. 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. Mon. Wea. Rev., 142, 4139–4163, https://doi.org/10.1175/MWR-D-13-00275.1.
Zeng, W., G. Chen, L. Bai, Q. Liu, and Z. Wen, 2022: Multiscale processes of heavy rainfall over East Asia in summer 2020: Diurnal cycle in response to synoptic disturbances. Mon. Wea. Rev., 150, 1355–1376, https://doi.org/10.1175/MWR-D-21-0308.1.

指導教授

楊舒芝(Yang, Shu-Chih)

審核日期

2024-8-12

推文