利用三維回波移動場改善即時降雨預報並建構系集即時預報系統：臺灣梅雨鋒面及秋季降水個案分析

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：54

、訪客IP：18.191.195.110

姓名

許育蕎(Yu-Chiao Hsu) 查詢紙本館藏

畢業系所

大氣科學學系

論文名稱

利用三維回波移動場改善即時降雨預報並建構系集即時預報系統：臺灣梅雨鋒面及秋季降水個案分析

相關論文

★ McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation(MAPLE)即時預報系統在臺灣複雜地形之可行性評估：颱風與梅雨鋒面個案分析	★ 利用系集法估計與檢驗對流尺度之預報誤差：SoWMEX IOP8 個案分析
★ 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	★ 藉由數值模式水平風場改善雷達回波外延即時預報系統:16個颱風個案統計分析
★ 分析不同微物理參數化之系集預報誤差: SoWMEX-IOP8 對流個案	★ 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
★ 台灣地區對流胞特性統計分析與即時路徑預報之改善	★ 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
★ 利用多頻道衛星觀測評估WRF數值模式於不同微物理方案之雲特性:以梅雨鋒面降水系統個案為例	★ 使用局地系集轉換卡爾曼濾波器同化雙偏極化參數的全新方法：夏季真實個案中的分析場與預報場
★ 台灣地區強對流胞即時預報與冰雹預警能力之分析與改善	★ Extreme Heavy Rainfall Event on 01-02 June 2017 over Northern Taiwan Area: Analysis of Radar Observation and Ensemble Simulations
★ WRF-LETKF系統同化反演熱動力場與雷達資料：鋒面雨帶個案之分析探討	★ Investigating hygroscopic cloud-seeding effects in liquid-water clouds in northern Taiwan: in-situ measurements and model simulation
★ 同化雙偏極化雷達差異反射率之方法與影響評估：2021 年宜蘭降雨觀測實驗 IOP2 個案分析	★ 從衛星觀測看西北太平洋熱帶氣旋快速增強的前兆

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

雷達回波外延法使用過去時間的觀測回波資料，計算出可提供天氣系統移動及旋轉特性資訊的回波移動場，並根據此移動場結果來進行外延預報。根據前人研究，移動場計算上的不確定性是外延預報當中最主要的誤差來源之一，且天氣系統在不同高度上亦有不同的移動方向，這些因素都將影響外延預報的能力。
本研究針對此一不確定性來源，將三維的回波移動場應用至MAPLE (McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation) 即時預報系統中，並挑選臺灣秋季及梅雨鋒面降水事件來進行分析討論。根據三維移動場的時間與空間分析結果，在秋季降水事件中，東西方向(u)分量和南北方向(v)分量移速大小相近；在梅雨鋒面事件中，東西方向之移速則大於南北方向之移速，這樣的結果顯示出不同季節的降水系統移動特性。三維回波移動場資訊也被應用至即時預報方法中，將其預報能力以連續校驗法以及絕對校驗法進行分析，結果顯示，三維雷達外延方法之預報能力有所提升，可改善至三小時的預報結果。
此外，本研究亦根據三維移動場的時空分析結果，建立一系集即時預報系統，考慮在移動場計算過程所包含之不確定性。校驗結果顯示，應用此系集即時預報系統至秋季降水及梅雨鋒面個案時，皆具有正確判斷降水事件發生之區辨能力，且可以維持約三小時的預報可信度表現。並且於累積雨量預報上也能正確的掌握和觀測結果相似的降雨分布。整體而言，三維雷達外延即時預報方法以及系集即時預報方法皆可以針對移動場上的不確定性進行改善，也較原先的外延方法有更好的預報能力。

摘要(英)

Radar echo extrapolation utilizes the observed composite reflectivity to estimate the motion fields of radar echoes and provide advection and rotation information for extrapolation. The uncertainty of motion fields is one of the major error in radar extrapolation. In addition, the weather system may have different moving directions at different heights.
In this study, 3-Dimension motion fields are estimated by the entire volume scanned data and applied to MAPLE (McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation) nowcasting system. With autumn precipitation events in the Yilan area and the Meiyu front events in Taiwan, the characteristic of 3D motion fields in space and time are analyzed. The results show that u-component varies more than v-component both in time and space for the Mei-yu front event. As for autumn precipitation, the diversity of u- and v- components are similar. Then, the added value of 3D motion fields for the nowcasting is evaluated by continuous and categorical verification. It is found that the improvement of the nowcasting with 3D motion fields can be up to 3-h.
Furthermore, based on the analysis of 3D motion field, an ensemble nowcasting scheme was developed by considering the uncertainty of motion field. The verification of ensemble nowcasting shows a good ability to correctly predict the occurrence of the precipitation and the reliability can up to nearly 3-h in autumn precipitation and Meiyu front events. The forecast of accumulated rainfall also accurately captures rainfall distribution similar to the observation. Overall, both the utilization of 3D motion fields in nowcasting and the ensemble nowcasting scheme can improve the uncertainty caused by motion field estimation and demonstrate better forecasting ability compared to the original extrapolation method.

關鍵字(中)

★ 雷達回波外延法
★ 即時預報
★ 系集即時預報

關鍵字(英)

★ radar echo extrapolation
★ nowcasting
★ ensemble nowcasting

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1.1 前言 1
1.2 文獻回顧 1
1.3 研究動機 4
第二章研究方法 6
2.1 三維雷達回波外延預報系統(3D MAPLE) 6
2.2 系集即時預報系統 7
2.3 校驗方法 8
2.3.1 連續校驗法 9
2.3.2 絕對校驗法 9
2.3.3 系集校驗法 10
2.3.4 系集定量降水預報方法 12
第三章資料來源及個案介紹 13
3.1 資料來源 13
3.1.1 臺灣雷達整合回波資料 13
3.1.2 全臺三維雷達合成回波資料 13
3.1.3 QPESUMS定量降水估計資料 14
3.2 個案介紹 14
3.2.1 秋季降水個案 14
3.2.2 梅雨鋒面個案 15
第四章研究結果分析 17
4.1 回波移動場分析討論 17
4.1.1 空間相關性分析 17
4.1.2 空間分析 18
4.1.3 時間分析 19
4.2 秋季降水及梅雨鋒面預報結果分析 20
4.2.1 單一預報區間結果分析 20
4.2.2 回波預報校驗統計分析 23
4.2.3 累積雨量預報校驗統計分析 23
4.3 系集預報校驗 24
4.3.1 系集成員數量評估 24
4.3.2 預報能力評估 25
4.4 三小時累積雨量預報校驗 27
第五章結論與未來展望 29
5.1 結論 29
5.2 未來展望 30
參考文獻 31
附表 35
附圖 38

參考文獻

陳如瑜, 張偉裕, and 陳台琦, 2017: 北台灣 S 與 C 波段雙偏極化雷達定量降雨估計之比較. 大氣科學, 45, 57-81.
黃椿喜, 葉世瑄, 呂國臣, and 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, 173-196.
葉世瑄, 林沛練, 洪景山, and 黃椿喜, 2016: 機率擬合之系集定量降水預報後處理方法. 大氣科學, 44, 83-111.
潘俊瑋, 鍾高陞, 林欣弘, 陳台琦, and 姚奕安, 2018: 雷達回波變分追蹤法應用於臺灣複雜地形環境下之可行性評估. 大氣科學, 46, 1-34.
Atencia, A., and I. Zawadzki, 2014: A comparison of two techniques for generating nowcasting ensembles. Part I: Lagrangian ensemble technique. Monthly Weather Review, 142, 4036-4052.
——, 2015: A comparison of two techniques for generating nowcasting ensembles. Part II: Analogs selection and comparison of techniques. Monthly Weather Review, 143, 2890-2908.
Bellon, A., I. Zawadzki, A. Kilambi, H. C. Lee, Y. H. Lee, and G. Lee, 2010: McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique. Asia-Pacific Journal of Atmospheric Sciences, 46, 369-381.
Bowler, N. E., C. E. Pierce, and A. W. Seed, 2006: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 132, 2127-2155.
Bröcker, J., and L. A. Smith, 2007: Increasing the reliability of reliability diagrams. Weather and forecasting, 22, 651-661.
Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli, 1999: Probabilistic Predictions of Precipitation Using the ECMWF Ensemble Prediction System. Weather and Forecasting, 14, 168-189.
Chang, P.-L., and Coauthors, 2021: An Operational Multi-Radar Multi-Sensor QPE System in Taiwan. Bulletin of the American Meteorological Society, 102, E555-E577.
Chung, K.-S., and I.-A. Yao, 2020: Improving radar echo Lagrangian extrapolation nowcasting by blending numerical model wind information: Statistical performance of 16 typhoon cases. Monthly Weather Review, 148, 1099-1120.
Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology. Journal of Atmospheric and Oceanic Technology, 10, 785-797.
Ebert, E. E., 2001: Ability of a poor man′s ensemble to predict the probability and distribution of precipitation. Monthly Weather Review, 129, 2461-2480.
Foresti, L., M. Reyniers, A. Seed, and L. Delobbe, 2015: Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium. Hydrology and Earth System Sciences Discussions, 12, 6831-6879.
Foresti, L., I. V. Sideris, L. Panziera, D. Nerini, and U. Germann, 2018: A 10‐year radar‐based analysis of orographic precipitation growth and decay patterns over the Swiss Alpine region. Quarterly Journal of the Royal Meteorological Society, 144, 2277-2301.
Germann, U., and I. Zawadzki, 2002: Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Monthly Weather Review, 130, 2859-2873.
Germann, U., I. Zawadzki, and B. Turner, 2006: Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction. Journal of the Atmospheric Sciences, 63, 2092-2108.
Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15, 559-570.
Johnson, J. T., P. L. MacKeen, A. Witt, E. D. W. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm. Weather and Forecasting, 13, 263-276.
Laroche, S., and I. Zawadzki, 1994: A variational analysis method for retrieval of three-dimensional wind field from single-Doppler radar data. Journal of Atmospheric Sciences, 51, 2664-2682.
——, 1995: Retrievals of Horizontal Winds from Single-Doppler Clear-Air Data by Methods of Cross Correlation and Variational Analysis. Journal of Atmospheric and Oceanic Technology, 12, 721-738.
Lin, H.-H., C.-C. Tsai, J.-C. Liou, Y.-C. Chen, C.-Y. Lin, L.-Y. Lin, and K.-S. Chung, 2020: Multi-weather evaluation of nowcasting methods including a new empirical blending scheme. Atmosphere, 11, 1166.
Nerini, D., N. Besic, I. Sideris, U. Germann, and L. Foresti, 2017: A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform. Hydrology and Earth System Sciences, 21, 2777-2797.
Pulkkinen, S., V. Chandrasekar, and A.-M. Harri, 2019a: Stochastic spectral method for radar-based probabilistic precipitation nowcasting. Journal of Atmospheric and Oceanic Technology, 36, 971-985.
Pulkkinen, S., V. Chandrasekar, and T. Niemi, 2021: Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting. Journal of Atmospheric and Oceanic Technology, 38, 2125-2145.
Pulkkinen, S., D. Nerini, A. A. Pérez Hortal, C. Velasco-Forero, A. Seed, U. Germann, and L. Foresti, 2019b: Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1. 0). Geoscientific Model Development, 12, 4185-4219.
Radhakrishnan, C., and V. Chandrasekar, 2020: CASA prediction system over dallas–fort worth urban network: Blending of nowcasting and high-resolution numerical weather prediction model. Journal of Atmospheric and Oceanic Technology, 37, 211-228.
Ravuri, S., and Coauthors, 2021: Skilful precipitation nowcasting using deep generative models of radar. Nature, 597, 672-677.
Rinehart, R., and E. Garvey, 1978: Three-dimensional storm motion detection by conventional weather radar. Nature, 273, 287-289.
Schertzer, D., and S. Lovejoy, 1987: Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes. Journal of Geophysical Research: Atmospheres, 92, 9693-9714.
Seed, A. W., 2003: A Dynamic and Spatial Scaling Approach to Advection Forecasting. Journal of Applied Meteorology, 42, 381-388.
Su, S.-H., and Coauthors, 2022: Observing severe precipitation near complex topography during the Yilan Experiment of Severe Rainfall in 2020 (YESR2020). Quarterly Journal of the Royal Meteorological Society, 148, 1663-1682.
Tsonis, A., and G. Austin, 1981: An evaluation of extrapolation techniques for the short‐term prediction of rain amounts. Atmosphere-Ocean, 19, 54-65.
Turner, B. J., I. Zawadzki, and U. Germann, 2004: Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE). Journal of Applied Meteorology, 43, 231-248.
Tuttle, J. D., and G. B. Foote, 1990: Determination of the Boundary Layer Airflow from a Single Doppler Radar. Journal of Atmospheric and Oceanic Technology, 7, 218-232.
Zhivomirov, H., 2018: A method for colored noise generation. Romanian journal of acoustics and vibration, 15, 14-19.

指導教授

鍾高陞(Kao-Shen Chung)

審核日期

2023-8-7

推文