夏季的午後熱對流經常造成致災性的短延時強降雨，快速生成並發展的對流系統經常讓民眾與預警系統無法及時因應。本研究將使用雷達資料來了解對流胞在生命期三個階段(發展期、成熟期、消散期)的微物理變化特徵，透過雙偏極化參數(Dual-PolarimetricMeasurements，DPMs)可以瞭解降水粒子的特徵並探討對流胞隨時間的變化特性。選取Taiwan-Area Heavy rain Observation and Prediction Experiment (TAHOPE)期間的午後對流事件，並使用五分山雷達站(RCWF)的雙偏極化參數資料進行分析，而為了追蹤對流胞加以分析雙偏極化參數特徵隨時間的變化，使用Storm Motion Analysis by Radar Tracking(SMART)系統來辨識與追蹤對流胞，給定的回波門檻(40𝑑𝐵𝑍)與面積門檻(10𝑘𝑚2)辨識強對流系統，並將辨識出來的幾何特徵透過計算進行後續的追蹤。
藉由雙偏極化變數從對流發展高度、強度和結構進行分析，從個案在時序上的變化可以發現變數在對流發展各時期的變化特徵，𝑍𝐷𝑅在發展期初期呈現數值較大且分布高度較高的特徵，隨後在成熟期前後隨時間降低並減弱直至消散；𝐾𝐷𝑃則是在發展初期數值較小，在成熟期前後在中高層呈現大數值分布，成熟期相對大的數值則分布於低至中層，直到消散期才明顯減弱，透過𝑍𝐷𝑅和𝐾𝐷𝑃的變化可以完整呈現對流發展中，上升氣流初期將大水滴帶至高空隨後破裂成多個小水滴下落的微物理過程。
統計分析中，使用垂直累積液態水含量(Vertically Integrated Liquid，VIL)作為對流胞的潛在強度分類指標，由統計結果發現各雙偏極化變數的變化特徵皆與對流發展階段和VIL 極大值有高度相關性，且當對流VIL 極大值愈大時，各雙偏極化變數的變化特徵愈顯著，尤其𝑍𝐷𝑅於發展初期呈現出明顯的大數值與高紮實度，該特徵可以作為因熱力作用而生成之強對流的預警指標之一。

Short-duration heavy rainfall from afternoon thunderstorms in summer often causes disasters. Early warnings of rapid development of convective systems remain challenging. This
study investigates the microphysical characteristics of convective cells using dual-polarimetric measurements (DPMs) observations during their three life stages (developing, mature, and dissipating), enabling the analysis of precipitation particle characteristics and their temporal
evolution. Afternoon thunderstorm events were selected from Taiwan-Area Heavy rain Observation and Prediction Experiment (TAHOPE), and dual-polarimetric data from the Wufenshan Weather Radar (RCWF) were analyzed. To analyze convective cell characteristics over time, the Storm Motion Analysis by Radar Tracking (SMART) system was applied. Strong convective systems were identified based on a reflectivity threshold of 40 dBZ and a minimum area of 10 𝑘𝑚2. The corresponding geometric features were then tracked through computational
analysis.
By utilizing dual-polarimetric variables to examine the development, intensity, and structure of convection. The analysis of the temporal evolution of precipitation reveals changes in these variables during different stages of convection. At the early stage of the developing stage, 𝑍𝐷𝑅 exhibits high values at higher levels, which then gradually decrease and weaken before the mature stage until dissipation. 𝐾𝐷𝑃 remains relatively low in the initial phase but increases at mid-to-upper levels before the mature stage. In the mature phase, 𝐾𝐷𝑃 exhibits high values at lower to mid-levels, and then significantly weakens in the dissipation stage. The variations in 𝑍𝐷𝑅 and 𝐾𝐷𝑃 show the microphysical process of large raindrops being carried
aloft by updrafts, subsequently breaking into smaller droplets that descend.
For statistical analysis of afternoon thunderstorm cases during TAHOPE, this study uses Vertically Integrated Liquid (VIL) as an indicator for classifying the potential intensity of convective cells. The analysis reveals that dual-polarimetric variables exhibit distinct variations across different stages of convective development and VIL maxima. As the VIL maximum increases, these features become more pronounced, especially the high values and strong solidity of 𝑍𝐷𝑅 during the early developing stage. This characteristic may serve as an early warning indicator for strong convection by thermal forcing.

繆炯恩與楊明仁， 2018：2015 年6 月14 日台北盆地劇烈午後雷暴個案研究:對流胞合
併機制與強降雨過程探討。大氣科學，46(4)，427-454。
李泓寬，2022：「利用模糊邏輯法預報臺灣地區午後對流肇始事件」。國立中央大學
碩士論文。
李泓寬，2024：「S 波段與C 波段雙偏極化雷達於強降雨系統之觀測資料系統性分析
與應用」。交通部中央氣象署計畫。
施凱智，2021：「應用雷達觀測追蹤分析臺北盆地夏季午後雷暴雨胞」。國立臺灣大
學碩士論文。
葉玉婕，2021：「統計分析2008 年西南氣流實驗期間對流系統的雙偏極化雷達拉格朗
日特徵」。國立中央大學碩士論文。
楊承泰，2022：「台灣周邊中尺度對流系統及綜觀環境特徵統計分析」。國立中央大
學碩士論文。
吳冠伯，2019：「2005-2015 年暖季弱綜觀環境下對流降水系統之特徵統計」。國立
中央大學碩士論文。
Amburn, S. A., and P. L. Wolf, 1997: VIL density as a hail indicator. Weather and
Forecasting, 12, 473–478.
Capsoni, C., M. D’Amico, and P. Locatelli, 2008: Statistical properties of rain cells in the
Padana Valley. Journal of Atmospheric and Oceanic Technology, 25, 2230–2244.
Chen, T.-C., M.-C. Yen, J.-D. Tsay, C.-C. Liao, and E. S. Takle, 2014: Impact of Afternoon
Thunderstorms on the Land–Sea Breeze in the Taipei Basin during Summer: An
Experiment. Journal of Applied Meteorology and Climatology, 53, 1714–1738.
Chudler, K., S. A. Rutledge, and B. Dolan, 2022: Unique Radar Observations of Large
Raindrops in Tropical Warm Rain during PISTON. Monthly Weather Review, 150,
2719–2736.
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.
Dixon, M., and U. Romatschke, 2022: Three-Dimensional Convective–Stratiform Echo-Type
Classification and Convectivity Retrieval from Radar Reflectivity. Journal of
Atmospheric and Oceanic Technology, 39, 1685–1704.
Feng, Z., R. A. Houze, L. R. Leung, F. Song, J. C. Hardin, J. Wang, W. I. Gustafson, and C.
R. Homeyer, 2019: Spatiotemporal characteristics and Large-Scale environments of
mesoscale convective systems east of the Rocky Mountains. Journal of Climate, 32,
7303–7328.
Gourley, J. J., P. Tabary, and J. P. Du Chatelet, 2007: A Fuzzy Logic Algorithm for the
Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar
Observations. Journal of Atmospheric and Oceanic Technology, 24, 1439–1451.
Greene, D. R., and R. A. Clark, 1972: Vertically Integrated Liquid Water—A new analysis
tool. Monthly Weather Review, 100, 548–552.
Hirano, K., and M. Maki, 2018: Imminent Nowcasting for Severe Rainfall Using Vertically
Integrated Liquid Water Content Derived from X-Band Polarimetric Radar. Journal of
the Meteorological Society of Japan Ser II, 96A, 201–220.
Homeyer, C. R., and M. R. Kumjian, 2015: Microphysical Characteristics of Overshooting
Convection from Polarimetric Radar Observations. Journal of the Atmospheric
Sciences, 72, 870–891.
Johnson, J. T., P. L. MacKeen, A. Witt, E. De Wayne 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.
Jung, C.-J., and B. J.-D. Jou, 2023: Bulk microphysical characteristics of a Heavy-Rain
complex thunderstorm system in the Taipei Basin. Monthly Weather Review, 151,
877–896.
Kumjian, M. R., A. V. Ryzhkov, V. M. Melnikov, and T. J. Schuur, 2010: Rapid-Scan Super-
Resolution Observations of a Cyclic Supercell with a Dual-Polarization WSR-88D.
Monthly Weather Review, 138, 3762–3786.
Kumjian, M. R., A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips,
2014: The Anatomy and Physics of ZDR Columns: Investigating a Polarimetric Radar
Signature with a Spectral Bin Microphysical Model. Journal of Applied Meteorology
and Climatology, 53, 1820–1843.
Kuster, C. M., T. J. Schuur, T. T. Lindley, and J. C. Snyder, 2020: Using ZDR columns in
forecaster Conceptual Models and Warning Decision-Making. Weather and
Forecasting, 35, 2507–2522.
Liang, Q., Y. Feng, W. Deng, S. Hu, Y. Huang, Q. Zeng, and Z. Chen, 2010: A composite
approach of radar echo extrapolation based on TREC vectors in combination with
model-predicted winds. Advances in Atmospheric Sciences, 27, 1119–1130.
Loeffler, S. D., and M. R. Kumjian, 2018: Quantifying the separation of enhanced ZDR and
KDP regions in nonsupercell tornadic storms. Weather and Forecasting, 33, 1143–
1157.
Loh, J. L., W.-Y. Chang, H.-W. Hsu, P.-F. Lin, P.-L. Chang, Y.-L. Teng, and Y.-C. Liou,
2022: Long-Term assessment of the reflectivity biases and Wet-Radome effect using
collocated operational S- and C-Band Dual-Polarization radars. IEEE Transactions on
Geoscience and Remote Sensing, 60, 1–17.
Masuda, A., and E. Nakakita, 2014: Development of a Technique to Identify the Stage of
Storm Life Cycle Using X-band Polarimetric Radar. Journal of Japan Society of Civil
Engineers Ser B1 (Hydraulic Engineering), 70, I_493-I_498.
Miao, J.-E., and M.-J. Yang, 2020: A modeling study of the severe afternoon thunderstorm
event at Taipei on 14 June 2015: the roles of sea breeze, microphysics, and terrain.
Journal of the Meteorological Society of Japan Ser II, 98, 129–152.
Miller, P. W., and T. L. Mote, 2017: A climatology of weakly forced and pulse thunderstorms
in the southeast United States. Journal of Applied Meteorology and Climatology, 56,
3017–3033.
Nakakita, E., H. Sato, R. Nishiwaki, H. Yamabe, and K. Yamaguchi, 2017: Early detection of
Baby-Rain-Cell aloft in a severe storm and risk projection for urban flash flood.
Advances in Meteorology, 2017, 1–15.
Pilorz, W., M. Zięba, J. Szturc, and E. Łupikasza, 2022: Large hail detection using radarbased
VIL calibrated with isotherms from the ERA5 reanalysis. Atmospheric
Research, 274, 106185.
Pulkkinen, S., V. Chandrasekar, A. Von Lerber, and A.-M. Harri, 2020: Nowcasting of
convective rainfall using volumetric radar observations. IEEE Transactions on
Geoscience and Remote Sensing, 58, 7845–7859.
Rinehart, R. E., and E. T. Garvey, 1978: Three-dimensional storm motion detection by
conventional weather radar. Nature, 273, 287–289.
Romatschke, U., and M. J. Dixon, 2022: Vertically resolved Convective–Stratiform Echo-
Type identification and convectivity retrieval for vertically pointing radars. Journal of
Atmospheric and Oceanic Technology, 39, 1705–1716.
Rowe, A. K., S. A. Rutledge, and T. J. Lang, 2012: Investigation of Microphysical Processes
Occurring in Organized Convection during NAME. Monthly Weather Review, 140,
2168–2187.
Seo, B.-C., W. F. Krajewski, and Y. Qi, 2019: Utility of vertically integrated liquid water
content for Radar-Rainfall estimation: quality control and precipitation type
classification. Atmospheric Research, 236, 104800.
Snyder, J. C., A. V. Ryzhkov, M. R. Kumjian, A. P. Khain, and J. Picca, 2015: A ZDR
column detection algorithm to examine convective storm updrafts. Weather and
Forecasting, 30, 1819–1844.
Snyder, J. C., H. B. Bluestein, D. T. Dawson II, and Y. Jung, 2017: Simulations of
polarimetric, X-Band radar signatures in supercells. Part II: ZDR columns and rings
and KDP columns. Journal of Applied Meteorology and Climatology, 56, 2001–2026.
Starzec, M., C. R. Homeyer, and G. L. Mullendore, 2017: Storm labeling in three dimensions
(SL3D): a Volumetric radar Echo and Dual-Polarization Updraft Classification
algorithm. Monthly Weather Review, 145, 1127–1145.
Sun, J., and Coauthors, 2013: Use of NWP for nowcasting convective Precipitation: Recent
progress and challenges. Bulletin of the American Meteorological Society, 95, 409–
426.
Tuftedal, K. S., B. P. Treserras, M. Oue, and P. Kollias, 2024: Shallow- and deep-convection
characteristics in the greater Houston, Texas, area using cell tracking methodology.
Atmospheric Chemistry and Physics, 24, 5637–5657.
Van Lier-Walqui, M., and Coauthors, 2016: On Polarimetric Radar Signatures of Deep
Convection for Model Evaluation: Columns of Specific Differential Phase Observed
during MC3E*. Monthly Weather Review, 144, 737–758.
Wilson, M. B., and M. S. Van Den Broeke, 2021: An automated Python algorithm to quantify
ZDR ARC and KDP–ZDR separation signatures in supercells. Journal of Atmospheric
and Oceanic Technology, 38, 371–386.
Zou, H., S. Wu, J. Shan, and X. Yi, 2019: A method of radar echo extrapolation based on
TREC and Barnes filter. Journal of Atmospheric and Oceanic Technology, 36, 1713–
1727.