致災性強對流與冰雹事件好發於台灣夏季，如何針對此類劇烈天氣事件進行更快速、準確的預警，為當前氣象局亟欲提升的能力。本研究利用中央氣象局預報中心所使用的對流監測平台(System for Convection Analysis and Nowcasting, SCAN)歷史事件，進行2011~2018年台灣夏季(5~8月)於不同天氣系統(綜觀與弱綜觀)及地理環境(北部及南部)下的對流胞統計特徵分析，來建構對流胞即時預報產品，使得災害潛勢區域能有定量上的依據。此外，收集2011~2020年間的夏季冰雹事件的目擊資訊，以檢驗SCAN系統在台灣地區對冰雹的預警能力。
特徵統計結果顯示，台灣夏季對流胞的移動速度分布以2~10 m/s居多、最大反射率集中在45~55dBZ、生命期則以1小時以內為主。此外，分布在海上的對流胞移動速度相較陸地上的對流胞移動速度為快，因此由綜觀天氣產生之對流胞移動速度會相較於內陸弱綜觀天氣生成之對流胞快。比較一小時預報的路徑誤差，南部地區的誤差比北部大、綜觀天氣系統下的誤差比弱綜觀大。而經緯向誤差大致相同，統計結果主要受到綜觀天氣系統對流所主導。本研究進一步結合過去開發的對流胞路徑潛勢預報(Potential Track Area for Storm, PTAS)與颱風侵襲機率預報(Wind Speed Probability, WSP)技術，利用統計誤差特性建構對流胞侵襲機率預報(Storm Threat Optimal Probability, STOP)產品。校驗結果顯示，該產品於易受強對流侵襲地區的整體預報誤差在10%以內。此外，區分天氣系統所建構出之即時預報在一般對流胞速度下(10 m/s 以下)並無太大差異。而針對冰雹預警能力的方面，當前SCAN系統對於冰雹事件的預警時間(lead time)以15分鐘以內居多，且有二大預警問題：(1)部分目擊事件未被預警以及(2)假警報過多。本研究分別提出(1)以冰雹目擊個案特徵統計結果下修預警門檻以及(2)以台灣探空資料修正SCAN 系統背景溫度高度場等改善建議。最終，以個案證實對流胞即時預報於冰雹預警上的適用性。

System for Convection Analysis and Nowcasting (SCAN) is one of the operational forecast systems used by the Central Weather Bureau (CWB), which can provide storm cells and hail information. This study presents an 8-year analysis of summer storm cells in Taiwan (from 2011 to 2018, May-Aug) based on the SCAN, discusses the characteristics of cells under different weather systems (synoptic or weak synoptic), and geographical environments (north or south of Taiwan). The statistical results of storm track errors are used to establish the storm cell nowcasting, in order to define 0-1-hour severe storm warning area and quantitatively improve the warning capability of severe weather. In addition, this study also collected summer hail events from 2011 to 2020 in Taiwan to check the hail warning capability of the SCAN.
The results show that the cell speed in Taiwan is mostly 2~10 m/s, the maximum reflectivity is concentrated at 45~55dBZ, and the lifetime is mainly within 1 hour. The cells that come from the ocean (synoptic) will have a higher moving speed than those generated in the inland (weak synoptic). Comparing the one-hour forecast error, the error in the southern region is higher than the error in the northern region, the error in the synoptic day is higher than which in the weak synoptic day, and the latitude and longitude errors are roughly the same. Furthermore, the statistical results are dominated by the synoptic weather. Construct the Storm Threat Optimal Probability (STOP) forecast using the statistical data. The verification shows that the forecast error of the product is within 10%. Besides, the forecast results in the different weather systems are nonsignificant differences in most cases (below 10 m/s). In terms of hail warning, the lead time of hail that SCAN can provide is around 15 minutes, and there have two major warning problems: (1) some hail cases were not warning in the SCAN system, and (2) there are too many false alarms. This research proposes (1) lowering the warning threshold based on the statistical results of the hail report and (2) correcting the default value of the SCAN system by using the sounding data in Taiwan to solve the problems. Finally, confirm that the probability forecast can be used for hail warning.

顧欣怡，2006: 颱風侵襲機率預報系統開發。中央氣象局研究發展專題第CWB95-1A-07號，76頁。

張保亮，丘台光，陳嘉榮，張惠玲，王碧霞，2006: QPESUMS系統對流胞偵測與預報路徑校驗，中央氣象局2006年天氣分析與預報研討會。

蔡孝忠，2007: 中央氣象局颱風路徑機率預報新產品−路徑機率預報，中央氣象局通訊月刊2007年12月號。

張保亮，林品芳，陳嘉榮，2008: 台灣地區午後對流雷達觀測特徵分析，中央氣象局 2008 年天氣分析與預報研討會。

蔡甫甸，2011: 中央氣象局即時預報發展之探討-只用SCAN 追蹤分析2011年新店龍捲風所伴隨之風暴個案，中央氣象局2011年天氣分析與預報研討會。

蔡孝忠、呂國臣、許乃寧、賈愛玫，2011: 蒙地卡羅法在颱風侵襲機率估計的應用。大氣科學期刊，39(3)，269-288。

劉人瑋，交通部中央氣象局，2013: 「Hail Detection Algorithm (HDA) in SCAN」。

劉宇其、王品翔，2018: WSR-88D冰雹演算法於臺灣冰雹預報之應用，107年度研究報告第CWB 107-1A-01號。

鄒益豪，2020: 台灣地區對流胞特性統計分析與即時路徑預報之改善。國立中央大學大氣科學學系碩士論文。

Bally, J., 2004: The Thunderstorm Interactive Forecast System: Turning automated thunderstorm tracks into severe weather warnings. Wea. Forecasting, 19, 64–72.

Bowler, N., C. Pierce, and A. Seed, 2006: STEPS: A probabilistic precipitationforecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quart. J. Roy. Meteor. Soc., 132, 2127–2155.

Brovelli, P., S. Sénési, E. Arbogast, P. Cau, S. Cazabat, M. Bouzom, and R. Reynaud, 2005: Nowcasting thunderstorms with SIGOONS: A significant weather object oriented nowcasting system. World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting, Toulouse, France, WMO, 7.08.

Capozzi, V., E. Picciotti, V. Mazzarella, F. S. Marzano, and G. Budillon, 2018: Fuzzy-logic detection and probability of hail exploiting short-range X-band weather radar. Atmos Res, 201, 17-33.

Chase, P. 0., 1969: A note on drawing probability sectors. Mon. Wea. Rev., 97, 602-603.

Chen, C. S., and Y. L.Chen, 2003: The rainfall characteristic of Taiwan. Mon. Wea. Rev., 131, 1323–1341.

Chen, C.-S., Y.-L. Chen, C.-L. Liu, P.-L. Lin, and W.-C. Chen, 2007: Statistics of heavy rainfall occurrences in Taiwan. Wea. Forecasting, 22, 981–1002.

Chen, T. C., J. D. Tsay, and E. S. Takle, 2016: A Forecast Advisory for Afternoon Thunderstorm Occurrence in the Taipei Basin during Summer Developed from Diagnostic Analysis. Weather and Forecasting, 31(2), 531-552.

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. Mon. Weather. Rev., 148(3), 1099–1120.

DeMaria, M., Knaff, J. A., Knabb, R., Lauer, C., Sampson, C. R., DeMaria, R. T., 2009: A new method for estimating tropical cyclone wind speed probabilities, Weather and Forecasting, 24, 1573-1591.

del Moral, A., del Carmen Llasat, M., Rigo, T., 2020: Connecting flash flood events with radar-derived convective storm characteristics on the northwestern Mediterranean coast: Knowing the present for better future scenarios adaptation. Atmos. Res., 238, 104863.

Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis and nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10(6), 785–797.

Eilts, M. D., J. T. Johnson, E. D. Mitchell, S. Sanger, G. J. Stumpf, A. Witt, K. W. Thomas, K. Hondl, D. Rhue and M. H. Jain, 1996: Severe weather warning decision support system. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc., 536-540.

Foote, G. B., and C. A. Knight, 1979: Results of a randomized hail suppression experiment in northeast Colorado. Part I: Design and conduct of the experiment. J. Appl. Meteor., 18, 1526–1537.

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(716), 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. Mon. Wea. Rev., 130, 2859-2873.

Han, L., S. Fu, G. Yang, H. Wang, Y. Zheng, and Y. Lin , 2008: A stochastic method for convective storm identification, tracking and nowcasting. Progress in Natural Science, 18, 1557–1563.

Han, L., X. Yu, Y. Zheng, M. Chen, H. Wang, and Y. Lin, 2009b: Statistic characteristics of severe convective storm during warm‐season in the Beijing‐Tianjin region and its vicinity. Chin. Sci. Bull. 54: 2493– 2498.

Hope, J.R. and C.J. Neumann, 1970: An operational technique for relating the movement of existing tropical cyclones to past tracks. Mon. Wea. Rev., 98, 925-933.

Hou, J. Y., and P. Wang, 2017: Storm Tracking via Tree Structure Representation of Radar Data. J Atmos Ocean Tech, 34, 729-747.

Jarrell,.J.D.,S. Brand and D.S. Nicklin, 1978: An analysis of Western North Pacific tropical cyclone forecast errors. Mon. Wea. Rev., 106, 925-937.

Joe, P., and P. T. May, 2003: Correction of dual PRF velocity errors for operational Doppler weather radars. J. Atmos. Oceanic Technol., 20, 429–442.

Johnson, J. T., P. L. MacKeen, A. Witt, E. D. 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 Forecast, 13, 263-276.

Jung, S. H., and G. Lee, 2015: Radar-based cell tracking with fuzzy logic approach. Meteorol Appl, 22, 716-730.

Kato, R., S. Shimizu, K. Shimose, T. Maesaka, K. Iwanami, and H. Nakagaki, 2017: Predictability of meso-gamma-scale, localized, extreme heavy rainfall during the warm season in Japan using high-resolution precipitation nowcasts. Q J Roy Meteor Soc, 143, 1406-1420.

Kawabata, Y., and M. Yamaguchi, 2020: Probability ellipse for tropical cyclone track forecasts with multiple ensembles. J. Meteor. Soc. Japan, 98, 821-833.

Kitzmiller, D. H., and J. P. Breidenbach, 1993: Probabilistic nowcasts of large hail based on volumetric reflectivity and storm environment characteristics. Preprints 26th International Conference on Radar Meteorology, Norman, Amer. Meteor. Soc., 157-159.

Kishimoto, K., 2010: JMA's five-day tropical cyclone track forecast. RSMC Tokyo–Typhoon Center Technical Review, 12, 55–63.

Lakshmanan, V., T. Smith, G. Stumpf, and K. Hondl, 2007: The Warning Decision Support System–Integrated Information. Weather and Forecasting, 22, 596-612.

Li PW, Lai EST. 2004a. Applications of radar‐based nowcasting techniques for mesoscale weather forecasting in Hong Kong. Meteorol. Appl. 11: 253–264.

Lin, P. F., P. L. Chang, B. J. D. Jou, J. W. Wilson, and R. D. Roberts, 2011: Warm season afternoon thunderstorm characteristics under weak synoptic-scale forcing over Taiwan Island. Weather and forecasting, 26(1), 44-60.

Mather, G. K., D. Treddenick, and R. Parsons, 1976: An observed relationship between the height of the 45-dBZ contours in storm profiles and surface hail reports. J. Appl. Meteor., 15, 1336–1340.

Morel, C., and S. Senesi, 2002: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quarterly Journal of the Royal Meteorological Society, 128(584), 1953-1971.

Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003: NCAR Auto-Nowcast System. Weather and Forecasting, 18, 545–561.

Novo S, Martinez D, Puentes O, 2013. Tracking, analysis, and nowcasting of Cuban convective cells as seen by radar. Meteorol. Appl. 21: 585–595.

Potts RJ, Keenan TD, May PT. 2000. Radar characteristics of storms in the Sydney area. Mon. Weather Rev. 128: 3308–3319.

Rasmussen, K. L., Zuluaga, M. D., and Houze, R. A., 2014: Severe convection and lightning in subtropical South America. Geophy. Res. Lett. 41, 7359–7366.

Waldvogel, A., W. Schmid, and P. Grimm, 1979: Criteria for the detection of hail cells. J. Appl. Meteor., 18, 1521–1525.

Wapler, K., 2017: The life-cycle of hailstorms: Lightning, radar reflectivity and rotation characteristics. Atmos. Res., 193, 60–72.

Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. W. Mitchell, and K.W. Thomas, 1998a: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286–303.

Wong, M., W. Wong, and E. Lai, 2006: From SWIRLS to RAPIDS: Nowcast applications development in Hong Kong. Proc. PWS Workshop on Warnings of Real-Time Hazards by Using Nowcasting Technology, Sydney, Australia, WMO, 9–13.