學位論文

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    2020年梅雨季前段極端降雨事件之原因及多重尺度交互作用分析
    (2024) 邱彥超; Chiu, Yen-Chao
    本研究調查了2020年5月21日至23日，臺灣地區所經歷的一次破42年梅雨季紀錄之極端降水事件（Y20R事件）。此次降水事件的成因主要與多個天氣系統之間的交互作用有關。2020年梅雨季前段，西北太平洋副熱帶高壓異常強大，導致西南季風顯著增強，從而加強了南海與中南半島一帶往東亞地區的水氣傳輸。在強烈的季風影響之下，於青藏高原東南側形成的西南渦（SWV）向臺灣方向移動。當SWV通過臺灣北側海面時，它提供了臺灣附近的潮濕渦旋環境，其底層的西北風與西南季風在臺灣海峽中部匯合，形成了有利於鋒面生成的動力和熱力條件，進而增強了海峽上的鋒面活動。這一過程中，SWV的位置則會影響鋒面生成的位置，而鋒面的位置又對降水強度影響甚大，當鋒面位於臺灣南部雨區的北側時，會顯著增加該地區的降水量。最後，觀測系統模擬實驗的結果顯示，在預報系統中加入南海北部的投落送資料可以提升對南海地區風場和水氣場的初始條件，從而改善臺灣，尤其是臺灣南部地區的強降水預報能力。本研究對理解臺灣梅雨季降水的形成機制以及大尺度背景場(2020年環境場)和中尺度天氣系統(SWV)之間的交互作用具有重要意義，並對提升極端降水事件的預報能力提供了一個參考方案。未來的研究可以擴大範圍，包含更多類型的降水事件，以便深入瞭解其降水機制。
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    全球暖化影響之下日降水與極端降水事件變化之探討
    (2006) 吳郁娟
    Weather and climate events can have serious and damaging effects on human society (such as flood, heavy precipitation, heat wave, etc.). In this study, the simulation of the variability and extremes of daily rainfall for the present and the future climate is investigated. This is done by the ECHAM4/OPYC3 GSDIO for the period 1960-1990 and the Special Report on Emission Scenarios (SRES) A2 (rapid CO2 increase) and B2 (moderate CO2 increase) forcing scenario for the period of 2070-2100. Moreover, observational rainfall data from the Global Precipitation Climatology Project (GPCP, 1996-2004) is considered. In general, analysis of model data revealed agreement with observations. For the future, the ECHAM4/OPYC3 simulates the variability of the daily rainfall predicts the most pronounced precipitation changes are found in high latitudes of the Northern Hemisphere for the winter. However for some continental areas, the change of mean precipitation and rainfall intensity is not coincident. A clear reduction in the probability of wet day, in particular, for the large areas in the northern mid-latitudes and subtropics. Despite this decrease the relative contribution of heavy precipitation has grown due to the corresponding increase of the scale parameter of the gamma distribution. This implies a more extreme climate with higher probabilities of droughts and heavy precipitation events. Furthermore, the variability of the 99.7th percentile also implies in the area of heavy precipitation, stronger heavy rainfall will happen in the future, vice versa. Extreme value theory based on GEV and GPD provides a much more complete analysis of the statistical distribution of extreme rainfall event. We have obtained statistically significant spatial models of the three parameters of GEV and GPD. N-years return level form GEV or GPD all show the relative changes in extreme precipitation is larger than change in total precipitation.