7,006 research outputs found
Simulation of the 1998 East Asian summer monsoon using the Purdue regional model
No full textsimulation of the 1998 east asian summer monsoon using the. purdue regional model. huang-hsiung hsu. 1. . yi-chiang yu. wen-shung kau. wu-ron hsu. department of atmospheric sciences. national taiwan university. taipei. taiwa
Dataset in support of the publication "Broadband optical fibre with an attenuation lower than 0.1 decibel per kilometre"
No full textThis dataset supports the publication:
AUTHORS: Marco Petrovich, Eric Numkam Fokoua, Yong Chen, Hesham Sakr, Abubakar Isa Adamu, Rosdi Hassan, Dong Wu, Ron Fatobene Ando, Athanasios Papadimopoulos, Seyed Reza Sandoghchi, Gregory Jasion, Francesco Poletti
TITLE: Broadband optical fibre with an attenuation lower than 0.1 decibel per kilometre </span
[[alternative]]Seasonal and interannual variability of the Taiwan Strait current
No full text[[abstract]]摘要
本研究建構了一個高解析度的臺灣海峽模式，並利用此模式來研究海峽流場與溫度場之季節與年際變化。模式的初始條件及邊界條件是使用東亞邊緣海模式之輸出結果，水平的網格配置則使用正交曲線座標，模式解析度在海峽內部約3公里向外逐漸遞減至10公里。
臺灣海峽模式之輸出場與現有的觀測資料比對結果相當一致，可以重建臺灣海峽時空變化的特性。就季節變化而言，風向季節性的轉變對於臺灣海峽有顯著的影響；盛行西南季風的夏季時，海峽流場以北向海流為主，並在彰雲隆起一帶出現上下分層的流場結構。此外，在海峽西部及臺灣灘一帶，有湧升流所造成的低溫區出現。冬季時風場轉為東北風，隨風南下的低溫海水與經海峽凹陷地形北上的溫暖海水在烏坵凹陷一帶相遇，並在海峽北部與中部分別形成U型及順時鐘的環流型態；而溫度場則是呈現南暖北冷的分布狀況。另外，在東北風盛行的冬季，海峽流場對於東北增強事件，也會有相對應的反應歷程。
就年際變化而言，在2001 ~2003年間的夏季，臺灣海峽西南風場強度以2003年為最強，在此風場的帶動下，海峽西北方低溫區的範圍與強度呈現三年中最明顯的一次。在2002/2003 年ENSO發生的冬季，其東北風較正常年減弱，而使得海峽內部南向傳輸較正常年減弱，此時南向的冷水勢力退縮，而北上的暖水勢力增強，這兩股勢力的消長，因而讓海峽溫度場分布與正常年有所不同。整體而言，風場強度及風向之改變對於臺灣海峽流場與溫度場的變化型態有密切關係，流場與溫度場之季節或是年際變化都直接受到風場的影響。
[[alternative]]Circulation structure in the northern South China Sea
No full text[[abstract]] 南海（South China Sea，SCS）位於0°N~23°N、99°~121°E，是一個東北－西南走向半封閉性的海盆。其對外的通道多為水深較淺或水道狹窄的海峽，唯有東北部的呂宋海峽具有較廣闊及縱深的水道。
本研究，利用POM（Princeton Ocean Model）建構一個高解析度的區域海流數值模式來模擬並描述南海的流況。運用巢狀嵌合模式系統之方法，利用較大模式範圍輸出之結果，提供南海模式所需之初始條件及邊界條件。並使用AVHRR（Advanced Very High Resolution Radiometer）的海表面溫度。
南海模式（South China Sea Model，SCSM）的範圍東西向介於99°~124°E，南北向介於2°~27°N，水平解析度為1/16°x 1/16°，垂直方向有26個σ層，執行時間為1999~2003年。
本模式可重現南海季節、季內變化，並可成功地模擬出南海暖流（South China Sea Warm Current，SCSWC）。經由分析及試驗得知，風應力的變化並沒有立即地影響南海暖流。另外亦求得呂宋海峽附近之渦旋脫離週期於夏天為90~120天，冬天為40~50天，冬天形成之渦旋個數較多。渦旋脫離後向西行進的速率大約是0.09~0.11公尺/秒，與此區域之第一斜壓羅士培波（1st baroclinic Rossby wave）之行進速率相近。
Parallel discrete differential dynamic programming for multireservoir
No full textAuthor name used in this publication: Chau, Kwok-Wing.2014-2015 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishedGreen (AAM
Oral history interview with Ron Wallace
No full textRon Wallace, author and instructor, talks about growing up in Durant, Oklahoma, and having a father on the police force. He recalls his college days and earning a degree in English. He explains how he developed a love of poetry initially and how he began writing poetry. Wallace also shares stories of his grandparents and reads a few of his favorite poems. He has been a Oklahoma Book Award finalist several times.The Deep Roots: Oklahoma Authors Collection is a series of interviews with authors who discuss their lives, work, and creative processes
Using portfolio optimisation models to enhance decision making and prediction
No full textPurpose: the purpose of this paper is to analyse and compare the performances of portfolio optimisation models including Markowitz's mean-variance model (MV model), Konno and Yamazaki's mean-absolute deviation portfolio optimisation model (MAD model), Young's minimax portfolio model and the VaR model.Design/methodology/approach: historical data on 43 constituent shares listed on the Hong Kong Hang Seng Index (HSI) covering a four-year period are obtained. The paper then tests the performance of each model under different scenarios and against different sets of historical data.Findings: the paper finds that different levels of required annual returns impact on portfolio composition, historical data have a major impact on the determination of portfolio composition and the level of required annual return impacts on how optimisation models perform.Practical implications: the paper posits that with a comprehensive understanding of the performance of each of these performance optimisation models, investors may be able to develop a better understanding of how to adjust investment risk strategies, thus preventing serious losses.Originality/value: there are two major points of value to this paper. In the first place, the paper presents an original review of portfolio optimisation models. Second, using “real” data, the paper utilises five different scenarios to test the performance of each model under different situations
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