東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Methodologies for seasonal adjustmen...

Yu, Chunguang.

FindBook

Google Book

Amazon

博客來

Methodologies for seasonal adjustment in ENSO studies.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Methodologies for seasonal adjustment in ENSO studies./
作者:	Yu, Chunguang.
面頁冊數:	156 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6001.
Contained By:	Dissertation Abstracts International71-10B.
標題:	Climate Change. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3421717
ISBN:	9781124216799

Methodologies for seasonal adjustment in ENSO studies.
Yu, Chunguang.

Methodologies for seasonal adjustment in ENSO studies. - 156 p.

Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6001.

Thesis (Ph.D.)--George Mason University, 2010.

The El Nino/Southern Oscillation (ENSO) represents the most important natural part of global climate variability. The Southern Oscillation Index (SOI) and the Nino Regional Sea Surface Temperature (SST) Indices measure ENSO fluctuations over the atmosphere and the ocean, respectively. Scientists recognize that relying on the unchanged seasonal cycles as an annual reference is a weak assumption for anomaly estimation and, thus, the accuracy of these two climate indicators is affected. Hence, several seasonal adjustment methodologies to estimate a modulated seasonal cycle have been studied and employed.

ISBN: 9781124216799Subjects--Topical Terms:

894284
Climate Change.

Methodologies for seasonal adjustment in ENSO studies.
LDR:02479nam 2200313 4500 001 1398836
005 20110915090242.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124216799
035 $a (UMI)AAI3421717
035 $a AAI3421717
040 $a UMI $c UMI
100 1 $a Yu, Chunguang. $3 1677747
245 1 0 $a Methodologies for seasonal adjustment in ENSO studies.
300 $a 156 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6001.
500 $a Adviser: Ruixin Yang.
502 $a Thesis (Ph.D.)--George Mason University, 2010.
520 $a The El Nino/Southern Oscillation (ENSO) represents the most important natural part of global climate variability. The Southern Oscillation Index (SOI) and the Nino Regional Sea Surface Temperature (SST) Indices measure ENSO fluctuations over the atmosphere and the ocean, respectively. Scientists recognize that relying on the unchanged seasonal cycles as an annual reference is a weak assumption for anomaly estimation and, thus, the accuracy of these two climate indicators is affected. Hence, several seasonal adjustment methodologies to estimate a modulated seasonal cycle have been studied and employed.
520 $a One weakness of modulated seasonal adjustment methodologies, however, is their inability to quantify the separation of seasonal variation from the original data due to the intrinsic ambiguity of the definition for seasonal variation. Moreover, no previous study has characterized these methodologies, evaluated their performance, or compared and contrasted their impact on the description of the ENSO phenomenon.
520 $a Based on rigorous statistical analysis and comparison, this thesis: (1) develops a tool to separate the seasonal and inter-annual variations quantitatively and also extends the implementation of modulated seasonal adjustment methods to spatiotemporal data sets, (2) provides guidelines on which seasonal adjustment methodology one should choose when handling seasonal variability in climate data-processing for different circumstances, and (3) highlights the impact of the seasonal adjustment methodology on the interpretation of ENSO phenomena.
590 $a School code: 0883.
650 4 $a Climate Change. $3 894284
650 4 $a Physical Oceanography. $3 1019163
650 4 $a Statistics. $3 517247
690 $a 0404
690 $a 0415
690 $a 0463
710 2 $a George Mason University. $3 1019450
773 0 $t Dissertation Abstracts International $g 71-10B.
790 1 0 $a Yang, Ruixin, $e advisor
790 $a 0883
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3421717