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

Long Term Ground Based Precipitation...

Rodriguez, Luciano .

FindBook

Google Book

Amazon

博客來

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability./
作者:	Rodriguez, Luciano .
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	559 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
標題:	Climate change. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27738541
ISBN:	9781392466605

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability.
Rodriguez, Luciano .

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 559 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Ph.D.)--Chapman University, 2020.

This item must not be sold to any third party vendors.

This dissertation evaluates response variables (classifiers) on various models applied to the detection of El Nino Southern Oscillation (ENSO) on California's seven climate divisions by using modeled and gauge (in-situ/ground) precipitation measurements and various climate indices. Three scientific studies were conducted as part of this research for evaluation of spatial and temporal ENSO events from modeled and gauge data using: 1) Wavelets 2) Autoregressive-moving-average (ARMA) model / Empirical Mode Decomposition (EMD) 3) Vector Generalized Linear Model (VGLM). This dissertation aims to propose and evaluate a methodology for developing a model to measure ENSO events accurately. The hypothesis is that precipitation data (either modeled or gauge) can be used to forecast ENSO events. One can create an assimilated index from various weighted indices as opposed to solely relying on popular climate indices as SOI, PDO, or Nino 3.4. Another objective is to identify how well modeled precipitation compares to gauge precipitation (ground truth) and if the composition of the indices are the same for both. A methodology for validating the generalization performance of the model is proposed and implemented (Controlled Parameter Cross-Validation). An analysis was performed using modeled data (regeneration of observable measurements and ground measurements) and gauge from seven climate divisions in California.

ISBN: 9781392466605Subjects--Topical Terms:

2079509
Climate change.
Subjects--Index Terms:

ARMA

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability.
LDR:02588nmm a2200361 4500 001 2266255
005 20200608092718.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9781392466605
035 $a (MiAaPQ)AAI27738541
035 $a AAI27738541
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rodriguez, Luciano . $3 3543441
245 1 0 $a Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 559 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
500 $a Advisor: El-Askary, Hesham.
502 $a Thesis (Ph.D.)--Chapman University, 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This dissertation evaluates response variables (classifiers) on various models applied to the detection of El Nino Southern Oscillation (ENSO) on California's seven climate divisions by using modeled and gauge (in-situ/ground) precipitation measurements and various climate indices. Three scientific studies were conducted as part of this research for evaluation of spatial and temporal ENSO events from modeled and gauge data using: 1) Wavelets 2) Autoregressive-moving-average (ARMA) model / Empirical Mode Decomposition (EMD) 3) Vector Generalized Linear Model (VGLM). This dissertation aims to propose and evaluate a methodology for developing a model to measure ENSO events accurately. The hypothesis is that precipitation data (either modeled or gauge) can be used to forecast ENSO events. One can create an assimilated index from various weighted indices as opposed to solely relying on popular climate indices as SOI, PDO, or Nino 3.4. Another objective is to identify how well modeled precipitation compares to gauge precipitation (ground truth) and if the composition of the indices are the same for both. A methodology for validating the generalization performance of the model is proposed and implemented (Controlled Parameter Cross-Validation). An analysis was performed using modeled data (regeneration of observable measurements and ground measurements) and gauge from seven climate divisions in California.
590 $a School code: 1538.
650 4 $a Climate change. $2 bicssc $3 2079509
653 $a ARMA
653 $a Climate
653 $a ENSO
653 $a Precipitation
653 $a Time series
690 $a 0404
710 2 $a Chapman University. $b Schmid College of Science & Technology. $3 3541548
773 0 $t Dissertations Abstracts International $g 81-09B.
790 $a 1538
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27738541