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

Using ICESat-2 Satellite Altimeter D...

Koo, Younghyun.

FindBook

Google Book

Amazon

博客來

Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea./
作者:	Koo, Younghyun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	202 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Remote sensing. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419136
ISBN:	9798379580254

Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea.
Koo, Younghyun.

Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 202 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2023.

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

The fine spatial resolution (~11 m of footprint) and precision (~2 cm of vertical precision) of NASA's ICESat-2 (IS2) satellite altimeter data allow us to detect sea ice deformation features, such as pressure ridges and leads. Moreover, the thermodynamic and dynamic sea ice processes can be investigated by monitoring sea ice deformation features for a large area over a regular period. However, the IS2 sea ice products (ATL07/ATL10) have critical problems in further accurate monitoring of sea ice: (1) spatial sparsity, (2) dark lead misclassification, and (3) incapability of thin/gray ice detection. In this study, I address these issues and improve the monitoring of sea ice freeboard in the Ross Sea using IS2 data. In Chapter 2, a geostatistical approach, kriging, is employed to address the spatial sparsity issue and interpolate the freeboard values where the IS2 tracks are not directly available. The freeboard maps retrieved from the kriging method exhibit the weekly distribution of sea ice freeboard and persistent polynya areas in the Ross Sea. In Chapter 3, instead of the original ATL07 decision tree algorithm, a data-driven neural network (NN) model is used to classify sea ice surface into open water, thin/gray ice, and snow-covered/thick ice. The NN approach shows a better performance in delineating the sea ice surface than the original ATL07 product, especially the separation of thin ice covers. In Chapter 4, the ability of IS2 to capture the thermodynamic and{A0}dynamic ice growth is assessed using in-situ sea ice mass balance array (SIMBA) buoys deployed in the central Arctic. The modal freeboard of the IS2 measurement agrees with the thermodynamic ice growth, but the mean and median IS2 freeboard involve the redistribution of sea ice thickness induced by sea ice deformation. Finally, in Chapter 5, taking advantage of IS2 data for monitoring dynamic sea ice processes, the spatiotemporal variations of pressure ridges in the Ross Sea are investigated. The southeast sector of the Ross Sea is highly deformed by a strong easterly wind and thick ice inflow in 2019-2020, but the transition in the atmospheric condition in 2021-2022 affects the distribution of deformation features. This result emphasizes the importance of atmospheric forcings on the thermodynamic and dynamic sea ice conditions in the Ross Sea.

ISBN: 9798379580254Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Katabatic winds

Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea.
LDR:03673nmm a2200421 4500 001 2396706
005 20240611104922.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379580254
035 $a (MiAaPQ)AAI30419136
035 $a AAI30419136
040 $a MiAaPQ $c MiAaPQ
100 1 $a Koo, Younghyun. $0 (orcid)0000-0001-9235-5009 $3 3766449
245 1 0 $a Using ICESat-2 Satellite Altimeter Data to Improve Understanding of Thermodynamic and Dynamic Sea Ice Characteristics in the Ross Sea.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 202 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Xie, Hongjie.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a The fine spatial resolution (~11 m of footprint) and precision (~2 cm of vertical precision) of NASA's ICESat-2 (IS2) satellite altimeter data allow us to detect sea ice deformation features, such as pressure ridges and leads. Moreover, the thermodynamic and dynamic sea ice processes can be investigated by monitoring sea ice deformation features for a large area over a regular period. However, the IS2 sea ice products (ATL07/ATL10) have critical problems in further accurate monitoring of sea ice: (1) spatial sparsity, (2) dark lead misclassification, and (3) incapability of thin/gray ice detection. In this study, I address these issues and improve the monitoring of sea ice freeboard in the Ross Sea using IS2 data. In Chapter 2, a geostatistical approach, kriging, is employed to address the spatial sparsity issue and interpolate the freeboard values where the IS2 tracks are not directly available. The freeboard maps retrieved from the kriging method exhibit the weekly distribution of sea ice freeboard and persistent polynya areas in the Ross Sea. In Chapter 3, instead of the original ATL07 decision tree algorithm, a data-driven neural network (NN) model is used to classify sea ice surface into open water, thin/gray ice, and snow-covered/thick ice. The NN approach shows a better performance in delineating the sea ice surface than the original ATL07 product, especially the separation of thin ice covers. In Chapter 4, the ability of IS2 to capture the thermodynamic and{A0}dynamic ice growth is assessed using in-situ sea ice mass balance array (SIMBA) buoys deployed in the central Arctic. The modal freeboard of the IS2 measurement agrees with the thermodynamic ice growth, but the mean and median IS2 freeboard involve the redistribution of sea ice thickness induced by sea ice deformation. Finally, in Chapter 5, taking advantage of IS2 data for monitoring dynamic sea ice processes, the spatiotemporal variations of pressure ridges in the Ross Sea are investigated. The southeast sector of the Ross Sea is highly deformed by a strong easterly wind and thick ice inflow in 2019-2020, but the transition in the atmospheric condition in 2021-2022 affects the distribution of deformation features. This result emphasizes the importance of atmospheric forcings on the thermodynamic and dynamic sea ice conditions in the Ross Sea.
590 $a School code: 1283.
650 4 $a Remote sensing. $3 535394
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Geographic information science. $3 3432445
650 4 $a Aerospace engineering. $3 1002622
653 $a Katabatic winds
653 $a Polynyas
653 $a Pressure ridges
653 $a Sea ice leads
653 $a Southern Ocean
653 $a Ross Sea
690 $a 0799
690 $a 0404
690 $a 0370
690 $a 0538
710 2 $a The University of Texas at San Antonio. $b Civil & Environmental Engineering. $3 1023684
773 0 $t Dissertations Abstracts International $g 84-11B.
790 $a 1283
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419136