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On the Development of a New Analysis...

Damadeo, Robert P.

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On the Development of a New Analysis for the Derivation of Long-Term Trends in Stratospheric Ozone from Sparsely-Sampled Data Sets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the Development of a New Analysis for the Derivation of Long-Term Trends in Stratospheric Ozone from Sparsely-Sampled Data Sets./
作者:	Damadeo, Robert P.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	148 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:	Dissertations Abstracts International80-05B.
標題:	Atmospheric sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10978448
ISBN:	9780438653931

On the Development of a New Analysis for the Derivation of Long-Term Trends in Stratospheric Ozone from Sparsely-Sampled Data Sets.
Damadeo, Robert P.

On the Development of a New Analysis for the Derivation of Long-Term Trends in Stratospheric Ozone from Sparsely-Sampled Data Sets. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 148 p.

Source: Dissertations Abstracts International, Volume: 80-05, Section: B.

Thesis (Ph.D.)--Hampton University, 2018.

This item must not be added to any third party search indexes.

The ozone layer is a critical component of Earth's atmosphere and monitoring its health has been a cornerstone of international research efforts for decades. Having had a substantial negative impact throughout the 20th century, humanity is now concerned with determining if stratospheric ozone is starting to recover. This is accomplished by analyzing decades of data from multiple instruments using regression techniques to derive long-term trends. This dissertation details a new method of regression for sparsely-sampled data sets for use with time series analyses and applies it to multiple solar occultation instruments simultaneously without the need for homogenization. This technique can compensate for the non-uniform temporal, spatial, and diurnal sampling of the instruments and can also be used to account for biases and drifts between instruments. These problems are noted in recent international assessments as being a primary source of uncertainty that clouds the significance of derived trends. Results herein show positive trends of ~2-3%/decade in the upper stratosphere at mid-latitudes in the post-2000 time period and also how sampling biases present in these data sets can create differences in derived "recovery" trends of up to ~1%/decade if not accounted for. Limitations inherent to all techniques (e.g., relative instrument drifts) and their impacts (e.g., trend differences up to ~2%/decade) are also described and a potential path forward towards resolution is presented.

ISBN: 9780438653931Subjects--Topical Terms:

3168354
Atmospheric sciences.

On the Development of a New Analysis for the Derivation of Long-Term Trends in Stratospheric Ozone from Sparsely-Sampled Data Sets.
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520 $a The ozone layer is a critical component of Earth's atmosphere and monitoring its health has been a cornerstone of international research efforts for decades. Having had a substantial negative impact throughout the 20th century, humanity is now concerned with determining if stratospheric ozone is starting to recover. This is accomplished by analyzing decades of data from multiple instruments using regression techniques to derive long-term trends. This dissertation details a new method of regression for sparsely-sampled data sets for use with time series analyses and applies it to multiple solar occultation instruments simultaneously without the need for homogenization. This technique can compensate for the non-uniform temporal, spatial, and diurnal sampling of the instruments and can also be used to account for biases and drifts between instruments. These problems are noted in recent international assessments as being a primary source of uncertainty that clouds the significance of derived trends. Results herein show positive trends of ~2-3%/decade in the upper stratosphere at mid-latitudes in the post-2000 time period and also how sampling biases present in these data sets can create differences in derived "recovery" trends of up to ~1%/decade if not accounted for. Limitations inherent to all techniques (e.g., relative instrument drifts) and their impacts (e.g., trend differences up to ~2%/decade) are also described and a potential path forward towards resolution is presented.
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