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An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data./
作者:	Brunson, Brianna G.
面頁冊數:	1 online resource (55 pages)
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29214214click for full text (PQDT)
ISBN:	9798834008606

An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data.
Brunson, Brianna G.

An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data. - 1 online resource (55 pages)

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--College of Charleston, 2022.

Includes bibliographical references

Assumptions regarding raindrop arrival statistics influence strategies for measuring and analyzing rainfall within the atmospheric science community. Because many instruments automatically aggregate rainfall data on particular time scales, it is standard to subdivide drop-by-drop arrival data into 1 or 5-minute intervals for parameter estimation. Under the assumption of homogeneous rain, there is little reason to be concerned about this aggregation. However, research suggests that rainfall has a complex spatiotemporal structure even on small scales. To explore the potential effects of assumptions made during this subdivision, we will compare the results of two dynamic partitionings of drop-by-drop rainfall arrival data. We will then discuss their possible uses and advantages over standard five-minute data aggregation. This research prompts questions about the measured variability of rainfall and how to define a rain event.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798834008606Subjects--Topical Terms:

517247
Statistics.
Index Terms--Genre/Form:

542853
Electronic books.

An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data.
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245 1 3 $a An Investigation of Dynamic Partitioning Methods for the Analysis of Raindrop Data.
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500 $a Source: Masters Abstracts International, Volume: 83-12.
500 $a Advisor: Kai, Bo.
502 $a Thesis (M.S.)--College of Charleston, 2022.
504 $a Includes bibliographical references
520 $a Assumptions regarding raindrop arrival statistics influence strategies for measuring and analyzing rainfall within the atmospheric science community. Because many instruments automatically aggregate rainfall data on particular time scales, it is standard to subdivide drop-by-drop arrival data into 1 or 5-minute intervals for parameter estimation. Under the assumption of homogeneous rain, there is little reason to be concerned about this aggregation. However, research suggests that rainfall has a complex spatiotemporal structure even on small scales. To explore the potential effects of assumptions made during this subdivision, we will compare the results of two dynamic partitionings of drop-by-drop rainfall arrival data. We will then discuss their possible uses and advantages over standard five-minute data aggregation. This research prompts questions about the measured variability of rainfall and how to define a rain event.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Applied mathematics. $3 2122814
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
690 $a 0725
690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a College of Charleston. $b Mathematical Sciences. $3 3693552
773 0 $t Masters Abstracts International $g 83-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29214214 $z click for full text (PQDT)