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Automated classification of rainfall...

Baldwin, Michael Eugene.

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Automated classification of rainfall systems using statistical characterization.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automated classification of rainfall systems using statistical characterization./
作者:	Baldwin, Michael Eugene.
面頁冊數:	195 p.
附註:	Adviser: Frederick H. Carr.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Geophysics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3085711
ISBN:	9780496335183

Automated classification of rainfall systems using statistical characterization.
Baldwin, Michael Eugene.

Automated classification of rainfall systems using statistical characterization. - 195 p.

Adviser: Frederick H. Carr.

Thesis (Ph.D.)--The University of Oklahoma, 2003.

A general, completely automated procedure for classifying rainfall systems is developed. The technique is flexible and universally applicable, in that any rainfall system can be classified regardless of size, location, time of day or year, degree of organization, etc. The knowledge obtained from previous research was used to develop a relatively straightforward and unique classification system. To test the performance of the method, results were validated against a subjective classification based upon objective criteria. From an independent random sample, the automated classification system accurately placed events into stratiform, linear, and cellular classes 85% of the time.

ISBN: 9780496335183Subjects--Topical Terms:

535228
Geophysics.

Automated classification of rainfall systems using statistical characterization.
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520 $a A general, completely automated procedure for classifying rainfall systems is developed. The technique is flexible and universally applicable, in that any rainfall system can be classified regardless of size, location, time of day or year, degree of organization, etc. The knowledge obtained from previous research was used to develop a relatively straightforward and unique classification system. To test the performance of the method, results were validated against a subjective classification based upon objective criteria. From an independent random sample, the automated classification system accurately placed events into stratiform, linear, and cellular classes 85% of the time.
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