کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
486761 | 703390 | 2012 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting It Using Information Theory
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting It Using Information Theory Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting It Using Information Theory](/preview/png/486761.png)
چکیده انگلیسی
A framework for visualizing and detecting climate variability and change based on time-dependent probability density functions (PDFs) is developed. A set of information-theoretic statistics based on the Shannon Entropy and the Kullback-Leibler Divergence (KLD) are defined to assess PDF complexity and temporal variability. The KLD based measures quantify the representativeness of a thirty year sampling window of a larger climatic record, how well a long sample can predict a smaller sample's PDF, and how well one thirty year sample matches a similar sample shifted in time. These techniques are applied the the Central England Temperature record, the longest continuous meteorological observational record.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 9, 2012, Pages 917-926
Journal: Procedia Computer Science - Volume 9, 2012, Pages 917-926