Detecting discontinuities in time series of upper air data: Demonstration of an adaptive filter technique

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The issue of global climate change due to increased anthropogenic emissions of greenhouse gases in the atmosphere has gained considerable attention and importance. Climate change studies require the interpretation of weather data collected in numerous locations and/or over the span of several decades. Unfortunately, these data contain biases caused by changes in instruments and data acquisition procedures. It is essential that biases are identified and/or removed before these data can be used confidently in the context of climate change research. The purpose of this paper is to illustrate the use of an adaptive moving average filter and compare it with ... continued below

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29 p.

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Zurbenko, I.; Chen, J. & Rao, S.T. November 1, 1997.

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Description

The issue of global climate change due to increased anthropogenic emissions of greenhouse gases in the atmosphere has gained considerable attention and importance. Climate change studies require the interpretation of weather data collected in numerous locations and/or over the span of several decades. Unfortunately, these data contain biases caused by changes in instruments and data acquisition procedures. It is essential that biases are identified and/or removed before these data can be used confidently in the context of climate change research. The purpose of this paper is to illustrate the use of an adaptive moving average filter and compare it with traditional parametric methods. The advantage of the adaptive filter over traditional parametric methods is that it is less effected by seasonal patterns and trends. The filter has been applied to upper air relative humidity and temperature data. Applied to generated data, the filter has a root mean squared error accuracy of about 600 days when locating changes of 0.1 standard deviations and about 20 days for changes of 0.5 standard deviations. In some circumstances, the accuracy of location estimation can be improved through parametric techniques used in conjunction with the adaptive filter.

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29 p.

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OSTI as DE98001242

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  • Other Information: PBD: [1997]

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  • Other: DE98001242
  • Report No.: DOE/ER/61011--T9
  • Grant Number: AI05-90ER61011
  • DOI: 10.2172/548869 | External Link
  • Office of Scientific & Technical Information Report Number: 548869
  • Archival Resource Key: ark:/67531/metadc691664

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  • November 1, 1997

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  • Aug. 14, 2015, 8:43 a.m.

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  • Nov. 4, 2015, 4:16 p.m.

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Zurbenko, I.; Chen, J. & Rao, S.T. Detecting discontinuities in time series of upper air data: Demonstration of an adaptive filter technique, report, November 1, 1997; United States. (digital.library.unt.edu/ark:/67531/metadc691664/: accessed September 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.