globalchange  > 科学计划与规划
DOI: 10.1002/2016GL070852
论文题名:
Artificial bias typically neglected in comparisons of uncertain atmospheric data
作者: Pitkänen M.R.A.; Mikkonen S.; Lehtinen K.E.J.; Lipponen A.; Arola A.
刊名: Geophysical Research Letters
ISSN: 0094-8611
EISSN: 1944-8342
出版年: 2016
卷: 43, 期:18
起始页码: 10003
结束页码: 10011
语种: 英语
英文关键词: ordinary least squares ; regression dilution ; regression to the mean ; statistical bias
Scopus关键词: Regression analysis ; Atmospheric data ; Atmospheric science ; Data comparisons ; Data uncertainty ; Ordinary least squares ; Regression to the means ; Satellite retrieval ; Statistical bias ; Uncertainty analysis
英文摘要: Publications in atmospheric sciences typically neglect biases caused by regression dilution (bias of the ordinary least squares line fitting) and regression to the mean (RTM) in comparisons of uncertain data. We use synthetic observations mimicking real atmospheric data to demonstrate how the biases arise from random data uncertainties of measurements, model output, or satellite retrieval products. Further, we provide examples of typical methods of data comparisons that have a tendency to pronounce the biases. The results show, that data uncertainties can significantly bias data comparisons due to regression dilution and RTM, a fact that is known in statistics but disregarded in atmospheric sciences. Thus, we argue that often these biases are widely regarded as measurement or modeling errors, for instance, while they in fact are artificial. It is essential that atmospheric and geoscience communities become aware of and consider these features in research. ©2016. The Authors.
URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990213412&doi=10.1002%2f2016GL070852&partnerID=40&md5=4d31e319a8e69b24e158125e94b622ae
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9574
Appears in Collections:科学计划与规划
气候变化与战略

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作者单位: Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute, Kuopio, Finland

Recommended Citation:
Pitkänen M.R.A.,Mikkonen S.,Lehtinen K.E.J.,et al. Artificial bias typically neglected in comparisons of uncertain atmospheric data[J]. Geophysical Research Letters,2016-01-01,43(18).
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