DOI: 10.1002/joc.5801
论文题名: Accounting for missing data in monthly temperature series: Testing rule-of-thumb omission of months with missing values
作者: Anderson C.I. ; Gough W.A.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 13 起始页码: 4990
结束页码: 5002
语种: 英语
英文关键词: 3/5 rule
; data gaps
; missing values
; monthly series
; temperature
Scopus关键词: Climatology
; Earth atmosphere
; Temperature
; 3/5 rule
; Correlation analysis
; Data gap
; Linear Interpolation
; Missing at randoms
; Missing values
; monthly series
; Statistical robustness
; Autocorrelation
; correlation
; diurnal variation
; interpolation
; research work
; temperature anomaly
; trend analysis
英文摘要: The “3/5 rule” is a commonly used rule-of-thumb for dealing with missing data when calculating monthly climate normals. The rule states that any month that is missing more than three consecutive daily values, or more than five daily values in total, should not be included in calculated monthly climate normals. We quantify the impact of missing data in a given year–month for between 1 and 25 missing values. As such, we describe the error the “3/5 rule” (and a related rule that we have dubbed the “4/10 rule”) permits. We tested the statistical robustness of these rules using observed temperature data from a temperate station and a tropical station. We show that, for observed data, the “3/5 rule” permits an average of between 0.06 and 0.07 standard deviations of error in the calculated monthly mean (ɛ) when three consecutive or five random values are missing. For its part, the “4/10 rule” permits a maximum ɛ of between 0.07 and 0.09 when four consecutive values are missing, or up to 0.10 when 10 random values are missing. The proportional impact of missing values was similar across variables. We performed a correlation analysis and show that each additional missing value from a year–month of data increases ɛ by between 0.008 and 0.018 for up to 19 missing values. There is a significant relationship between the lag-1 autocorrelation of a year–month, and ɛ. ɛ can be reduced by simple linear interpolation when values are missing at random and the year–month exhibits lag-1 autocorrelation. Overall, we find that the application of any “rule-of-thumb” should be based on the particular characteristics of the source data and the goals of the research project. © 2018 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116748
Appears in Collections: 气候减缓与适应
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作者单位: Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON, Canada
Recommended Citation:
Anderson C.I.,Gough W.A.. Accounting for missing data in monthly temperature series: Testing rule-of-thumb omission of months with missing values[J]. International Journal of Climatology,2018-01-01,38(13)