Effect of Data Homogenization for Trend and Seasonal Signal Detection

Tanır Kayıkçı E., Zengin Kazancı S., Tornatore V.

COST Action ES1206 Final Workhop, Zuid-Holland, Netherlands, 21 - 23 February 2017, pp.26-27

  • Publication Type: Conference Paper / Summary Text
  • City: Zuid-Holland
  • Country: Netherlands
  • Page Numbers: pp.26-27
  • Karadeniz Technical University Affiliated: Yes


Time series of any climatic variables, e.g., precipitation, may be affected by non-climatic factors, which may alter the climatic signals.  Homogeneous climatic time series are important for climate change studies. Inhomogeneities caused by station relocation and instrumentation changes, etc., can mask actual trends and seasonal variability in time series.


This work presents a methodology for outlier detection and series homogenization for ZTD and IWV estimates at various IGS stations in the period 1995-2010 from IGS-Repro1. Standard Normal Homogeneity Test, Mann-Kendall test, Mann-Kendall Rank Correlation test, Pettitt-test and Multiple Linear Regression were applied. The aim is to detect significant inhomogeneities occur in different IGS stations and investigate reasons of inhomogenities.


Data underwent an outlier detection procedure and outliers were discarded; afterwards daily series were elaborated. Afterwards, homogenization tests were applied to ZTD and IWV series, and trend and seasonal variations were calculated. The same calculations were carried out also for the original series to investigate the impact of the homogenization for trend and seasonal signal detection.