Technical note: A procedure to clean, decompose, and aggregate time series

Ritter, François

doi:https://doi.org/10.5194/hess-27-349-2023

Articles | Volume 27, issue 2

https://doi.org/10.5194/hess-27-349-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-349-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 2

Technical note

|

18 Jan 2023

Technical note |

| 18 Jan 2023

Technical note: A procedure to clean, decompose, and aggregate time series

François Ritter

Supplement

https://doi.org/10.5194/hess-27-349-2023-supplement

Data sets

Global Historical Climatology Network - Daily (GHCN-Daily), version 3.0 M. J. Menne, I. Durre, B. Korzeniewski, S. McNeill, K. Thomas, X. Yin, S. Anthony, R. Ray, R. S. Vose, B. E. Gleason, and T. G. Houston https://doi.org/10.7289/V5D21VHZ

Single aspirated air temperature, RELEASE-2021 (DP1.00002.001) NEON (National Ecological Observatory Network) https://doi.org/10.48443/2nt3-wj42

Model code and software

fritte2/ctbi_article: ctbi article (v1.0.0) F. Ritter https://doi.org/10.5281/zenodo.7529126

Short summary

This study offers a method to clean time series – data recorded at specific time intervals (hours, months, etc.). It cuts time series into small pieces (called bins) and rejects bins without enough data. Errors in each bin are then flagged with a popular method called the box plot rule, which has been improved in this study. Finally, each bin can be averaged to produce a new time series with less noise, fewer gaps, and fewer errors. This procedure can be generalized to any discipline.