Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/ascmo-11-133-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-11-133-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
05 Sep 2025
 | 05 Sep 2025

A new data-standardization procedure for comprehensive outlier detection in correlated meteorological sensor data

Natalie D. Benschop, Temesgen Zewotir, Rajen N. Naidoo, and Delia North

Viewed

Total article views: 392 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
358 29 5 392 17 26
  • HTML: 358
  • PDF: 29
  • XML: 5
  • Total: 392
  • BibTeX: 17
  • EndNote: 26
Views and downloads (calculated since 05 Sep 2025)
Cumulative views and downloads (calculated since 05 Sep 2025)

Viewed (geographical distribution)

Total article views: 392 (including HTML, PDF, and XML) Thereof 392 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Oct 2025
Download
Short summary
Meteorological data recorded at high frequency by automatic sensors are often marred by multiple forms of error. Existing validation techniques, in isolation, are sub-optimal for such error-prone data. We propose a new data-standardization procedure for the validation of strongly correlated series which commonly arise in meteorology. We show the procedure to be more comprehensive in the simultaneous detection of solitary spikes, shifts in series means, and irregular diurnal patterns.
Read more
Share