Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 4, issue 1/2
Articles | Volume 4, issue 1/2
https://doi.org/10.5194/ascmo-4-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-4-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 4, issue 1/2
22 Aug 2018
 | 22 Aug 2018

The joint influence of break and noise variance on the break detection capability in time series homogenization

Ralf Lindau and Victor Karel Christiaan Venema

Viewed

Total article views: 3,360 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,732 537 91 3,360 228 107 92
  • HTML: 2,732
  • PDF: 537
  • XML: 91
  • Total: 3,360
  • Supplement: 228
  • BibTeX: 107
  • EndNote: 92
Views and downloads (calculated since 22 Aug 2018)
Cumulative views and downloads (calculated since 22 Aug 2018)

Viewed (geographical distribution)

Total article views: 2,917 (including HTML, PDF, and XML) Thereof 2,891 with geography defined and 26 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Read more