Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 3, issue 1
Articles | Volume 3, issue 1
https://doi.org/10.5194/ascmo-3-1-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-3-1-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 3, issue 1
27 Jan 2017
 | 27 Jan 2017

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

John Tipton, Mevin Hooten, and Simon Goring

Viewed

Total article views: 2,961 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,811 948 202 2,961 437 205 260
  • HTML: 1,811
  • PDF: 948
  • XML: 202
  • Total: 2,961
  • Supplement: 437
  • BibTeX: 205
  • EndNote: 260
Views and downloads (calculated since 27 Jan 2017)
Cumulative views and downloads (calculated since 27 Jan 2017)

Viewed (geographical distribution)

Total article views: 2,882 (including HTML, PDF, and XML) Thereof 2,876 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Feb 2026
Download
Short summary
We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Read more
Share