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: 3,319 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,018 1,077 224 3,319 459 225 325
  • HTML: 2,018
  • PDF: 1,077
  • XML: 224
  • Total: 3,319
  • Supplement: 459
  • BibTeX: 225
  • EndNote: 325
Views and downloads (calculated since 27 Jan 2017)
Cumulative views and downloads (calculated since 27 Jan 2017)

Viewed (geographical distribution)

Total article views: 3,222 (including HTML, PDF, and XML) Thereof 3,216 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 15 Aug 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