Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 1
Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-41-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-41-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 1
Research article
 | 
04 Jan 2022
Research article |  | 04 Jan 2022

Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Amir H. Souri, Kelly Chance, Kang Sun, Xiong Liu, and Matthew S. Johnson

Viewed

Total article views: 3,245 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,401 777 67 3,245 167 57 55
  • HTML: 2,401
  • PDF: 777
  • XML: 67
  • Total: 3,245
  • Supplement: 167
  • BibTeX: 57
  • EndNote: 55
Views and downloads (calculated since 24 Sep 2021)
Cumulative views and downloads (calculated since 24 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,245 (including HTML, PDF, and XML) Thereof 3,105 with geography defined and 140 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
The central component of satellite and model validation is pointwise measurements. A point is an element of space, whereas satellite (model) pixels represent an averaged area. These two datasets are inherently different. We leveraged some geostatistical tools to transform discrete points to gridded data with quantified uncertainty, comparable to satellite footprint (and response functions). This in part alleviated some complications concerning point–pixel comparisons.
Read more