Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 22, issue 5
Articles | Volume 22, issue 5
https://doi.org/10.5194/acp-22-3445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-22-3445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 5
Research article
 | 
15 Mar 2022
Research article |  | 15 Mar 2022

Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models

Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang

Viewed

Total article views: 3,715 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,892 771 52 3,715 156 39 46
  • HTML: 2,892
  • PDF: 771
  • XML: 52
  • Total: 3,715
  • Supplement: 156
  • BibTeX: 39
  • EndNote: 46
Views and downloads (calculated since 26 Aug 2021)
Cumulative views and downloads (calculated since 26 Aug 2021)

Viewed (geographical distribution)

Total article views: 3,715 (including HTML, PDF, and XML) Thereof 3,845 with geography defined and -130 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Read more
Special issue
The role of fire in the Earth system: understanding interactions...
Altmetrics
Final-revised paper
Preprint