Articles | Volume 22, issue 5
Atmos. Chem. Phys., 22, 3445–3468, 2022
https://doi.org/10.5194/acp-22-3445-2022

Special issue: The role of fire in the Earth system: understanding interactions...

Atmos. Chem. Phys., 22, 3445–3468, 2022
https://doi.org/10.5194/acp-22-3445-2022

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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-634', Anonymous Referee #2, 01 Oct 2021
  • RC2: 'Comment on acp-2021-634', Anonymous Referee #1, 12 Nov 2021
  • AC1: 'Responses to Reviewers on "Interpreting machine learning prediction of fire emission and comparison with FireMIP process-based models"', Sally S.-C. Wang, 10 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sally S.-C. Wang on behalf of the Authors (10 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (01 Jan 2022) by Xiaohong Liu
RR by Anonymous Referee #1 (11 Jan 2022)
ED: Publish as is (07 Feb 2022) by Xiaohong Liu
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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.
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