Articles | Volume 22, issue 5
https://doi.org/10.5194/acp-22-3445-2022
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, Yun Qian, L. Ruby Leung, and Yang Zhang

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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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