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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-3445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, 99354, USA
Yun Qian
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, 99354, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, 99354, USA
Yang Zhang
Department of Civil and Environmental Engineering, Northeastern
University, Boston, Massachusetts, 02115, USA
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Total article views: 5,435 (including HTML, PDF, and XML)
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Cited
17 citations as recorded by crossref.
- Current model capabilities and future predictive pathways of wildfires under a changing climate C. Whaley et al. https://doi.org/10.1038/s43247-026-04013-w
- A Fortran–Python interface for integrating machine learning parameterization into earth system models T. Zhang et al. https://doi.org/10.5194/gmd-18-1917-2025
- Burning questions on wildfire Y. Qian https://doi.org/10.1126/science.aea7430
- Projected Global Mortality and Economic Burden from Wildfire Smoke under Climate Change W. Yu et al. https://doi.org/10.1021/EHP.6c00085
- Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America M. Velásquez-García et al. https://doi.org/10.5194/bg-23-1341-2026
- Improving extreme Fire Radiative Power forecasting with Gaussian loss-based transformer and LSTM models Z. Sun et al. https://doi.org/10.1080/01431161.2026.2749622
- Evaluation of global fire simulations in CMIP6 Earth system models F. Li et al. https://doi.org/10.5194/gmd-17-8751-2024
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. https://doi.org/10.1029/2023JD039154
- A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin S. Wu et al. https://doi.org/10.1021/acsestwater.3c00478
- Recent Advances in Wildland Fire Smoke Dynamics Research in the United States Y. Liu et al. https://doi.org/10.3390/atmos16111221
- Regional climate change: consensus, discrepancies, and ways forward T. Shaw et al. https://doi.org/10.3389/fclim.2024.1391634
- Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate X. Chen et al. https://doi.org/10.1029/2023JD039136
- Machine Learning for predicting climate change in the environment: Review B. Fernández-Baldeón et al. https://doi.org/10.56294/sctconf2023465
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
- Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US S. Wang et al. https://doi.org/10.1029/2022EF003086
- Wildfire smoke exposure and mortality burden in the USA under climate change M. Qiu et al. https://doi.org/10.1038/s41586-025-09611-w
- Radiative impact of record-breaking wildfires from integrated ground-based data E. Kassianov et al. https://doi.org/10.1038/s41598-025-85103-1
17 citations as recorded by crossref.
- Current model capabilities and future predictive pathways of wildfires under a changing climate C. Whaley et al. https://doi.org/10.1038/s43247-026-04013-w
- A Fortran–Python interface for integrating machine learning parameterization into earth system models T. Zhang et al. https://doi.org/10.5194/gmd-18-1917-2025
- Burning questions on wildfire Y. Qian https://doi.org/10.1126/science.aea7430
- Projected Global Mortality and Economic Burden from Wildfire Smoke under Climate Change W. Yu et al. https://doi.org/10.1021/EHP.6c00085
- Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America M. Velásquez-García et al. https://doi.org/10.5194/bg-23-1341-2026
- Improving extreme Fire Radiative Power forecasting with Gaussian loss-based transformer and LSTM models Z. Sun et al. https://doi.org/10.1080/01431161.2026.2749622
- Evaluation of global fire simulations in CMIP6 Earth system models F. Li et al. https://doi.org/10.5194/gmd-17-8751-2024
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. https://doi.org/10.1029/2023JD039154
- A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin S. Wu et al. https://doi.org/10.1021/acsestwater.3c00478
- Recent Advances in Wildland Fire Smoke Dynamics Research in the United States Y. Liu et al. https://doi.org/10.3390/atmos16111221
- Regional climate change: consensus, discrepancies, and ways forward T. Shaw et al. https://doi.org/10.3389/fclim.2024.1391634
- Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate X. Chen et al. https://doi.org/10.1029/2023JD039136
- Machine Learning for predicting climate change in the environment: Review B. Fernández-Baldeón et al. https://doi.org/10.56294/sctconf2023465
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
- Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US S. Wang et al. https://doi.org/10.1029/2022EF003086
- Wildfire smoke exposure and mortality burden in the USA under climate change M. Qiu et al. https://doi.org/10.1038/s41586-025-09611-w
- Radiative impact of record-breaking wildfires from integrated ground-based data E. Kassianov et al. https://doi.org/10.1038/s41598-025-85103-1
Saved (final revised paper)
Latest update: 31 Oct 2026
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.
This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire...
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