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

Related authors

Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques
Sally S.-C. Wang and Yuxuan Wang
Atmos. Chem. Phys., 20, 11065–11087, https://doi.org/10.5194/acp-20-11065-2020,https://doi.org/10.5194/acp-20-11065-2020, 2020
Short summary

Related subject area

Subject: Biosphere Interactions | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Why do inverse models disagree? A case study with two European CO2 inversions
Saqr Munassar, Guillaume Monteil, Marko Scholze, Ute Karstens, Christian Rödenbeck, Frank-Thomas Koch, Kai U. Totsche, and Christoph Gerbig
Atmos. Chem. Phys., 23, 2813–2828, https://doi.org/10.5194/acp-23-2813-2023,https://doi.org/10.5194/acp-23-2813-2023, 2023
Short summary
Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system
Saqr Munassar, Christian Rödenbeck, Frank-Thomas Koch, Kai U. Totsche, Michał Gałkowski, Sophia Walther, and Christoph Gerbig
Atmos. Chem. Phys., 22, 7875–7892, https://doi.org/10.5194/acp-22-7875-2022,https://doi.org/10.5194/acp-22-7875-2022, 2022
Short summary
Distinguishing the impacts of natural and anthropogenic aerosols on global gross primary productivity through diffuse fertilization effect
Hao Zhou, Xu Yue, Yadong Lei, Chenguang Tian, Jun Zhu, Yimian Ma, Yang Cao, Xixi Yin, and Zhiding Zhang
Atmos. Chem. Phys., 22, 693–709, https://doi.org/10.5194/acp-22-693-2022,https://doi.org/10.5194/acp-22-693-2022, 2022
Short summary
Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 21, 17453–17494, https://doi.org/10.5194/acp-21-17453-2021,https://doi.org/10.5194/acp-21-17453-2021, 2021
Short summary
CO2-equivalence metrics for surface albedo change based on the radiative forcing concept: a critical review
Ryan M. Bright and Marianne T. Lund
Atmos. Chem. Phys., 21, 9887–9907, https://doi.org/10.5194/acp-21-9887-2021,https://doi.org/10.5194/acp-21-9887-2021, 2021
Short summary
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.
Altmetrics
Final-revised paper
Preprint