Formulation of parametric uncertainty forecasts  towards operational wildfire smoke assimilation

Vogel, Annika; Ménard, Richard; Abu, James; Chen, Jack

doi:10.5194/npg-33-347-2026

Articles | Volume 33, issue 3

https://doi.org/10.5194/npg-33-347-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-33-347-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 33, issue 3

Research article

|

14 Jul 2026

Research article |

| 14 Jul 2026

Formulation of parametric uncertainty forecasts towards operational wildfire smoke assimilation

Annika Vogel, Richard Ménard, James Abu, and Jack Chen

Data sets

Output of GEM-MACHv3.2.1.1_firePKF and OAv2 suite: Parametric uncertainty forecasts for wildfire case study on 6 June 2023 Annika Vogel et al. https://doi.org/10.5281/zenodo.21298068

Model code and software

GEM-MACHv3.2.1.1_firePKF Environment and Canada https://doi.org/10.5281/zenodo.18306098

Short summary

Recent wildfire activity in Canada has been rising the public demand for fast, yet accurate operational air quality forecasts along with related forecast uncertainties. This study explores the potential of an efficient process-based approach to estimate forecast uncertainties of operational air quality models. Our results show its potential for understanding how uncertainties of operational forecast evolve over time and an improved use of sparse observation signals at remote wildfire regions.