the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire
Georg A. Grell
Greg R. Carmichael
Farren L. Herron-Thorpe
Johnathan W. Hair
David M. Giles
Pablo E. Saide
- Final revised paper (published on 29 Sep 2021)
- Supplement to the final revised paper
- Preprint (discussion started on 12 Apr 2021)
- Supplement to the preprint
RC1: 'Comment on acp-2021-223', Anonymous Referee #1, 07 May 2021
AC1: 'Reply on RC1', Xinxin Ye, 21 Jul 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-223/acp-2021-223-AC1-supplement.pdf
- AC1: 'Reply on RC1', Xinxin Ye, 21 Jul 2021
RC2: 'Comment on acp-2021-223', Alan Wei Lun Lim, 16 May 2021
This paper compares fire emission parameters derived from twelve different fire emission models forecasting a 2019 wildfire event in the United States. Parameters are compared among models and observed data. By doing so, the authors aim to derive meaningful insights into the current progress of fire emission models and hence suggest ways to further improve the efficacy of such models. The parameters compared in this paper include biomass burning organic carbon emissions, smoke AOD (magnitude and spatial coverage), surface PM2.5, plume rise height and ratio of smoke AOD and PM2.5; hence covering both physics and chemistry aspect of fire emission modeling. The paper suggests areas which current fire emission models can improve on, which includes methodologies to represent diurnal evolution of fire emissions, improved vertical distribution of emitted pollutants and better representation of plume injection heights.
The models and methodology used are clearly described and the paper is well written. I have only two suggestion and a few minor suggestions/clarifications to make.
Line 877: It would be highly insightful to understand how the type of emission injection method (within PBL, intermediate, deep) affect the model skill in predicting plume rise heights. Certain emission injection method may be more useful for a certain kind of fire plume (fresh, aged, fire characteristics: smoldering, raging fire, etc.) and not others. If we can associate a better emission injection method with a corresponding type of fire plume, we can improve fire modelling skill. Indeed, further investigations in this regard is necessary and will definitely be a good follow up work.
Line 981: I may not be proficient enough in this aspect, so this is just some thoughts. There might be inherent problems using surface smoke PM2.5 to smoke AOD ratio when you have different sAOD filters for different models. For models with small sAOD (denominator), the ratio will tend to be bigger and hence result in larger spread. This is consequentially seen in the large nominal mean bias. For example, ARQI and NAQFC have 0.01 sAOD threshold and consequentially have a very large NMB. HRRR smoke and WISC WRF-Chem have 0.02 sAOD threshold and also have very large NMBs. CAMS have a larger threshold, 0.05, and consequentially have smaller magnitude NMBs. AIRPACT is the exception here. This may affect both the magnitude and spread of the ratio calculated and may lead to unfair comparisons between the models. This may affect the model evaluation.
Line 36: For 2.5 in PM2.5, suggest to be written in subscript.
Line 85: There is one multi-model comparison done by Li, et. al., 2019. Atmos. Chem. Phys., 19, 12545–12567 for many different fire models, which may be worthy to look at.
Line 157: If the forecast system produces more than 1 cycle per day, how is the data treated? Is the data averaged?
Line 220: The style of writing for Section 2.1.6 seems to be slightly different from the rest of the paper. Consider revising.
Line 378: I would like to clarify if the models were in a spun-up condition when model forecasts were extracted to compare with observed data.
Line 446: AERONET is already defined in line 435.
Line 534: It may be insightful to suggest a reason why FRP-driven models results in higher sAOD compared to hotspot driven models.
Line 655: It may be problematic to compare point derived ground-based measurement station data against model grid predictions of smoke PM2.5. Perhaps a small discussion about this issue will be helpful.
Line 660 and a few other places: May want to revise the use of ‘it’s’.
Line 730: Consider revising this sentence.
Figure 5: Spelling of AERONET in the figure and caption.
AC2: 'Reply on RC2', Xinxin Ye, 21 Jul 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-223/acp-2021-223-AC2-supplement.pdf
- AC2: 'Reply on RC2', Xinxin Ye, 21 Jul 2021
Peer review completion
- Full-text XML
The present study highlights the importance of various physical processes in smoke dispersion from wildfire biomass burning. The manuscript is clearly structured, it is well written and presents important analysis regarding the comparison between twelve state-of-the-art atmospheric models during a specific case study in US. Overall, it will be a very useful addition to current literature, and I suggest publication with a few minor comments as shown below:
Solomos, S., V. Amiridis, P. Zanis, E. Gerasopoulos, F.I. Sofiou, T. Herekakis, J. Brioude, A. Stohl, R.A. Kahn, C. Kontoes, Smoke dispersion modeling over complex terrain using high resolution meteorological data and satellite observations – The FireHub platform, Atmospheric Environment, Volume 119, October 2015, Pages 348–361, doi:10.1016/j.atmosenv.2015.08.066, 2015
Solomos S., A. Gialitaki, E. Marinou, E. Proestakis, V. Amiridis, Holger Baars, Mika Compula, Albert Ansmann, Modeling and remote sensing of an indirect Pyro-Cb formation and biomass transport from Portugal wildfires towards Europe, Atmospheric Environment, https://doi.org/10.1016/j.atmosenv.2019.03.009 , 2019
4. Line 320 typo “biome maps”