the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Sep 2024
Remote Sensing Estimates of Time-Resolved HONO and NO2 Emission Rates and Lifetimes in Wildfires
Abstract. Quantification of wildfire emissions is essential for comprehending and simulating the effects of wildfires on atmospheric chemical composition. Sub-orbital measurements of vertical column nitrous acid (HONO) and nitrogen dioxide (NO2) were made during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign using the GeoCAPE Airborne Simulator (GCAS) instrument aboard the NASA ER-2 aircraft. Emission rates and lifetimes of HONO and NO2 from the Sheridan Fire were estimated by fitting exponentially modified Gaussians (EMGs) to line densities, a technique previously used to estimate urban and point source NO2 emissions. As the EMG approach does not capture temporal changes in emissions and lifetimes due to time-varying fire behavior, we developed a Monte Carlo implementation of the Python Editable Chemical Atmospheric Numeric Solver (PECANS) model that includes diurnal fire radiative power (FRP) behavior. We assess the validity of a range of emission rate and lifetime combinations for both HONO and NO2 as the fire evolves by comparing the resulting line density predictions to the observations. We find that our method results in emissions that are lower than top-down biomass burning emissions inventories and higher than bottom-up inventories. Our approach is applicable to interpreting time-resolved remotely sensed measurements of atmospheric trace gases such as those now becoming available with instruments aboard geo-stationary satellites such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO) and the Geostationary Environment Monitoring Spectrometer (GEMS) instruments.
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Status: final response (author comments only)
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RC1: 'Comment on amt-2024-158', Anonymous Referee #1, 15 Oct 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2024-158/amt-2024-158-RC1-supplement.pdf
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RC2: 'Comment on amt-2024-158', Anonymous Referee #2, 18 Oct 2024
The authors evaluate the emission rate and lifetime of NO2 and HONO during the FIREX-AQ campaign. The authors use aircraft and GOES-16 and -17 observations to do the analysis. The following comments are provided to clarify the context for the reader’s better understanding. Among the comments, one of the significant issues is the suitability of using GOES-17 observations in the study due to mechanical issues of ABI. Without evidence or references that GOES-17 FRP products are safely usable, erroneous signals from malfunctioning GOES-17 ABI may affect the study's results.
Major comments
- In lines 104 – 115, the authors did not describe how to calculate the AMF. If it is the author’s intent not to describe in detail, references are needed to give readers a proper way to calculate the values. In addition, in line 110, please indicate the bidirectional reflectance distribution functions are used to describe the surface. The optical properties of aerosols also weren’t mentioned in the context.
- Section 2.3 describes FRP derived from GOES-16 and -17 observations. However, as mentioned in https://www.goes-r.gov/users/GOES-17-ABI-Performance.html, GOES-17 ABI has cooling system issues. Please provide evidence or references indicating GOES-17 ABI observations can be used to obtain FRP. In addition, in line 149, why choose 5 %?
- The Gaussian emission rate is shown in Fig. B1 and Figure 5 shows the results. These two figures can be put together, and the constant and step-change emission rates can be added to Figure 5 for better understanding.
- Figure 6 and lines 375 – 393 describe the results based on R2 Since the authors compared the model and satellite observations, would it be better to compare results using biases, standard deviations, or root-mean-square errors?
Minor comments
- In lines 35 – 36, references are needed to the statement, “As the intensity of fires and burned area from fires are predicted to increase in the United States in the future.”
- In lines 42 – 44 and 154 - 155, the way of citing references is confusing. Please put matched references after the name of the relevant inventories.
- In lines 76 – 78 and after, please provide references to GCAS and FIREX-AQ, and also provide references to TROPOMI and TEMPO instruments in line 93.
- Please provide the full name of TROPOMI in line 93, as it is its first time mentioned in the main context.
- Please provide references to VLIDORT in line 108.
- Please provide references to Carnegie-Ames-Stanford Approach model in line 157.
- Please check the units “-m” in lines 156 and 161.
- In lines 166 – 167, 180 – 181, and 229 - 232, please provide units to the variables.
- Please provide references to SAPRC99, MOZART, and GOES-Chem in lines 171 - 172.
- In line 214, please define OMI.
- Please add the direction of North as Fig 1 to Fig 2 and indicate the flight track number for the corresponding Fig 2 Figures.
- In line 264, please describe what enhanced NO2 is.
Citation: https://doi.org/10.5194/amt-2024-158-RC2 -
RC3: 'Comment on amt-2024-158', Anonymous Referee #3, 23 Oct 2024
This manuscript does a very nice job evaluating NO2 and HONO from the Sheridan fire plume during FIREX-AQ using measurements from GCAS. This manuscript is worthy of publication after some minor revisions.
Major comments:
It would be helpful if the authors could provide a better motivation for Section 3.3. I am a bit confused. To be clear, I fully understand why there’s a need to better understand uncertainties in the EMG method and/or develop a better method, but it’s unclear to me exactly how Section 3.3 is doing this. Additional clarification is needed.
Errors in the wind speed and direction are a likely contributor to the errors in the EMG fit. From Figure 1 it seems that the rotated plume is not perfectly horizontal, partially due to meandering winds. This will lead to an artificial shortening of the derived lifetimes. In my more detailed comments, I suggested ways to look at this uncertainty.
Also, some more discussion on the aerosol effects on the GCAS retrieval would be helpful. See the Introduction of Cooper et al., 2019 (https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GL083673) for a great discussion on this. This is especially relevant for Section 3.4 when comparing to inventories. I am guessing that the shielding effect (aerosols “shielding” the sunlight from NO2 at lower layers) is more prominent than the enhancement effect (aerosols “enhancing” sensitivity to the GCAS measurement above the wildfire plume layer), and therefore would consistently underestimate NO2 and HONO emissions, but this is merely a hypothesis.
More detailed comments are below:
Line 87. What was specific altitude of the ER-2? It’s relevant because it’s important to know how much of the upper troposphere and lower stratosphere is being observed by GCAS. Also can you briefly comment here or elsewhere in the manuscript regarding how much NO2 and HONO is in the lower stratosphere that could have been observed by GCAS?
Line 133. What is the 588 mb pressure level referring to: the plane altitude or the mean plume height or both or neither? Can you clarify? And if it’s not the mean plume height, what was the mean plume height? And did this vary by time of day?
Figure 1. As a sensitivity study, it may worthwhile to artificially rotate the plume even further, maybe another 20 degree clockwise to see what effect that has. It does seem that the plume meandered a bit, from southwesterly flow initially to more westerly flow 20 km downwind. I don’t know if it’s possible, but I would recommend rotating the 0-10 km section of the plume differently than the 10-60 km section of the plume. I think this may be a partial cause as to why the lifetimes from the EMG method are underestimated as discussed in Lines 345-350.
Lines 246 - 249. I’m not following the 3rd and 4th configurations. Is the motivation of this to better quantify the pulsing nature of wildfires? Or something else? One or two more sentences motivating these four sensitivity studies could be very helpful.
Figure 3a. Can you clarify x-axis to be 16-Aug 16Z, etc. It’s not intuitive that the second number is the hour.
Lines 319 - 351. I am not fully following Figure 5 and the associated analysis. I think more detail motivating this section is needed near Line 319. I think you are trying to better understand how the EMG method could better capture the pulsing nature of wildfires, but that’s not explicitly said, so maybe I am misinterpreting. Also see comments regarding Lines 246 - 249 which are contributing to my confusion. The times (200 s 5000 s, etc.) are also confusing to me. Are you referring to how much time it takes on your local computer to do the analysis? Can you better define what these mean?
Line 436. A discussion of any potential systematic biases of your MC diurnal 1-D method is warranted here. In particular, the effects of aerosols on the retrievals should be discussed. I am guessing the aerosols could be causing an underestimate in the GCAS retrieval, which would in turn cause derived emissions to be too small. Something to think about and include as discussion.
Citation: https://doi.org/10.5194/amt-2024-158-RC3
Data sets
FIREX-AQ 2019 The FIREX-AQ Science Team https://www-air.larc.nasa.gov/cgi-bin/ArcView/firexaq
ERA5 hourly data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview
Global Fire Emissions Database (GFED) 4s G. R. van der Werf, J. T. Randerson, L. Giglio, T. T. van Leeuwen, Y. Chen, B. M. Rogers, M. Mu, M. J. E. van Marle, D. C. Morton, G. J. Collatz, R. J. Yokelson, and P. S. Kasibhatla https://www.geo.vu.nl/~gwerf/GFED/GFED4/
Fire Inventory from NCAR version 2 Fire Emission C. Wiedinmyer and L. Emmons https://rda.ucar.edu/datasets/d312009/
Quick Fire Emissions Dataset v2.5 A. Darmenov and A. da Silva https://portal.nccs.nasa.gov/datashare/iesa/aerosol/emissions/QFED/v2.5r1/0.25/QFED/
CAMS global biomass burning emissions based on fire radiative power (GFAS) J. W. Kaiser, A. Heil, M. O. Andrae, A. Benedetti, N. Chubarova, L. Jones, J.-J. Morcrette, M. Razinger, M. G. Schultz, M. Suttie, and G. R. van der Werf https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-fire-emissions-gfas?tab=overview
Model code and software
v0.1.1-custom: Fredrickson et al. 2024 J. Laughner, Q. Zhu, and C. Fredrickson https://doi.org/10.5281/zenodo.13621859
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