the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Dec 2023
Pyrogenic HONO seen from space: insights from global IASI observations
Abstract. Nitrous acid (HONO) is a key atmospheric component, acting as a major source of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, understanding its spatial and temporal variability remains a significant challenge. Recent TROPOMI/S5P UV-Vis measurements of fresh fire plumes shed light on the impact of global pyrogenic HONO emissions. Here, we leverage IASI/Metop's global infrared satellite measurements, complementing midday TROPOMI observations with morning and evening overpasses, to detect and retrieve pyrogenic HONO in 2007–2023. Employing a sensitive detection method, we identify HONO enhancements within concentrated fire plumes worldwide. Most detections are in the North Hemisphere mid and high latitudes, where intense wildfires and high injection heights favour HONO detection. IASI's nighttime measurements yield tenfold more HONO detections than daytime, emphasizing HONO's extended lifetime in the absence of photolysis during the night. The annual detection count increases by at least 3–4 times throughout the IASI time series, mirroring the recent surge in intense wildfires at these latitudes. Additionally, we employ a neural network-based algorithm for retrieving pyrogenic HONO total columns from IASI and compare them with TROPOMI in the same fire plumes. The results demonstrate TROPOMI's efficacy in capturing HONO enhancements in smaller fire plumes and in proximity to fire sources, while IASI's morning and evening overpasses enable HONO measurements further downwind, highlighting the survival of HONO or its secondary formation along long-range transport in smoke plumes.
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RC1: 'Comment on egusphere-2023-2707', Anonymous Referee #1, 10 Jan 2024
The paper by Franco et al. presents pyrogenic nitrous acid (HONO) detection and total column quantification based on satellite observations from IASI on Metop since 2007. The detection method is based on the hyperspectral range index to identify spectra with observable HONO signature. Two spectral regions are investigated (820-890 cm-1 and 1210-1305 cm-1) to detect HONO in fire plumes and it is shown the 1210-1305 cm-1 band is the most sensitive because less affected by interfering species. An additional filter combining the HONO detection with ammonia (NH3) and ethylene (C2H4) detection, also emitted by fires, is proposed to limit false detections of pyrogenic HONO in IASI spectra. The paper provides an analysis of the pyrogenic HONO detections in terms of spatial and temporal distributions from the entire archive of IASI A, B, and C instruments and compares the results with the TROPOMI HONO product available at the end of the period and to MODIS fire products. The IASI HONO detection is the most reliable for the mid and high latitudes of both hemispheres. The reasons of the low detection rate in the tropics are discussed. The paper highlights the increase of pyrogenic HONO detection during the last five years in agreement with the increase of wildfire activities. Finally, the paper proposes an estimation of HONO total columns using an artificial neural network architecture, already applied to other low-absorbing species retrieved from IASI. A tentative of comparison with TROPOMI HONO columns estimation is provided for two case studies, and the limitations of such comparisons are discussed.
The paper provides an important step forward in satellite remote sensing of wildfire impacts on atmospheric composition, focusing on a challenging species, HONO. Nitrous acid is a key atmospheric species as a major source of hydroxyl radical, but important gaps remain in our knowledge of its global budget due to the difficulties to measure it at large scales. The new HONO IASI product presented in the paper complements the recent TROPOMI HONO product, especially with nighttime observations to probe globally HONO in pyrogenic plumes, and the long time series available is of great value to improve knowledge on HONO atmospheric budget. The paper is well structured, written and documented. The results are well argued, and limitations of the HONO product mostly discussed. The paper is suitable for publication in ACP after some clarifications.
Main comments:
My main comments concern the description of the detection and retrieval approaches, which needs some clarifications. These approaches have been already described in other papers for other species and the authors referred to these papers, but some details are missing to understand the specificities to HONO detection and retrieval.
Concerning the detection method (section 2.1), it is not clear which assumptions are made on the atmospheric concentrations of HONO and interfering species to calculate the Jacobian K. We understand later in the text (line 376) that the US 1976 standard atmosphere is used as well as a HONO profile including a narrow layer (line 375) but without much more details. Is the HONO profile considered as a gaussian profile, as described in section 4? What is the impact of the shape and the height of the HONO peak on the detection based on the HRI? These details should be provided earlier in the text (section 2.1) and completed. The authors should also specify how they calculate the generalized covariance matrix Sy. Is this matrix diagonal, for example?
Similarly, the set-up for the retrieval of HONO in section 4.1 is confused. It is not clear for me what are the inputs and the outputs of the NN. Indeed, it is not described how the parameters related to the abundance and vertical distribution are chosen to feed the NN and if they are retrieved at the output of the NN or if it is just the total columns. Line 517 it seems these parameters are variable from one pixel to another in the inputs (on which basis/assumption these variations are chosen? Is a model used?) but line 523, it seems that σ is fixed to 350m for all the pixels without any discussion of this choice, whereas a range of possible variations from 100m to 3km is mentioned in Eq 8. This should be clarified.
Specific comments:
- Line 113: the authors could specify that the spectra in brightness temperature are considered for the HRI calculation.
- Lines 170-171: trace gases and surface emissivity are mentioned but what about aerosol spectral signatures? Do they interfere with HONO signature?
- Lines 210-213: as up to now, detection of HONO with IASI was done only in Australian fires, it would have been interesting to provide an example of the magnitude of the HONO spectral contributions also in an Australian fire case to see how higher it is compared to other fires.
- Section 2.4: The authors have demonstrated that the 820-890 cm-1 region is less sensitive to HONO detection compared to the 1210-1305 cm-1 region. What is the interest to provide the filter for this spectral region, which is not leveraged after? What would be the interest to use both regions for detection and retrievals?
- Figure 5: IASI and TROPOMI are not compared for the same period (IASI since 2007, TROPOMI since 2018). Are the results different if the same period is used for the comparison?
- Line 375: the authors should specify what they mean by narrow layer (see main comments).
- Line 445 and around when the authors discuss the difference between the early time series and the more recent ones: at the beginning of IASI-A lifetime, only one pixel out of two was distributed. In the time series presented here, is it still the case or all the pixels are considered? If not, this may impact the number of detections for this period.
- Line477-492: It is not clear for me to what conclusion for HONO the analysis of ethylene leads.
- Line 518: What do the authors mean by “actual retrieval”? Is it the retrieval based on a radiative transfer model, or is it the ANNI retrieval?
- Section 4.3, discussion of the Woosley Fire: the authors elaborate on the time evolution and the spatial extension of the plumes using IASI and TROPOMI observations. I would be more cautious about what we can draw from these comparisons, given the assumptions and the large uncertainties in the observations and the differences in terms of sensitivity of the two instruments.
Technical corrections:
- Figures quality when printed is low.
- Figure 3: the colorbars should be reversed. C2H4 colorbars is below NH3 plots and vice versa.
- Line 481: change “The IASI retrieval” to “The IASI detection”.
Citation: https://doi.org/10.5194/egusphere-2023-2707-RC1 - AC1: 'Reply on RC1', Bruno Franco, 28 Feb 2024
-
RC2: 'Comment on egusphere-2023-2707', Anonymous Referee #2, 02 Feb 2024
In their manuscript, "Pyrogenic HONO seen from space: insights from global IASI observations," the authors present a new retrieval of HONO from biomass burning using the infrared sounder IASI. Franco et al. identify two absorption bands (820-890 cm-1 and 1210-1305 cm-1) for optimal use in the calculation of the hyperspectral range index (HRI), used in the detection of HONO. The authors demonstrate that the 1210-1305 cm-1 absorption band has the least interferences with ammonia (NH3) and dust in the Middle East. They reduce false detections of pyrogenic HONO by also using co-located NH3 and C2H4. Instead of focusing solely on individual fires as previous studies have done, the authors also present a global HONO detection climatology of fires from 2007 to 2023, analyzing HONO detection location, aerosol height, MODIS FRP count and mean FRP, and detection time of year. These parameters are compared against the TROPOMI UV-Vis HONO retrieval and a discussion about the differences in retrieval quality between the two instruments is provided. IASI is capable of outputting trace gas retrievals during the daytime and nighttime, and the authors use the number of IASI HONO detections and case studies to form hypotheses about the HONO diel cycle. Finally, Franco et al. use a neural network, ANNI, to compute HONO vertical column densities. These are qualitatively compared to TROPOMI.
This paper demonstrates a new and groundbreaking retrieval of pyrogenic HONO using infrared sensing that allows for long-term analysis of HONO and sub-diurnal observations without the aerosol sensitivity that the UV-Vis retrieval has. HONO has been shown to be critical in the early stages of fire plumes as it is the majority source of OH due to its short photolytic lifetime. How varying sunlight impacts HONO lifetime within smoke is a crucial topic of interest given its oxidative importance. This retrieval is also prime for use on new geostationary satellite (MTG-IRS) which will make observations has frequent as every half hour. This paper is thorough, well-structured, and written well. The limitations of each HONO retrieval method (IASI IR vs TROPOMI UV-Vis) are discussed and reiterated throughout the manuscript and was very appreciated by this reviewer. I recommend this paper for publication subject to minor revisions.
General Comments:
I thought the authors did a great job presenting their work, arguments, and conclusions to the reader. The following are a few general comments:
The authors ultimately decided to show many of their results using the 1210-1305 cm-1 window due to the limitations shown for the 820-890 cm-1 window (NH3 interference and Middle East dust interference) and I was curious why this window was included for earlier discussion in the paper. The conditions defined in lines 252-253 made me wonder why this is defined since it has a demonstrated surface emissivity anomaly and NH3 interference. Line 285 also mentions using the two different HONO HRIs right after the authors state that only the 1210-1305 cm-1 window would be used from then on. It would make for a cleaner story if the 820-890 cm-1 window was not included at all and just summarized as a window that was up for consideration.
Many of the figures referenced in the paper do not have the subpanels added to direct the reader to the specific panel the authors are referring to. For example, line 192 talks about the retrieved spectrum, which is in Figure 2a, line 203 talks about NH3 interferences, which is in Figure 2b, line 258 talks about non simultaneous detection of NH3 and C2H4, which can be easily seen in Figures 3c, d, and h, etc. These cues make it easier for the reader to follow the authors’ line of thought.
Specific Comments:
- Figure 1: I suggest adding wind vectors over each panel to demonstrate where the HONO is being transported from. I think it would be especially helpful in Figures 1c and 1d, the October 15, 2017 9:30 PM overpass. Additionally, it would aid the reader if a general HRI = 4 contour was included in each subfigure. Finally, I suggest making the red cross either a square or a thick outline. My initial thought was that a red cross meant bad data, ignore this.
- Line 195: Could the authors include an appendix figure covering the entire spectral interval? It makes me curious why 1210-1250 cm-1 was omitted.
- Section 2.3: I was confused what the author meant by the term “channel,” as in the channel contribution to HRI. I first thought it was for each individual compound, but then Figure 2b talks about the channel contribution to HRI for both HONO and NH3 referencing the same line. It may have been defined in a referenced paper or it may be common terminology in this specific field. However, I would like more context or a clear definition.
- Figure 2: Could the whitened Jacobian for NH3 also be added to c? Would this further demonstrate a lack of interference from NH3?
- Line 232: Can these localized surface emissivity anomalies be accounted for in the retrieval or are they time-varying properties?
- Figure 3: Is there any reason why it looks like there are more than two populations in the HONO vs NH3 colored by C2H4 figures (Figures 3a, c, e, and g)? It is especially apparent in 3a, where a linear trend of yellow dots seems distinct from the pink/orange dots of higher NH3, but lower HONO. Could the authors discuss this? It may be relevant to the biomass type and region comment in lines 257-258.
- Line 289-290: Is there any other data that would explain these random detections over remote ocean?
- Line 316: The authors later go into an analysis of why there are detections lacking in the global tropics (sect 3.2), but I was curious what is the effect of clouds on the retrievals? I know that the authors included cloud and cloud-free spectra, but there wasn’t a comment on how clouds affect the retrieval. I asked myself if the ITCZ had any effect? This may be showing my lack of IR expertise.
- Figure 5: I found it interesting that IASI HONO saw detections off the coast of Australia, over water. How does this retrieval do over water? There was a large discussion of the thermal contrast affecting the retrieval. Were these plumes detected because they were over water or because the strength of the fires had lofted them high enough for key detection?
- Equation 6: I’m not sure why this equation is presented, if only to demonstrate height dependence on the detection threshold. When the authors talk about the NN VCD calculation in section 4, I was wondering if this equation was used or not.
- Line 376: This line made me wonder what the height limitations are on HONO detection with IASI.
- Line 377: The authors spend a lot of time explaining their constraints for IASI HONO detection, but not a lot of time is spent explaining the constraints on valid TROPOMI HONO detections, which are frequently compared against.
- Line 390: Why is volcanic ash included?
- Line 395: Did your analysis lead to the conclusion that layers between 2 and 4 km contained many false detections? I’d like to see this in an appendix figure or have a paper cited.
- Figure 8: Please add latitude labels.
- Figure 9: Are the MODIS fire detections in comparison to TROPOMI from Aqua or Terra, or both? Should TROPOMI be compared to the closest overpass? Or what about in comparison to VIIRS which has a very close overpass time to TROPOMI?
- Figure 10: I may have missed it, but is there a reference comparing the differences between IASI-A, -B, and -C? I am seeing slight differences between the three instruments, though the authors show similar annual counts of HONO detections in Figure 11.
- Line 440: What is a confirmed HONO detection?
- Line 452: Has anyone else cited this drop in the year 2022? Not for HONO detections but perhaps in sum FRP? Very interesting.
- Section 3.4: One paragraph starts by saying “To rule out potential other reasons for the observed am/pm difference…” but then the section doesn’t have an overall concluding statement about HONO’s diel cycle, just about the effect of photochemistry on C2H4
Technical Corrections:
Line 377: “An HONO detection” to “A HONO detection.”
Citation: https://doi.org/10.5194/egusphere-2023-2707-RC2 - AC2: 'Reply on RC2', Bruno Franco, 28 Feb 2024
Status: closed
-
RC1: 'Comment on egusphere-2023-2707', Anonymous Referee #1, 10 Jan 2024
The paper by Franco et al. presents pyrogenic nitrous acid (HONO) detection and total column quantification based on satellite observations from IASI on Metop since 2007. The detection method is based on the hyperspectral range index to identify spectra with observable HONO signature. Two spectral regions are investigated (820-890 cm-1 and 1210-1305 cm-1) to detect HONO in fire plumes and it is shown the 1210-1305 cm-1 band is the most sensitive because less affected by interfering species. An additional filter combining the HONO detection with ammonia (NH3) and ethylene (C2H4) detection, also emitted by fires, is proposed to limit false detections of pyrogenic HONO in IASI spectra. The paper provides an analysis of the pyrogenic HONO detections in terms of spatial and temporal distributions from the entire archive of IASI A, B, and C instruments and compares the results with the TROPOMI HONO product available at the end of the period and to MODIS fire products. The IASI HONO detection is the most reliable for the mid and high latitudes of both hemispheres. The reasons of the low detection rate in the tropics are discussed. The paper highlights the increase of pyrogenic HONO detection during the last five years in agreement with the increase of wildfire activities. Finally, the paper proposes an estimation of HONO total columns using an artificial neural network architecture, already applied to other low-absorbing species retrieved from IASI. A tentative of comparison with TROPOMI HONO columns estimation is provided for two case studies, and the limitations of such comparisons are discussed.
The paper provides an important step forward in satellite remote sensing of wildfire impacts on atmospheric composition, focusing on a challenging species, HONO. Nitrous acid is a key atmospheric species as a major source of hydroxyl radical, but important gaps remain in our knowledge of its global budget due to the difficulties to measure it at large scales. The new HONO IASI product presented in the paper complements the recent TROPOMI HONO product, especially with nighttime observations to probe globally HONO in pyrogenic plumes, and the long time series available is of great value to improve knowledge on HONO atmospheric budget. The paper is well structured, written and documented. The results are well argued, and limitations of the HONO product mostly discussed. The paper is suitable for publication in ACP after some clarifications.
Main comments:
My main comments concern the description of the detection and retrieval approaches, which needs some clarifications. These approaches have been already described in other papers for other species and the authors referred to these papers, but some details are missing to understand the specificities to HONO detection and retrieval.
Concerning the detection method (section 2.1), it is not clear which assumptions are made on the atmospheric concentrations of HONO and interfering species to calculate the Jacobian K. We understand later in the text (line 376) that the US 1976 standard atmosphere is used as well as a HONO profile including a narrow layer (line 375) but without much more details. Is the HONO profile considered as a gaussian profile, as described in section 4? What is the impact of the shape and the height of the HONO peak on the detection based on the HRI? These details should be provided earlier in the text (section 2.1) and completed. The authors should also specify how they calculate the generalized covariance matrix Sy. Is this matrix diagonal, for example?
Similarly, the set-up for the retrieval of HONO in section 4.1 is confused. It is not clear for me what are the inputs and the outputs of the NN. Indeed, it is not described how the parameters related to the abundance and vertical distribution are chosen to feed the NN and if they are retrieved at the output of the NN or if it is just the total columns. Line 517 it seems these parameters are variable from one pixel to another in the inputs (on which basis/assumption these variations are chosen? Is a model used?) but line 523, it seems that σ is fixed to 350m for all the pixels without any discussion of this choice, whereas a range of possible variations from 100m to 3km is mentioned in Eq 8. This should be clarified.
Specific comments:
- Line 113: the authors could specify that the spectra in brightness temperature are considered for the HRI calculation.
- Lines 170-171: trace gases and surface emissivity are mentioned but what about aerosol spectral signatures? Do they interfere with HONO signature?
- Lines 210-213: as up to now, detection of HONO with IASI was done only in Australian fires, it would have been interesting to provide an example of the magnitude of the HONO spectral contributions also in an Australian fire case to see how higher it is compared to other fires.
- Section 2.4: The authors have demonstrated that the 820-890 cm-1 region is less sensitive to HONO detection compared to the 1210-1305 cm-1 region. What is the interest to provide the filter for this spectral region, which is not leveraged after? What would be the interest to use both regions for detection and retrievals?
- Figure 5: IASI and TROPOMI are not compared for the same period (IASI since 2007, TROPOMI since 2018). Are the results different if the same period is used for the comparison?
- Line 375: the authors should specify what they mean by narrow layer (see main comments).
- Line 445 and around when the authors discuss the difference between the early time series and the more recent ones: at the beginning of IASI-A lifetime, only one pixel out of two was distributed. In the time series presented here, is it still the case or all the pixels are considered? If not, this may impact the number of detections for this period.
- Line477-492: It is not clear for me to what conclusion for HONO the analysis of ethylene leads.
- Line 518: What do the authors mean by “actual retrieval”? Is it the retrieval based on a radiative transfer model, or is it the ANNI retrieval?
- Section 4.3, discussion of the Woosley Fire: the authors elaborate on the time evolution and the spatial extension of the plumes using IASI and TROPOMI observations. I would be more cautious about what we can draw from these comparisons, given the assumptions and the large uncertainties in the observations and the differences in terms of sensitivity of the two instruments.
Technical corrections:
- Figures quality when printed is low.
- Figure 3: the colorbars should be reversed. C2H4 colorbars is below NH3 plots and vice versa.
- Line 481: change “The IASI retrieval” to “The IASI detection”.
Citation: https://doi.org/10.5194/egusphere-2023-2707-RC1 - AC1: 'Reply on RC1', Bruno Franco, 28 Feb 2024
-
RC2: 'Comment on egusphere-2023-2707', Anonymous Referee #2, 02 Feb 2024
In their manuscript, "Pyrogenic HONO seen from space: insights from global IASI observations," the authors present a new retrieval of HONO from biomass burning using the infrared sounder IASI. Franco et al. identify two absorption bands (820-890 cm-1 and 1210-1305 cm-1) for optimal use in the calculation of the hyperspectral range index (HRI), used in the detection of HONO. The authors demonstrate that the 1210-1305 cm-1 absorption band has the least interferences with ammonia (NH3) and dust in the Middle East. They reduce false detections of pyrogenic HONO by also using co-located NH3 and C2H4. Instead of focusing solely on individual fires as previous studies have done, the authors also present a global HONO detection climatology of fires from 2007 to 2023, analyzing HONO detection location, aerosol height, MODIS FRP count and mean FRP, and detection time of year. These parameters are compared against the TROPOMI UV-Vis HONO retrieval and a discussion about the differences in retrieval quality between the two instruments is provided. IASI is capable of outputting trace gas retrievals during the daytime and nighttime, and the authors use the number of IASI HONO detections and case studies to form hypotheses about the HONO diel cycle. Finally, Franco et al. use a neural network, ANNI, to compute HONO vertical column densities. These are qualitatively compared to TROPOMI.
This paper demonstrates a new and groundbreaking retrieval of pyrogenic HONO using infrared sensing that allows for long-term analysis of HONO and sub-diurnal observations without the aerosol sensitivity that the UV-Vis retrieval has. HONO has been shown to be critical in the early stages of fire plumes as it is the majority source of OH due to its short photolytic lifetime. How varying sunlight impacts HONO lifetime within smoke is a crucial topic of interest given its oxidative importance. This retrieval is also prime for use on new geostationary satellite (MTG-IRS) which will make observations has frequent as every half hour. This paper is thorough, well-structured, and written well. The limitations of each HONO retrieval method (IASI IR vs TROPOMI UV-Vis) are discussed and reiterated throughout the manuscript and was very appreciated by this reviewer. I recommend this paper for publication subject to minor revisions.
General Comments:
I thought the authors did a great job presenting their work, arguments, and conclusions to the reader. The following are a few general comments:
The authors ultimately decided to show many of their results using the 1210-1305 cm-1 window due to the limitations shown for the 820-890 cm-1 window (NH3 interference and Middle East dust interference) and I was curious why this window was included for earlier discussion in the paper. The conditions defined in lines 252-253 made me wonder why this is defined since it has a demonstrated surface emissivity anomaly and NH3 interference. Line 285 also mentions using the two different HONO HRIs right after the authors state that only the 1210-1305 cm-1 window would be used from then on. It would make for a cleaner story if the 820-890 cm-1 window was not included at all and just summarized as a window that was up for consideration.
Many of the figures referenced in the paper do not have the subpanels added to direct the reader to the specific panel the authors are referring to. For example, line 192 talks about the retrieved spectrum, which is in Figure 2a, line 203 talks about NH3 interferences, which is in Figure 2b, line 258 talks about non simultaneous detection of NH3 and C2H4, which can be easily seen in Figures 3c, d, and h, etc. These cues make it easier for the reader to follow the authors’ line of thought.
Specific Comments:
- Figure 1: I suggest adding wind vectors over each panel to demonstrate where the HONO is being transported from. I think it would be especially helpful in Figures 1c and 1d, the October 15, 2017 9:30 PM overpass. Additionally, it would aid the reader if a general HRI = 4 contour was included in each subfigure. Finally, I suggest making the red cross either a square or a thick outline. My initial thought was that a red cross meant bad data, ignore this.
- Line 195: Could the authors include an appendix figure covering the entire spectral interval? It makes me curious why 1210-1250 cm-1 was omitted.
- Section 2.3: I was confused what the author meant by the term “channel,” as in the channel contribution to HRI. I first thought it was for each individual compound, but then Figure 2b talks about the channel contribution to HRI for both HONO and NH3 referencing the same line. It may have been defined in a referenced paper or it may be common terminology in this specific field. However, I would like more context or a clear definition.
- Figure 2: Could the whitened Jacobian for NH3 also be added to c? Would this further demonstrate a lack of interference from NH3?
- Line 232: Can these localized surface emissivity anomalies be accounted for in the retrieval or are they time-varying properties?
- Figure 3: Is there any reason why it looks like there are more than two populations in the HONO vs NH3 colored by C2H4 figures (Figures 3a, c, e, and g)? It is especially apparent in 3a, where a linear trend of yellow dots seems distinct from the pink/orange dots of higher NH3, but lower HONO. Could the authors discuss this? It may be relevant to the biomass type and region comment in lines 257-258.
- Line 289-290: Is there any other data that would explain these random detections over remote ocean?
- Line 316: The authors later go into an analysis of why there are detections lacking in the global tropics (sect 3.2), but I was curious what is the effect of clouds on the retrievals? I know that the authors included cloud and cloud-free spectra, but there wasn’t a comment on how clouds affect the retrieval. I asked myself if the ITCZ had any effect? This may be showing my lack of IR expertise.
- Figure 5: I found it interesting that IASI HONO saw detections off the coast of Australia, over water. How does this retrieval do over water? There was a large discussion of the thermal contrast affecting the retrieval. Were these plumes detected because they were over water or because the strength of the fires had lofted them high enough for key detection?
- Equation 6: I’m not sure why this equation is presented, if only to demonstrate height dependence on the detection threshold. When the authors talk about the NN VCD calculation in section 4, I was wondering if this equation was used or not.
- Line 376: This line made me wonder what the height limitations are on HONO detection with IASI.
- Line 377: The authors spend a lot of time explaining their constraints for IASI HONO detection, but not a lot of time is spent explaining the constraints on valid TROPOMI HONO detections, which are frequently compared against.
- Line 390: Why is volcanic ash included?
- Line 395: Did your analysis lead to the conclusion that layers between 2 and 4 km contained many false detections? I’d like to see this in an appendix figure or have a paper cited.
- Figure 8: Please add latitude labels.
- Figure 9: Are the MODIS fire detections in comparison to TROPOMI from Aqua or Terra, or both? Should TROPOMI be compared to the closest overpass? Or what about in comparison to VIIRS which has a very close overpass time to TROPOMI?
- Figure 10: I may have missed it, but is there a reference comparing the differences between IASI-A, -B, and -C? I am seeing slight differences between the three instruments, though the authors show similar annual counts of HONO detections in Figure 11.
- Line 440: What is a confirmed HONO detection?
- Line 452: Has anyone else cited this drop in the year 2022? Not for HONO detections but perhaps in sum FRP? Very interesting.
- Section 3.4: One paragraph starts by saying “To rule out potential other reasons for the observed am/pm difference…” but then the section doesn’t have an overall concluding statement about HONO’s diel cycle, just about the effect of photochemistry on C2H4
Technical Corrections:
Line 377: “An HONO detection” to “A HONO detection.”
Citation: https://doi.org/10.5194/egusphere-2023-2707-RC2 - AC2: 'Reply on RC2', Bruno Franco, 28 Feb 2024
Data sets
The IASI/Metop pyrogenic HONO product B. Franco, L. Clarisse, and P. Coheur https://doi.org/10.5281/zenodo.10141825
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