Wang et al. aim to enhance the temporal resolution of current biomass burning emission estimates, which are typically provided on a daily to monthly basis. When integrated into chemical transport models, these emission estimates currently rely on fixed diurnal cycles informed by climatological empirical data. The authors introduce a new method that utilizes day-specific diurnal variations derived from geostationary Fire Radiative Power (FRP). They then examine how these improvements in emissions impact simulated ozone levels. Overall, the subject matter aligns with the scope of ACP. The presentation is well-structured and clear, though the resulting improvements are subtle.

Specific comments

(1) Abstract, L21-23: The authors’ new method of incorporating day-specific diurnal variations from geostationary observations, compared to fixed diurnal cycles based on climatological data, appears to yield only subtle improvements. The surface ozone biases are reduced slightly from ‘−1.54 to +9.09 ppbv’ to ‘−1.58 to +9.13 ppbv’. This is particularly noteworthy given that significant changes to ozone levels can be achieved by addressing other uncertainties in wildfire emissions (e.g., injection height, emission budgets, etc.). The diurnal cycles of NOx seem to be reasonably captured by the diurnal scaling factors applied in both the GFED4 and GFAS inventories (Fig. 1), with no significant differences in bias between the new method and the conventional GFED and GFAS approaches (Fig. 3). How does this new method improve model performance or enhance our general understanding of current knowledge?

(2) L229: Unclear how CO relates to other emitted species. By a fixed CO-to-species ratio for all three emission inventories? This is an important point to clarify before interpreting the results in Section 3.1 where intra-inventory NOx emissions are compared.

(3) L300 & L567: The authors may want to clarify the intra-inventory differences in the species budgets for CO, PM, and other species, in addition to the NOx presented. This would help readers better understand the full context of the intra-inventory differences and make sense of the comparisons being made.

(4) Section 3.4: Most global effects are minor, around ± 0.1-1 ppbv in Fig.8.

(5) Conclusion: The new method may be more significant for understanding the hourly variations in atmospheric chemistry that occur throughout the diurnal progression of large wildfires. However, for general wildfire simulations, the improvements in the model are less noticeable.

Technical comments

(1) Title: Define the acronym FRP

(2) Fig.4: Define the acronym REF in the figure caption.

This study tested how diurnal variation of fire emissions in Africa impact tropospheric ozone using GEOS-Chem. The issues below need to be addressed before it can be published.

Line 16: “GFED and GFAS” add full name.

Line 22: “reduce surface ozone biases (−1.54 to +9.09 ppbv vs. −1.58 to +9.13 ppbv)” This change is very small. Is it statistically significant? Otherwise please remove this statement.

The writing needs significant improvement. Below are a few examples:

• Title: “Using Geostationary-Derived Sub-Daily FRP Variability vs. Prescribed Diurnal Cycles” reads awkward. Using FRP Variability for what? Also Geostationary-Derived should be Geostationary-Satellite-Derived. Please revise.
• Line 70-72: repeated statements.
• Line 150: remove a “both”
• Line 296: “We also show emissions in the lower part of the fire seasons” not sure what this means.

Line 82: “Most satellite instruments, operating in sun-synchronous orbits, sample at fixed local times, limiting the temporal resolution of inventories like GFED and GFAS to weekly or monthly scales, with sub-weekly estimates relying on empirical scaling factors” This statement is wrong. GFED has monthly temporal resolution because it is based on monthly MODIS burned area product, not because of the satellite overpass time. Also is GFAS really weekly product? GFAS is FRP based product and it’s hard to imagine it’s a weekly/monthly dataset. Even so, there are widely used global fire emission inventories that are daily such as QFED and FINN. The authors ignored that.

The motivation to study diurnal variation of fire emissions on atmospheric chemistry is valid. However the study overlooked previous efforts on this topic. I suggest revise the introduction to reflect previous efforts in the area. The manuscript also misses important references. Below are a few examples.

• Andela, N., Kaiser, J. W., van der Werf, G. R., & Wooster, M. J. (2015). New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations. Atmospheric Chemistry and Physics, 15, 8831–8846. https://doi.org/10.5194/acp-15-8831-2015
• Li, F., Zhang, X., Roy, D. P., & Kondragunta, S. (2019). Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States. Atmospheric Environment, 211, 274–287. https://doi.org/10.1016/j.atmosenv.2019.05.017
• Tang, W., Emmons, L. K., Buchholz, R. R., Wiedinmyer, C., Schwantes, R. H., He, C., Kumar, R., Pfister, G. G., Worden, H. M., Hornbrook, R. S., Apel, E. C., Tilmes, S., Gaubert, B., Martinez-Alonso, S.-E., Lacey, F., Holmes, C. D., Diskin, G. S., Bourgeois, I., Peischl, J., Ryerson, T. B., Hair, J. W., Weinheimer, A. J., Montzka, D. D., Tyndall, G. S., Campos, T. L., Effects of fire diurnal variation and plume rise on US air quality during FIREX‐AQ and WE‐CAN based on the Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICAv0). Journal of Geophysical Research: Atmospheres, p.e2022JD036650, 2022.
• Freeborn, P. H., Wooster, M. J., Roberts, G., Malamud, B. D., & Xu, W. (2009). Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data. Remote Sensing of Environment, 113, 1700–1711. https://doi.org/10.1016/j.rse.2009.03.013
• Mu, M., Randerson, J. T., van der Werf, G. R., Giglio, L., Kasibhatla, P., Morton, D., et al. (2011). Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model of predictions of carbon monoxide. Journal of Geophysical Research, 116, D24303. https://doi.org/10.1029/2011JD016245

Line 115: remove “high spatial resolution”

Line 454: Perhaps “meteorology” or “transport” is more appropriate here than “atmospheric circulation” as “atmospheric circulation” often refers to large scale motion.

Figure 2: do panels b-e have the same legend as panels f-i?

Satellite retrievals of O3 are subject to relatively large uncertainties compared to satellite CO and NO2 products. That need to be considered when you explain model-satellite discrepancies.

Line 375: It’s surprising there is no AK for OMI tropospheric O3 column. Is this true?

Line 558: Again it is not fair to say “conventional BB emission inventories like GFED and GFAS are typically available at daily-to-monthly temporal resolutions”. Most global fire emission inventories are daily. Some regional ones are hourly, just not over Africa.

The authors need to discuss how uncertainties in plume injection height influence the results, especially for the “global impacts of the diurnal cycle of BB emissions over Africa” part.