This manuscript reports the extensive comparison of the REAM chemical transport model (CTM) simulations with the NOx and NO_y observations acquired during the DISCOVER-AQ 2011 over the Baltimore and Washington-DC area. The observations include the data from surface monitors, PANDORA, P3 aircraft, ACAM, and satellites OMI and GOME-2. The model results with two spatial resolutions, 36 km and 4 km are compared in order to elucidate the impact of the resolutions on the model NOx and NOy simulations. Differences between the model and observations are discussed in details and causes for the discrepancies are suggested.

The manuscript reflects the extensive works dealing with almost all available data sets to evaluate NO₂ measurements and CTM results over the Baltimore and Washington-DC area for July 2011. I appreciate the efforts the authors made for this study. The manuscript will be more valuable if quality of presentation and interpretation of the results are enhanced.

The main focus of the paper seems to be the comparison of the model simulations with the 36 km and 4 km resolution and advocate the use of 36 km in the end. I think the authors should focus more on the analysis of 4 km resolution results and causes for the similarities and discrepancies with various observations. The emissions at 36 km resolution are simply accumulations of the emissions at 4 km. It is not important to compare the emissions at the two resolutions and judge which one is better. The authors have the best spatial resolution of emission inventory data and the model simulations at the comparable scale (4 km). If the model overestimates the NOx, NOy observations at one height or vertically column integrated, that simply means the model emissions are overestimated. For the pollution hot spots in the domain, the model values are higher than the observations (judging from the ACAM data). This may be about the spatial location error in the NEI as the authors jumped to the conclusions, but it is more probable that the uncertainties in the emission factors (or activities) over populated urban or roads as represented as MOVES caused the problem. Section 3.7 should be deleted or rewritten. This section is confusing and misleading.

Except for Figure 10, the model results and observations were not analyzed at the measurement sites. The plots are all averages for large domains and many sites. As the ACAM data demonstrate, there are heterogenous distributions of NO₂ at fine scales. This is important. The model results should be compared at each site of PANDORA and P3 spiral locations.

Figure 2 to 5 (and Figure 9) are about the surface monitor data and interpretation. There are many other interesting, important data sets from the DISCOVER-AQ campaign, which is discussed in short compared to the surface routine monitors. The interpretation of nighttime PBL height (or PBLH as in the model output name) may be right, or may be wrong. As authors mentioned in the manuscript, this may be due to overestimation of nighttime emissions, which can not be ruled out. People do not know much about nighttime PBL height and nighttime emissions. PBLH in the model output is not simply PBL height during nighttime. The nighttime PBL height from YSU scheme is sometimes recalculated based on many other nocturnal PBL height definition. Thus Figure 4 may need to be carefully revised or explain limitation of this analysis. Figure 7 is too busy. It is difficult see the details. More expansion of analysis in Figure 7 or a summary in Table would be helpful. A plot comparing satellite NO₂ spatial distributions and more presentation and discussions on the GMI, TM4, REAM as a priori for the retrieval would be useful. Authors frequently use the figures in Supplementary Material. It is difficult to read the manuscript with many supplementary figures. Because there are important plots in the supplementary, I suggest to move some of the plots in the supplementary to the main manuscript. For example, Figure S1, S2, S17, S21, S22, S23 and discussions about them would be useful. Differences  between the model and ACAM NO2, differences between two ACAM NO2 retrievals, and differences between weekdays and weekends are interesting and can have important implications for emissions and model assessments.  Figure S12, S13, and S19 or one of them can be also shown in the main text. For S19, one-to-one comparison of the model simulations at 36 km and 4km resolution would be more useful. It is difficult to understand the purpose of Figure 8.  

Regarding WRF model options, I am wondering why Single Moment 3 Class microphysics scheme is used. This scheme is for warm clouds. There are Grell ensemble or other Grell cumulus parameterization options that were widely tested in CTM groups.  Because the REAM model is an offline model, the performance of model at 4 km resolution may not be caused by original WRF physics, but by the integrator between the WRF and REAM. The performance of model at 12 km was not discussed, but on/off of cumulus parameterization option at this resolution may be another factor to be tested. For the analysis like Figure 6, the model temperature (potential temperature), moisture (specific humidity), U, and V also need to be analyzed with observations, particularly for afternoon.

One minor point is frequent use of red/green combinations in the plots, which is not ideal.

If the manuscript is revised following the comments above, I think publication can be reconsidered.

General comments:

The manuscript by Li et al. presents an important evaluation research work of NO₂ diurnal variation using observations and modelled results from DISCOVER-AQ 2011. The research topic is important and interesting to atmospheric modelling and observation communities. The approach used is comprehensive. Some of the findings (e.g., potential spatial distribution bias in emission inventory, potential bias in ground-based remote sensing instruments) in this work are important for not just modelling groups but also observation groups. But, the presentation of this work should be improved. I would recommend publishing this work if the following concerns and comments can be addressed.

Specific comments:

L87-88. Many previous works were not properly cited. As I know, various research work has been done to convert Pandora NO₂ VCD to TVCD or surface values to study diurnal variations. The authors should update relevant knowledge on these. E.g., Kollonige et al., 2017; Spinei et al., 2014; Zhao et al., 2019. I believe some of the results in this work could be compared with previous findings and may cast some light on the research topic.

L151-176. These detailed discussions of the wind-filed and precipitations should not be done here, as the reader does not know anything about your trace gas simulation results/discrepancy yet. Such detailed discussions (the author used six figures in total, Figs. S2-S7) of potential causes should be included in a separate discussion section.

L203 and L213. 36-km REAM profiles were used to calculate AMFs for both OMI and GOME-2A. Are these new AMFs have higher or lower (or comparable) resolution compared to the original AMFs used in the satellite data products? Please provide a brief description of how the model output has been smoothed or interpolated to OMI and GOME-2A grids.

L328-353. I saw at least three names for Kzz modelling, and I do have difficulty understanding which one is which. After reading this section back and forth several times, I think two Kzz modellings were used, i.e., Kzz-WRF and Kzz-modified. But, I am not sure if this Kzz-WRF is the same as Kzz-YSU. I can understand the logic of why the authors want to modify Kzz for nighttime, but please improve the descriptions to make it easier for a reader to absorb your idea.  

L347-351. Some justifications for the selected parameters are missing. A sensitivity test or correlation studies are needed to justify this 5 m s^-2_. The idea of a magic number is not impressive. It is difficult to justify the selection with Figure 4, which shows even the modified results still have large discrepancy compare to observations.

L364-369. I am worried that the ground observations from various sites should not be studied as a single group. Different local emissions patterns should be addressed. E.g., do all 11 NO₂ sites show the same concentration peak values at 5:00-6:00 LT? Do we see any differences between rural and urban sites?

L388-393. The general impression from Figure 5 is the REAM-4km shows a higher bias than REAM-36km compared to observations. But, this might be misleading. For example, if one looks at Figure 5b from 00:00 to 5:00 LT, the green line shows a better agreement with observations. Please provide some comments on this. The study sites should be grouped into at least two categories, e.g., rural and urban.

L435-442 and Fig. S14. I guess the authors want to show the Pandora TVCD should be corrected; otherwise, the results could be biased low due to a missing surface layer. I agree with the assumption, but it needs to be studied carefully (Fig. S14 shows some indication but not good enough). Fig. S14a shows that for some sites (e.g., SERC), one can expect Pandora to miss up to 20% of NO₂ columns. However, this is not reflected by Fig. S14b at all. If this 20% difference is true, it can be verified relatively easier than other sites. Could you plot Fig. S14b for each Pandora site separately?

L469-488. The findings here are critical for the research community to understand the discrepancy between aircraft, ground-based in situ, ground-based remote sensing, and models. The synthetic aircraft TVCDs have better agreement with REAM especially for 15:00 to 17:00 LT. The agreements between REAM and aircraft profiles (Figure 6) are very nice. So, for me, it looks like Pandora TVCDs are the one that has a major low bias. But, Figure 5 also shows that the REAM has a large positive bias compared to ground-based in situ observations from 15:00 to 17:00 LT (especially for REAM-4km). Can authors conclude if Pandora TVCDs are not accurate in this period? These results may affect the claim of accuracy of Pandora NO2 VCD is 2.7×10¹⁵ molecules cm^-2 in L218. Also, from Fig. S9, it is clear that the observed diurnal variations at different sites could be very different. This matched with the large error bars on the REAM modelled results in Fig. 7. But why Pandora TVCDs from 11 sites show very stable results (small error bars) in Figure 7? The current explanations are not good enough to convince me. Besides understanding the model resolutions, this could be another highlight of this research work. So, I would suggest the authors provide more investigation, explanations, or discussions.

Technical corrections:

L194. Please modify the description of estimated uncertainty. “molecules cm^-2 + 25%” does not make sense.

L217-218. The description of the precision of Pandora NO₂ VCD is not correct. In Herman et al. 2009, the 0.01 DU (or 2.7×10¹⁵ molecules cm^-2) precision is for slant column (not VCD). For Pandora NO₂ VCD, the estimated precision is about 0.02 DU (e.g., Zhao et al., 2020).

L288-294. The scale ratios look consistent between ECO and C42. The one that needs extra caution is CY42 (Thermo Model 421I-Y). But, if the Thermo Model 42I-Y NOy analyzer measurements are not used in this study at all (see L1175-1176), there is no need to include such detailed discussions (it will only confuse the reader). Or, at least, this information should be moved to supplement. I would suggest authors move other figures such as Fig. S1 to here, which should be more important (for the reader to understand the model scales/grids and locations of observations used in this study).

L377. Figure 6 is used before Figure 5. Please swap the order of the figures.

Figure 8. Please use different symbols for  >400m and <400m lines. Also, the caption said there are three bins, but I did not see proper labels for the “400m – 3.63 km”. Are those >400m lines represents “400m – 3.63km” results? Please make sure the legends match with the caption.

Figure 10. Description of the purple circles on panels a-c is needed.

Fig. S1 should be modified. The symbols for different observations jams together and very difficult to see. One should use other means to show instruments at a single site, e.g., a pie chart.

Reference

Kollonige, D. E., Thompson, A. M., Josipovic, M., Tzortziou, M., Beukes, J. P., Burger, R., Martins, D. K., Zyl, P. G. van, Vakkari, V. and Laakso, L.: OMI Satellite and Ground-Based Pandora Observations and Their Application to Surface NO₂ Estimations at Terrestrial and Marine Sites, J. Geophys. Res., 123(2), 1441–1459, https://doi.org/10.1002/2017JD026518, 2017.

Spinei, E., Cede, A., Swartz, W. H., Herman, J. and Mount, G. H.: The use of NO₂ absorption cross section temperature sensitivity to derive NO₂ profile temperature and stratospheric–tropospheric column partitioning from visible direct-sun DOAS measurements, Atmospheric Measurement Techniques, 7(12), 4299–4316, https://doi.org/10.5194/amt-7-4299-2014, 2014.

Zhao, X., Griffin, D., Fioletov, V., McLinden, C., Davies, J., Ogyu, A., Lee, S. C., Lupu, A., Moran, M. D., Cede, A., Tiefengraber, M. and Müller, M.: Retrieval of total column and surface NO₂ from Pandora zenith-sky measurements, Atmos. Chem. Phys., 19(16), 10619–10642, https://doi.org/10.5194/acp-19-10619-2019, 2019.

Zhao, X., Griffin, D., Fioletov, V., McLinden, C., Cede, A., Tiefengraber, M., Müller, M., Bognar, K., Strong, K., Boersma, F., Eskes, H., Davies, J., Ogyu, A. and Lee, S. C.: Assessment of the quality of TROPOMI high-spatial-resolution NO₂ data products in the Greater Toronto Area, Atmos. Meas. Tech., 13(4), 2131–2159, https://doi.org/10.5194/amt-13-2131-2020, 2020.