Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM_2.5 Predictions over Europe

Round 1

Reviewer 1 Report

The authors present a method assimilating MODIS AOD into Chimère model to improve AOD and PM_2.5 estimates over the Europe. The authors are very sophisticated in their use of the different models and reference to related works. The results show significant reduction in the mean biases compared to ground-based networks. The work explores the combination of satellite and data assimilation as a powerful technique that holds tremendous promise for social health. Overall, the manuscript contributes a useful and potentially important technique, but the experiments needs some more explanation and supplement.

 

For the following questions, I recommend that this work be considered for publication after major revisions.

 

Overall questions:

1. The authors claimed on page 13 line 7 that AERONET AOD used in this study is at 550 nm. As I know AERONET does not provide AOD at this wavelength, the closest is at 500 nm, which is also not among the four standard wavelengths (470/675/870/1020 nm) and thus frequently missing retrievals. The authors need to check the data they use, and if they are using a different wavelength, conversion might be needed before comparison with MODIS AOD at 550 nm.
2. The authors seem a little bit rough on the comparison between MODIS and AERONET daily average, regarding to temporal agreement. While MODIS onboard Terra and Aqua pass at 10:30 and 1:30 local time, respectively, AERONET can make a much wider range of measurements throughout the day.
3. I have serious concern of the results over the ocean. The diurnal change of boundary layer, as well as humidity and other factors are very different over the sea surface than the terrestrial surface. The authors only use AERONET and AIRBASE sites on land in the test, though there are a couple sites near the ocean or on the islands, it is not enough to validate model performance all the way into the Atlantic Ocean. I suggest removing some results from figure 5 and state the boundary clearly.
4. As the authors partly mentioned on page 22 line 2, MODIS AOD can be heavily impacted by dense cloud cover as well as surface snow cover in the winter. Please note that training and testing with one-month of data is a very short period compared to other studies on the same topic. In addition, I think it would be important for readers to know that what is the actual coverage of the satellite data utilized, and see if there is any regional representative biases.

 

Specific comments:

1. The full spelling of optimal interpolation (OI) is not given on the first occurrence. Same as CTM and a few other abbreviations.
2. Space or no space between number and units seems inconsistent.

Author Response

Please see attached document.

Author Response File: Author Response.docx

Reviewer 2 Report

This is an original paper to improve the modeling of fine particulate matter. I recommend publication in its current form.

 

Author Response

Thank you!

Reviewer 3 Report

Dear editor

 I have reviewed the manuscript named “Using Objective Analysis for the Assimilation of Satellite Derived Aerosol Products to Improve PM2.5 Predictions over Europe” written by Mounir Chrit and Marwa Majdi. They present a model to improve the resolution and precise of PM2.5 in Europe based on AOD measured by MODIS satellite.   The structure of the manuscript is reasonable, hence, I recommended minor revision for this manuscript. Detailed comments are shown as follows:

1. Fig 1 should be revised for its format is not scientific.
2. Tables in manuscript are too short, please make it more reasonability.
3. Please added the platform for LIDAR to detect PM2.5 . The following article may helps you.

1.Profiling the PM2.5 mass concentration vertical distribution in the boundary layer. Atmospheric Measurement Techniques, 9, 1369-1376

2.Quantifying CO2 uptakes over oceans using LIDAR: A tentative experiment in Bohai bay[J]. Geophysical Research Letters, 2021

4. Please add R2, ME, MAE in Figure 6.
5. Simulations has been introduced detailly, please added actual experiment to validate the proposed model.
6. Please add the future application of recommended model in manuscript.

Author Response

Please see attached document.

Author Response File: Author Response.docx

Reviewer 4 Report

Excuse me for being critical. To be honest, this manuscript looks more like a practice for an undergraduate student using successive correction method….. especially the test about the sensitivity of Creesman radius. This is what I often ask my students to do in my college class. The assimilation of AOD using 3DVar, 4DVar, EnKF, or even hybrid EnVar has been widely conducted, why the authors still use successive correction which I don't even want to call “data assimilation”. The authors say the method used in this manuscript is straightforward and computationally portable, this motivation can’t convince me in such a scientific paper. The authors try to argue that the successive correction has advantages over 3DVar or EnKF in introduction, but it seems to be a failure.

Author Response

Please see attached document.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

I thank the authors for answering my questions carefully. I have no major points to add.

I do wish the authors can make some discussion regarding the distribution of data in figure 5, right column. It is not explained in the work why certain area (both over land and ocean) is missing. Also, the grammar of revised part needs more attention, since I notice a couple spelling errors,see below.

"ngstrm": missing A;

avaliable: available;

iterpolation: interpolation;

terresterial: terrestrial;

Reviewer 4 Report

Agree