the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Mar 2026
Assimilation of ground based lidar and ceilometer observations of aerosols from the European E-Profile network into ECMWF's Integrated Forecasting System (IFS-COMPO, CY49R1)
Abstract. The Integrated Forecasting System with its extension for atmospheric composition (IFS-COMPO) provides global forecasts of atmospheric trace gases and aerosols for the Copernicus Atmosphere Monitoring Service (CAMS). The present system constrains aerosol concentrations by assimilating aerosol optical depth (AOD) from different satellites. Here, we explore the possibility of assimilating, in addition, ground-based lidar and ceilometer observations from the European E-Profile network. The system performance is evaluated by comparison to non-assimilated E-Profile stations, AOD observations from Aeronet, and aerosol surface concentrations from AirBase. Assimilation of E-Profile data significantly reduces biases and root mean square errors (RMSE) of model-equivalent vertical profiles of the attenuated backscatter coefficient. Without assimilation of E-Profile, surface concentrations of particles smaller than 2.5 μm (PM2.5) are frequently overestimated during summer, while corresponding concentrations of particles smaller than 10 μm (PM10) tend to be underestimated. Assimilation of E-Profile can reduce the RMSE of PM2.5 by up to 50 % and of PM10 by up to 10 %. Since the present analysis system uses the total aerosol mass mixing ratio as control variable, it cannot simultaneously reduce the positive PM2.5 bias and the negative PM10 bias. It typically reduces the PM2.5 bias at the expense of PM10, since fine particles make the dominant contribution to the optical cross sections per mass. Tests of different assimilation-system configurations reveal that the best overall performance is obtained by treating optical properties of dust with a spheroid model, suppressing vertical correlations in the background error covariances, and applying a relatively aggressive cloud mask.
Competing interests: Co-author Samuel Rémy is a member of the editorial board of Geosci. Mod. Dev.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-6077', Jeffrey Reid, 20 Apr 2026
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RC2: 'Comment on egusphere-2025-6077', Anonymous Referee #2, 16 Jun 2026
Review
The paper of Kahnert et al., describes the efforts being made to assimilate continuous operating ground-based lidars ( in the framework of E-Profile) into the aerosol forecast model at ECMWF. Generally, the paper is very well written. The procedure and new developments for assimilation of this novel data are properly described. Furthermore, the effect of the assimilation efforts are discussed and show the clear benefit for the aerosol forecast (at ground wrt PM2.5 and PM10) when using ground-based profiling instruments. For this reason, I consider the manuscript as highly relevant and in general suitable to be published in GMD. While the main part of the manuscript deals mostly with a short overview of the assimilation efforts and the discussion of the results, the technical and also very interesting part is given in the several appendices. Due to the high relevance of the Appendix, I feel that its content gets a bit underrepresented if discussed after the main manuscript but if the authors like it can stay as is.
However, I have some comments which need to be tackled before the paper can be accepted. Even though they are more of technical nature, I consider major revisions as appropriate for the manuscript as the impact on the paper is severe. While I have not much to criticize in the major text part, most comments for the reasons stated above are for the appendix but also for almost all figures.
Attention: English language editing of the manuscript (spelling, grammar, sentence structure) is not provided anymore by Copernicus. But I did not check for this in detail during the review.
Major issues
My major concern is about the quality of the figures. Partly they need to be completely overworked. In some figures, basically nothing is visible due to unfavourable scaling (x- axis, y-axis, z – axis). Partly, the labels are missing or way too small so that the figure is not self-explaining. All in all, it appears sloppy.
Furthermore, one needs to homogenise the writing and style of the whole manuscript: Labels/captions in the plots start partly with capital letters partly not – please unify.
Partly () and partly [] is used for units in the plots – please unify.
Please also unify times in the manuscript (text and plots)For an outside model world reader, it would be great to make clear what the difference between CAMS and IFS-Compo is and then also stick to it in the text. Currently, it is quite often mixed up.
In general, it would be great to provide some of the model specific explanations in a more understandable way for the general reader or at least give proper reference.Could you explain why you assimilate CL(3)51 and CHMK differently? Could you do both simultaneously – if yes – why not done, if not – why?
Figures to be adjusted
The following figures need to be overworked:
Fig. 1: Quality of pixel image is poor, please provide higher resolution or vector graphics.
Fig. 2 + Fig. 3: Is way too small. Almost no labels are readable. Please enlarge all labels/captions and consider splitting the Figure into 2 different ones.
Fig. 4: Is hardly readable. Almost nothing is seen with the current color scheme – please change so that one can “see” something – also consider adjusting the z-scale (maximum at 5.353 – why?):
Fig 5: same as Fig 4, but also label not correct as it is not only CL51 but also CL 31.
Fig 6: same as Fig 4
Fig 7: same as Fig 4 but also label not correct as it is not only CL51 but also CL 31.
Fig: 8: No y-axis labels -> need to be added
Fig. 8; Bottom row plots: Labels to small, image quality poor: Please enlarge labels, enlarge image quality, and separate figures.
Fig 9: same as Fig 8.
Fig. A1: Please change y-axis scale for two-way transmittance, e.g. from 0 to 1.2. Currently hardly anything is visible in e and f
Fig. A3: Please use SI units (micron is not appropriate). Please also plot the absolute values in the Figures.
Figure B3: Is the x-axis in the centre plot correct?
Figure C1: Please unify x-scale with other plots. Epoch time is not appropriate here…
Figure C2: x-scales partly different (for the same day) and no label provided - thus hardly interpretable. Please also increase quality of image.
Figs. D1 + D2: see comment on Figure 4
Fig. D4: I am sorry, but I do not see nothing with the current color scale. It is simply not distinguishable. Please change color coding. Furthermore, you need to explain all abbreviations in the caption! It’s partly just guessing what is meant, E.g. what is “def mask”? The conservative mask?!? Remember that you need to explain all abbreviations in the caption. Furthermore, please also check the labels, aod should be AOD, Rmse should be RMSE etc… please be consistent in the whole manuscript (and of course between figures and their caption).
Specific comments:
Page 16: Lines 245 to 249: For curiosity: Are their plans to do so, e.g. assimilate profiling data at multiple wavelengths or with additional capacities (Raman, depolarization)?
Page 16: Lines 257-260: There are also many other networks which either provide already or work on providing near-real time data. I think it would be worth reaching out to them to advertise your work. Furthermore, a clear conclusion on what is needed for assimilation side except near-real time would be great. For example, att. backscatter profiles are already provided by ACTRIS, EARLINET, AD-Net just to name some – all are also part of the WMO GAW Aerosol Lidar Observation Network (GALION) and I wonder why one could not simply use this data as well.
Page 17, 282: Could you please list the values you use for the Lidar ratio and the mass mixing ratio for all aerosol species in IFS-COMPO – probably a table would be good for that.
A1: instead of using a median atmosphere for the correction of water vapor absorption wouldn’t it be better to use the IFS output for this? I guess everything is better than median profiles…and H20 is rather variable in time and space. Can you comment on this?
Page 19, 309 and 310: Why to only consider Ozone absorption at 532 nm and 1064 nm? Can you explain? It is not obvious at all from Figure A1.
Page 20, 331-33: What are adjoint tests + operator and tangent linear model --> for me it is not clear what is meant with this sentence. Please add some words.
Page 20, 333: What is CY50R1? Never explained. I assume it is the successor – but this is just an assumption….
Page 20: A2:
I really appreciate the efforts to account for the non-sphericity of dust particles. And obviously, everything is better than using Mie theory to calculate optical properties for dust. However, I think it is worth noticing in the manuscript that the spheroidal model cannot accurately reproduce the backscatter coefficient and thus also the lidar ratio. I therefore have my doubts that the calculated lidar ratios (which I do not know as just the ratio to the spherical model is provided) is in the range with observations. Could you comment on this and also provide the lidar ratio values you finally use (i.e. give absolute values for what you show in Fig A3).
Sec C1: Why do you use these hard boundaries for the height range top in your lidar data? Given your noise mask described further below, I would assume that such strict cut offs at 3 km or 5.3 km are not necessary….can you comment on it?
Sec C1: The threshold for the cloud and precipitation masks are rather low, but might be justified due to the strong attenuation in clouds and precipitation. How did you select the thresholds? Empirically? Please comment.
Page 23:399 -400. Basically, it means that for the precipitation mask, you use just the height range from 500 m below cloud top to 1000 m below cloud top, correct? I.e., you screen out data even though the precipitation might not reach the ground. Correct? Can you discuss why?
P. 23, Fog mask: Given Eq. C5, you might also screen out nocturnal or wintertime shallow boundary layers with a polluted PBL and clear sky above. Would this have a significant impact on your PM modelling? In fact, this threshold-based method might screen out too much?
Figure C1: Second panel: In the first ~9 hours of the day, the top of the PBL is frequently misclassified as cloud. Can you comment/discuss? How do such effects affect your PM2.5 and PM1.0 simulations at ground?
Figure C1: Third panel: the rain mask also affects many regions which are well below cloud boundary and most probably not affected by rain. For this reason, you lose a lot of data which might be interesting for assimilation as well. Can you comment on this? Is this by purpose?
Figure C1: Last panel: The noise mask plot shows some “vertical” stripes below the cloud reaching down to the ground. Why does this happen? Can you explain/discuss?
Page 32, 528 to 529: “for reasons already mentioned above” This is not understandable. To what reasons do you refer to? Please discuss more intensively.
Section D5 could properly be included in the main body of the manuscript.
Some technical comments:
Page 2, Line 28: This sentence is a bit sloppy, please provide a correct statement what ceilometers and lidars are. E.g. it’s not the laser power backscattered but parts of the light pulses emitted by a laser into the atmosphere…
Page2: Line 41: “Spectral truncation T511” never introduced. What is this?
Page 5, Line 117: What is “unwanted data”. This sounds a bit strange, please reformulate or explain better…
Page 8, Caption Figure 2/3: What is “analysis increment”? Please explain.,
Page 14, 204: Shows --> shows
Page 26, line 458. “Earlinet” was never introduced nor referenced. Furthermore, some more information is needed here about what exactly has been tested.
Fig. D3: What does “time: 3” stand for? 3 UTC? Please state more correctly so that it is understandable for an outside reader.
Citation: https://doi.org/10.5194/egusphere-2025-6077-RC2
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- 1
This paper presents numerical experiments on the assimilation of E-Profile data into CAMS. E-Profile is a well-populated network of ceilometers with what appears to be a robust and well-thought-out data distribution system, making it eligible for the Benedetti et al. (2018) critieria of being "fast, accessible, and well-documented" for use in operational aerosol assimilation. And the good news is that, if taken from a purely statistical point of view, the authors show that the assimilation of E-Profile data is beneficial to the analysis of other variables such as PM2.5. This is in itself important: assimilating one set of parameters (here ceilometer-derived attenuated backscatter) helps the analysis of a fully independent set—in this case, PM2.5. If this is the exit criteria, done. The authors and study participants have done their job and have a result that is quite worthy of publishing in GMT.
But (and in data assimilation there is always a "but"), while the authors show a positive impact for region-wide error statistics, it is unclear to the reader what is going on—and in particular, where the impact is relative to significant events. I very much appreciated the appendices, but even here specifics seem to be unclear. I can understand any possible reticence of the authors to add more details, as data assimilation investigations easily lead to endless rabbit warrens that can go in unexpected directions (think Watership Down). But I think there are many things that can be made clear through more direct language, and also some additional discussion, in particular of their example cases. I don’t expect the authors to perform any major reruns of their analysis, but what I suggest can take a little bit of time without being particularly onerous or lengthy.
The problem at hand is that data assimilation is not unlike a half-filled water balloon. You constrain it somewhere, and based on the workings of the model, things can pop out in other places. For CAMS, as the authors point out, the baseline is AOD assimilation. E-Profile data modified this baseline through the instruments' and the model's attenuated backscatter, which is an underdetermined parameter. There are lots of ways to distribute an increment in attenuated backscatter in the model, and yet there is no discussion whatsoever of the 4D-Var adjoint. Based on my previous conversation with Angela Benedetti, the adjoint for aerosol is quite simple (as it should be). Nevertheless, for the case studies, I would strongly recommend adding direct details of what is changing in the model. I am guessing the aerosol speciation fraction is not changing. If so, it would help if it was stated clearly. If it is, how so? Details of how the AOT error changes would also be helpful. There are many AERONET sites in Europe, and I found some nearby for the two test cases shown.
It is pretty trivial to make your model look like your observation, but the real question is to what extent you break something else in the process. Returning the the half filled water balloon analogy, they have optimized for pan-European bias and RMSE. But we don’t know what is happening at sites and more significant events. We then have a long throw from two incomplete case studies to domain-averaged bias and RMSE. This is equivalent to a forecast contest when you guess the next day's high temperature ahead of a cold front. Get the timing of the front wrong and you have lost. But statistically, if you pick the mean temperature climatologically, you have a good shot at winning—not in spirit, mind you, but by the metrics at hand. So I, as a reader, would appreciate a few more statistics—at the very least for picking more significant versus background events, event skill scores, and/or a PDF of sites that see improvement versus sites that may be diminished.
Some of what is going on is somewhat obfuscated in the appendices. There is a great deal of discussion on "updates to the optical model," but in the context of this work, all they need to say is, "We essentially changed the lidar ratio for the near-infrared from X to Y." Such a change could easily result in a big change/improvement in attenuated backscatter and vertical distribution without changing AOD. But it would help if they showed that. Likewise, adding a VIIRS image and an AOT plot would be greatly beneficial. Maybe even time-height cross-sections for Figures 2 and 3 would make the authors' case more effectively.
Anyways, I did enjoy reading the paper, and it made me think. These comments are offered out of admiration for the work. I leave it to the authors to decide how far they want to take this.
Specific comments.
Ln 6: AERONET, not Aeronet.
Figure 2&3: Figure text is way way too small. Just a suggestion, can you please add as visible dots the model midpoints to the profiles. Also, maybe add below a timecodes of the baseline ceilometer data? Lastly, please revise before the typesetters ask you to. Also, please add the change in AOD for these cases.
Section 2.1: I don’t mean to extend the paper, but I would appreciate a few more details on the assimilation process up front in Section 2, instead of having to rummage through the supplemental materials. The authors note, on one hand, that the model is at full resolution, but the minimization is at a lower resolution. I would appreciate the authors being a bit more specific on these points in the main body.
Where this is going is that their assimilation experiment is using high-density ceilometers, but what is the nature of the data assimilation resolution versus, say, meso-alpha features that may be important in Europe, where terrain variability can be significant? Again, I am not asking for a full analysis, just a little context for the experiment and what it captures and what it doesn’t. For example, the use of wavelets seems to be important, but the main body and the referenced appendices are quite thin on this method.
Section 2.2.1 / Figure 1: Comparing the left and right panels, it looks like most (but not all) of the stations for validation are also collocated with stations for assimilation—or are so close as to make no difference. How are you holding back data then? Temporal removal? A bit more description here would be appreciated.
Section 3.1/Figure 2 &3: I very much appreciate case studies—they are where you can learn what works and what doesn’t. I recommend significant expansion here, not only in the figures (e.g., speciated AODs would really help), but also by adding a time element. The case you showed for September 6, 2023, is on the backside of a big event, it seems, based on my own analysis. So, that site was hit with a big event on the 4th and 5th and cleared out on the 6th. But CAMS did not clear it out. Hence, E-Profile made the beneficial correction, but you never really know that unless you look at a bit of a timeseries. This kind of context helps the reader really understand what is happening four-dimensionally in the model. Oh, and please increase font size before the typesetter asks you.
Figure 4 and 5. With the colorbar it is sometimes hard to see what is going adding on. I suggest adding line plots for say 3 levels (surface, 1 km, 3 km?). You could also add individual stations for a good middling and low performers.
Section 3.2: Maybe a bit more discussion on what regions benefitted the most/least and a discussion why you think that is? You mention you think it is because some regions have higher/lower quality instruments (e.g., line 200), but does any of this also correlated with meteorological or terrain features? If you think it is instrument related, shouldn’t that be one of the core conclusions on what kind of instruments are most appropriate? Also, the discussion of PM2.5 bias (Lines 210) again leads to a rabbit’s warren on optical properties. So again, what ultimately is the lidar ratio you are deriving here? Does it make sense?
Figure A1. Maybe make (e)-(h) on a log scale?
Figure A3. Can you also please provide the baseline lidar ratio you are using for spherical or nonspherical (your preference), But there are factors of 2 floating of around and I am curious where this system sits relative to field observations.
D4: Perhaps a bit more discussion in the impact of AOD? Given the changes in Figure D3, I would imagine the AOD change would be huge. Can you give specific numbers for this? The mean bias plot (D4) would necessarily be of low magnitude because the mean AODs are low. How about for significant events? Maybe break it up between, low, middle, high AODs? The wavelet analysis finding is interesting. Care to elaborate?