the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2026
Constraining the Vertical Profiles of Aerosol Component and Heating Rate over East Asia through Assimilating CALIOP observations
Abstract. The vertical distribution of distinct aerosol components fundamentally governs atmospheric shortwave heating rates and radiative effects. However, traditional data assimilation (DA) methods typically rely on total aerosol optical thickness (AOT) or extinction, which fails to constrain the specific aerosol composition and often leads to the misallocation of aerosol between scattering and absorbing species. To address this limitation, we develop a component-resolved four-dimensional local ensemble transform Kalman filter (4D-LETKF) system with spatial and observational constraints within the WRF-Chem model. This novel system assimilates CALIOP-MODIS synergistic retrievals of species-specific extinction profiles (dust, sea salt, black carbon, and water-soluble aerosols) to explicitly optimize the three-dimensional distributions of individual components over East Asia. Results demonstrate that this DA system successfully reconstructs the complex vertical layering of multi-component aerosols. Notably, it effectively corrects severe underestimations of elevated black carbon (BC) plumes, capturing persistent free-tropospheric BC layers over South Asia that traditional models typically miss. Independent validations against ground-based AERONET and AD-Net lidar observations confirm significant improvements not only in total AOT and extinction but also in the single scattering albedo (SSA). By independently adjusting the mass of scattering and absorbing species, the system corrects local biases in aerosol optical properties. Consequently, the optimized component-specific profiles profoundly affect the atmospheric shortwave radiative heating. The elevated BC plumes induces a pronounced mid-tropospheric warming accompanied by a reduction in lower-tropospheric heating due to the attenuation of downward solar radiation. This study highlights the importance of component-specific vertical constraints for accurately assessing aerosol-induced atmospheric heating and its vertical structure.
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Status: open (until 17 Jul 2026)
- RC1: 'Comment on egusphere-2026-2340', Anonymous Referee #1, 19 Jun 2026 reply
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RC2: 'Comment on egusphere-2026-2340', Anonymous Referee #2, 20 Jun 2026
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2340/egusphere-2026-2340-RC2-supplement.pdf
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- 1
This paper describes a regional data assimilation experiment for aerosol using a novel height- and species resolved observational dataset. The authors evaluate the analysis against both assimilated and independent observations and conclude significant improvements can be found in the analysis vs the free run. The analysis shows sizeable redistributions of aerosol and an increase of total black carbon  which, as the authors show, strongly affects heating rates in atmosphere. This points to important limitations in our forward modelling ability.
The paper is very much in scope although one of its conclusions (that modelled and observed vertical profiles are very different with a strong impact on heating rates) will not surprise many people. However, the method to correct this is new and the paper describes the issue for a region very important for anthropogenic aerosol. Â The paper is also very readable.
I have three main comments:Â
I also want to point out that items 2) and 3) are related: the larger the observational uncertainty, the less truth worthy the background correction.Â
I further have the following comments/questions:
p. 1, l. 31: induces -> induce
Section 1
p. 2, l. 41: "Elevated absorbing layers...lower atmosphere" this sentence is not clear to me, it seems that the two parts say almost the same, so why do you use 'whereas"?
p. 2, l. 46: I'm a bit surprised by the mention of water vapor absorption. Surely this happens at ()IR wavelengths but your abstarct talks about SW.
p. 2, l. 60: I would argue that CALIOP and CATS also suffer from limited spatial coverage. CALIOP has a beam width of 70m? Maybe the authors can be a bit more precise. The spatial coverage here is horizonatl spatial coverage.
p. 3, l. 86-91: The statement (that the problem is particularly severe over East Asia) is not supported by analysis or references. Please provide some, or be more cautious in your statement.
p. 4, l. 120-121: "Aerosols are parametrised as external mixtures ..." Are the sizes and standard deviations defined in WRF-Chem identical to those in CRTM? Especially older models tend to be inconsistent about such choices.
p. 5, l. 39: Can the authors provide a bit more detail about observational uncertainty? They assume 10% retrieval uncertainty but that can't be right. Especially because they break down extinction into different components. Table 3 in Kudo et al. (2019) suggests uncertainties of more than 100% in components like absorbing aerosol, dust aerosol. Assumed uncertainties in components will be important in the data assimilation step so I would like to ask for more detail from the authors.
p. 5, l. 142: it is very likley that the authors underestimate representation uncertainty with this method as observations tend to cluster and aerosol fields tend to be strongly correlated in space. I understand that better approaches are hard to come by but please discuss this. See also Rijsdijk et al. GMD 2025 for a similar problem with NO2.
p. 5. Sect. 2.2.1.: It seems to me the authors do not discuss how the WRF-Chem aerosol is mapped onto the CALIOP-MODIS components. Can they provide more detail? In particular, how well do WRF-CHEM assumed particle sizes agree with retrieved sizes per component? Same for refractive index per species. Both WRF-Chem and Kudo et al. make assumptions on refractive index. Are they the same? I later saw that you partially answer this in Sect. 3.1. Still, my question on sizes and refractive indices still stands.
p. 7, sect 3: Can the authors provide more detail on the LETKF system, especially the timing of assimilation? Does it happen every hour, or only at CALIOP overpass times? What is the time-window (4D-LETKF suggests there is a time-window in which obersvations at t will influence state at times before t). Also, how is the ensemble created? Are emissions perturb, or concentrations? What size of perturbations? Any spatial/temporal correlations in the perturbations?Also, how do the authors deal with boundary conditions (inflow from source regions outside domain).
p. 9, l. 206: "local patch center" has not been defined. Please rephrase. Only people familiar with LETKF will understand what it means.
p. 9, l. 215: "This strict variable localization... after data assimilation". If the observations of components were independent and the mapping from WRF-Chem to observations perfect, wouldn't the authors expect this to be unnecessary? Of course, nothing is perfect but I would appreciate it if the authors explain a bit more why this variable localization is necessary.
p. 9, l. 219: "severe model underestimations" does this happen only for component AOD or also total AOD? What causes it? The authors description is a bit vague. Does default WRF-Chem already underestimate AOD? How large is the spread in the emissions and component AOD compared to typical errors? A common solution to this is a pre-tuning step whereby emissions are scaled according to a mismatch between deafult forward run and observations.
p. 10, l 230: "outliers" so what do you do with outliers? I suppose they are not assimilated but I can't find this in the text. Please clarify.
p. 10, l. 231: "background ensemble factors are rescaled by dividing by this factor." So each member is rescaled with the samefactor. Please clarify this. Also, this implies that you correct the ensemble mean but the ensemblespread remains rather small. I guess that may be why inflation is later on needed? (In my experience, with perturbed emissions, inflation is seldom needed).
p. 10, l. 236: "while preserving the physical consistency of the ensemble". I think this background correction stratgey is a poor man's data assimilation tehnique. It ignores uncertainty in both ensemble and observations and just puts the analysis equal to the observations. It seems unsatisfying especially because the authors remain quite vague about how often this happens in the first place. So I would appreciate more discussion and explanation (see also previous questions). How often is this background correction triggered compared to a real Kalman update? Are there any patterns in this triggering (e.g. more over land, more in free troposphere than in BL etc)?
p. 10, l. 236: "physical consistency" please explain, the meaning is unclear to me. What aspect of physics do you refer to? Clearly no law of conservation is involved so I don't understand "physical consistency"
p. 11, l 291: "core_shell" this should have been discussed earlier in Sect 2 on data and methods. See also my previous comments on mapping of aerosol species from obs to GOCART.Â
p. 11, fig. 3 I would suggest using logarithmic axes to bring out the region from AOD=0.01-0.1 better
p. 13, l. 317: "substantial improvement" please remind the reader that this is a self-validation; the authors are only testing with observations that were used in the assimilation.
p. 13, l 324: "system is difficult" -> "system has difficulty". Your background correction strategy is designed for this sort of problems, why does your system still have problems correcting the low bias?
p. 13, l. 325: "magnitude of lofted plumes" it would be interesting if the authors can supply some analyses of plume structure in obs and FR/DA. Is it possible to show curtain plots (latitude-altitude, averaged over longitude for E Asia)i for BC or something similar? Fig 3 and 4 provide average statistics so something that tells us more about how good DA reconstructs observed plumes.
p. 15, sect 4.2: It would be very interesting if authors can also provide an evaluation of the Kudo dataset with AERONET and ground-based LIDAR. This would help understand how much the validation of DA is driven by errors in the Kudo dataset vs errors in the assimilation itself.
p.20, l. 419: if we assume that the AERONET sites are relatively close to sources (closer than CALIOP), this suggests that emissions may be too high but lifetimes too short? If this idea has merit, please discuss it in the paper.Â
p. 21, l 451: "traditional assimilation of total AOT merely scales" This is incorrect and depends strongly on the details of the assimilation technique. Even by assimilating only AOD can one change profiles and speciation because the AOD observations include locations both near and faraway from sources and so influence the analysis at different parts of the transport. In addition it is entirely possible (and has been done many times) to include either/both speciation and profile into the statevector. I am not saying that speciated observtions (as in this paper) are not important (I think they are) but the authors' statement is simply incorrect.
p. 21, fig 12: please explain the downward spikes seen in SSA for the DA results in a), b) and d).
p. 22, l. 467: "warming suggests tha the assimilation effectively corrects" This is a really strange conclusion because you have no independent heat profile data to verify against. Also, the Da results have already been evaluated against independent AOD, extinction and SSA data.I suggest removing or completely rephrasing.
p. 26, l. 520: "the DA experiment succesfully recosntructed the vertical profiles of all four aerosol components". I think the authors overstate their results. First: there is no independent vertical profile data on speciation that confirms this result. Second: it is not a surprise that self-validation looks so good because the authors use a background correction scheme that makes the analysis identical to the assimilated observations. The authors should rephrarse their statement and clearly describe the limitations I just mentioned.
p. 26, l. 535: "internal mixing" while it is important to discuss this limitation in the Conclsuion I think it should already be discussed in Methods