the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Abstract. Modeling and observational techniques are pivotal in aerosol research, yet each approach exhibits inherent limitations. Aerosol observation is constrained by its limited spatial and temporal coverage compared to models. On the other hand, models tend to possess higher uncertainties and biases compared to observations. Aerosol data assimilation has gained popularity as it combines the advantages of both methods. Despite numerous studies in this domain, few have addressed the challenges faced in assimilating aerosol data with significant differences in magnitude and degree of freedom between the model state and observations, especially in the vertical direction. These challenges can lead to the preservation or even exacerbation of structural inaccuracies within the assimilation process. This study investigates the sensitivity of dust aerosol data assimilation to the vertical structure of the aerosol profile. We assimilate a variety of dust observations, encompassing ground-based particulate matter (PM10) measurements and satellite-derived dust optical depth (DOD) data, using the Ensemble Kalman Filter (EnKF). The assimilation process is elucidated, detailing the assimilation of raw ground-based and satellite-based observations for an optimized three-dimensional (3D) posterior state. To demonstrate the impact of accurate versus erroneous prior aerosol vertical profiles on the assimilation result, we select three cases of super dust storms for analysis. Our findings reveal that the assimilation of ground observations would optimize the dust field at the ground in general. However, the vertical structure presents a more complex challenge. When the prior profile accurately reflects the true vertical structure, the assimilation process can successfully preserve this structure. Conversely, if the prior profile introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile. This is also found in the assimilation of DOD, which exhibits a comparable pattern in its sensitivity to the initial aerosol profile's accuracy.
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Status: open (until 14 Nov 2024)
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CEC1: 'Comment on gmd-2024-113: No compliance with the policy of the journal', Juan Antonio Añel, 29 Oct 2024
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have archived your code in a repository that does not comply with our trusted permanent archival policy (https://airqualitymodeling.tno.nl/lotos-euros/open-source-version/). We can not accept this. Therefore, you must publish your code in one of the appropriate repositories according to our policy. Note that we can not accept embargoes such as registration or previous contact with the authors to get access to the code.
In this way, you must reply to this comment with the link to the repository used in your manuscript, with its DOI. The reply and the repository should be available as soon as possible, and before the Discussions stage is closed, to be sure that anyone has access to it for review purposes.Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code. Also, remember to include a license for your code. If you do not do it, the code continues to be your property and can not be tested by others. Therefore, when uploading the code to the repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Please, reply as soon as possible to this comment with the link for it so that it is available for the peer-review process, as it should be. Additionally, be aware that failing to comply promptly with this request will result in rejecting your manuscript for publication.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/gmd-2024-113-CEC1 -
AC1: 'Reply on CEC1', Jianbing Jin, 06 Nov 2024
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We would like to thank the editor for the reminding. We have published the LOTOS-EUROS code in Zenodo. A GPLv3 license file is also explicitly included in the repository. The LOTOS-EUROS model code is made available at https://doi.org/10.5281/zenodo.14039267. PyFilter is archived on Zenodo (https://doi.org/10.5281/zenodo.14036308). All the revised links are listed in the supplement and will be included in the manuscript in the future.
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CEC2: 'Reply on AC1', Juan Antonio Añel, 06 Nov 2024
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Dear authors,
Unfortunately, I have checked the new repository for LOTOS-EUROS and the access is restricted. As I said in my previous comment and our policy clearly states, repositories must be open to everyone and we can not accept that it is necessary to get permission to access to the code or data. Therefore, your previous reply does not address the issue that I pointed out, and you need to reply to this comment with a new non-restricted repository that contains the full LOTOS-EUROS v2.2 model. If you continue to fail to meet the requirements of our policy we will have to reject your manuscript for publication.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2024-113-CEC2 -
CC1: 'Reply on CEC2', Arjo Segers, 07 Nov 2024
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Dear Editor,
sorry for not immediately following the open-source requirements strictly. Our original upload of the source code was inspired by other Zenodo items accompanying GMD articles, which also used a restricted access, but apparently that form is not sufficient anymore.
We created a new item under link https://zenodo.org/records/14051139 (doi:10.5281/zenodo.14051138) that is open for public.
With this we hope that we have satisfied all requirements.
Arjo SegersCitation: https://doi.org/10.5194/gmd-2024-113-CC1
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CC1: 'Reply on CEC2', Arjo Segers, 07 Nov 2024
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CEC2: 'Reply on AC1', Juan Antonio Añel, 06 Nov 2024
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AC1: 'Reply on CEC1', Jianbing Jin, 06 Nov 2024
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RC1: 'Comment on gmd-2024-113', Anonymous Referee #1, 14 Nov 2024
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General comments
This manuscript by Pang et. al. presents a study into the sensitivity of aerosol’s vertical position when assimilating ground or columnar observations. Starting from the basic formulas of the Ensemble Kalman Filter, the expected results with the two observation kinds are formulated theoretically and they are subsequently studied using a standard NWP model and real observations.
Assimilation of this kind of observations still has many unanswered questions, and these kinds of sensitivity studies are highly relevant. I particularly enjoyed reading through the analytical formulation of the problems and then seeing actual real-world data to back up the conclusions. The manuscript is well written, with clear objectives and methodology. The drawn conclusion is fair considering the results. I wholeheartedly recommend publication after some relatively minor points are addressed. I hope the authors find the following comments useful for improving the quality of the manuscript.
Specific comments
- As I understand it, when the authors are assimilating ground-based observations, they are not using a vertical localization scheme. By not doing this, you implicitly assume that a ground-based point measurement of concentration is representative of the whole atmospheric column, which might not hold in many cases (e.g., long range dust transport occurring at higher altitudes, local emissions near the ground measurements). The point of this manuscript is not to study localization schemes, but applying localization would eliminate the issue demonstrated in Fig. 2, panels b.1 and b.2. The text near line 205 describes what would happen with strong vertical correlations in your prior, but with localization they would diminish as you increased your distance from the point observation and reduce the problematic inflation of concentrations. I strongly recommend the authors add a case demonstrating what would happen if you used vertical localization or add a note explaining its absence.
- It would increase clarity to add a brief description about the aerosol model used in the simulations. For example, which species are used, what kind of optical properties are assumed, if there are size bins and their ranges, etc. It would also be useful to describe the observational operators for AOD and PM10 in more detail (i.e., which size bins are considered for PM10).
- In the case studies, it is mentioned that DOD is assimilated from Himawari. Does this mean that only dust is considered in the model AOD operator? Maybe it can be referred to as a “DOD operator” for clarity.
- When comparing with CALIPSO, is the CALIOP aerosol classification checked to ensure that dust dominated the scenes? There are methods for extracting the dust-only component from lidar measurements (for example, Mamouri et. al. 2017 and Amiridis et. al. 2015), which might be useful, given that the model also only includes desert dust in the extinction profiles.
- When comparing CALIPSO to the model, some statistics would be interesting (e.g. RMSE). Currently the comparison is “by eye”.
- In the introduction, it would be helpful to mention uncertainties in optical properties when discussing the difficulties in assimilating aerosol-related observations (around line 50). Some of the publications mentioned in section 2.3.1 about LiDAR assimilation would fit in the introduction as well, around line 70.
- Line 69 mentions that no instrument can provide continuous vertical information about aerosols, but both ground-based and space-borne LiDARs do that. They do not provide 3D fields, but the information is continuous.
- Please consider providing more information about the modelling setup to aid in the reproduction of your experiments. More details could be added to section 2.1.1 about model settings and what kind of initial and boundary conditions are used. Consider uploading configuration files and IC/BC files to a public repository.
Technical Comments
- At lines 148-149, the superscripts `a` and `f` are used, probably referring to analysis and forecast. This should be mentioned for clarity.
- Line 176 includes the phrase “[…] to nudge the 3D states […]”. Since nudging refers to a specific family of methods, it might be clearer to use another word here.
- In Figure 5, it would be interesting to see the ground-based LiDAR profiles as a third line in the bottom panel, for direct comparison with the model. I understand that the model plots show concentration, but they could also be extinction, since that is a required step for computing the AOD/DOD.
- Lines 374-375, I believe the observations have ‘low/high dimensionality’
- At various points in the manuscript, the word “fraud” is used in regard to low quality observations or information. I think this specific word implies malicious intent, which I hope is not true in any case. For example, line 70 would be clearer as “When there is incorrect/inaccurate information […]” instead of “When there is fraud information […]”. Consider replacing all instances of “fraud” with something milder.
Best of luck to the authors
Citation: https://doi.org/10.5194/gmd-2024-113-RC1
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