the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Mar 2026
The Mineral Aerosol Profiling from Infrared Radiances version 5.1 algorithm and its evaluation
Abstract. Mineral (desert) dust aerosols are small sand/dust particles entrained by winds from bare areas and possibly transported over long distances. These aerosols are climate forcers and affect human health and many socio-economic sectors. They are therefore important to monitor both in near-real time and on the long term. In this work, the Infrared Atmospheric Sounding Interferometer (IASI) instrument is used to retrieve vertical profiles of mineral dust aerosols concentration, from which a 10 μm aerosol optical depth (AOD) and a mean aerosol altitude are obtained. More specifically, we present here the new version 5.1 of the Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm and its changes with respect to previous versions. MAPIR v5.1 was used to produce a consistent time series of dust profiles since the start of the IASI observations in 2007 and until now, using data from IASI onboard Metop-A and Metop-C. The capabilities of the instrument and retrieval are illustrated, showing good event detection, expected AOD seasonal cycles, good profiling capabilities and reasonable mean aerosol altitude, good time and cross-platform consistency. A true validation exercise is not possible as there exist no reference aerosols data from thermal infrared measurements (around 10 μm). Therefore, the absolute value of the obtained AOD can not be validated, although the best possible evaluation is provided using data obtained in the visible spectral range.
Competing interests: At least one of the (co-)authors serves as editor for the special issue to which this paper belongs.
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
(30965 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-924', Anonymous Referee #1, 31 Mar 2026
-
RC2: 'Comment on egusphere-2026-924', Anonymous Referee #2, 17 Apr 2026
Review of “The Mineral Aerosol Profiling from Infrared Radiances version 5.1 algorithm and its evaluation”
This study describes version 5.1 of the MAPIR algorithm for retrieving dust concentration profiles and column-integrated AOD at 10 µm from IASI observations onboard the Metop-A and Metop-C satellites. Key updates from version 4.1 include extending the vertical retrieval range to 10 km, removing pre-filters to maximize data coverage, adopting updated surface emissivity and radiative transfer models, reducing spectral noise inflation, and propagating temperature and humidity uncertainties. The algorithm is evaluated against AERONET, CALIOP, and EARLINET. Long-term stability and IASI-A versus IASI-C consistency are also assessed. Overall, the paper is comprehensive, transparent about limitations, and delivers a scientifically valuable long-term data product. However, there are still some major converns needed to be addressed before considering acceptance.
- Separation of retrieval quality from the TIR-to-VIS conversion uncertainty. In sect. 3.3, the authors compare MAPIR converted dust AOD at 550 nm against AERONET SDA coarse-mode AOD at 500 nm, using a fixed conversion factor of 1.78. The authors mentioned that the AOD-dependent bias could be a constant bias if a different conversion factor (2.85) were used. This means that the central validation metric conflates two distinct sources of uncertainty: the quality of the 10 µm retrieval itself and the accuracy of the wavelength conversion, which depends on assumed particle size and refractive index (Capelle et al., 2018; Clarisse et al., 2019; Zheng et al., 2023, 2026).
As the authors acknowledge that direct reference to AOD at 10 μm is currently unavailable, it is suggested to provide a sensitivity analysis in which the comparison statistics (correlation, slope, bias, same as Figures 4 and 5) for a range of plausible conversion factors (e.g., 0.9 to 3.5, as quoted from Clarisse et al., 2019), showing how the statistics change. In addition, it is suggested to acknowledge the necessity of constraining dust microphysical properties (e.g., size, shape, refractive index) to reduce the uncertainty contributed by the conversion factor, as previous studies mentioned (Capelle et al., 2018; Clarisse et al., 2019; Zheng et al., 2023, 2026).
It would also be informative to note which end of the conversion factor range corresponds to which particle sizes, such as "smaller particles produce higher conversion factors,” which would help the reader build intuition. - Quantitative profile validation needs strengthening. The profile comparisons with CALIOP (Sect. 3.6.1, Figs. 12–14) are presented as qualitative, side-by-side visualizations without summary statistics. The EARLINET validation (Sect. 3.6.2) is limited to 12 dust events at one station (Limassol, Cyprus). Given that MAPIR’s unique selling point relative to column-only TIR products is precisely the vertical profiling capability, a more rigorous profile validation would significantly strengthen the paper.
Therefore, for the CALIOP comparisons, it is suggested to add layer-by-layer statistics (mean bias and correlation) for the matched orbit segments, even if the comparison is approximate due to different retrieved quantities (extinction vs. concentration). If possible, for the EARLINET comparisons, the authors could also consider expanding the analysis beyond Limassol to other EARLINET stations in the dust belt. Otherwise, it would be better to provide the mean and standard deviation of the MAPIR–lidar difference as a function of altitude for all 12 cases, with and without the averaging kernel correction, to provide a quantitative sense of the systematic vertical bias.
Although the authors acknowledge that a dedicated profile validation paper is in preparation, a brief quantitative summary here would improve the current manuscript substantially. - The a priori positive bias floor and its implications for climatological averages. The increased minimum a priori concentration of 2 particles cm⁻³ across the entire profile (Sect. 2.1.3) creates a retrieval floor of approximately 0.06 at 10 µm AOD (~0.14 at 550 nm), which the authors acknowledge (lines 558–565 and 835–836). While this is understandable from a retrieval stability perspective, the consequences for users constructing climatologies, computing regional means, or performing trend analyses are significant. In dust-free regions, the product would always report non-zero dust AOD, inflating global or regional averages. I suggest the authors consider whether a bias correction or an additional quality flag (e.g., a flag indicating that the retrieval has not moved meaningfully from the a priori) could be included in future data versions.
- The MAPIR retrieval uses a single set of dust optical properties: a log-normal size distribution with 0.6 µm mean radius, a refractive index from GEISA–HITRAN, and spherical particles (Sect. 2.1.2). However, previous studies have shown that there are significant global variations of dust size distribution (Formenti and Di Biagio, 2024) and dust TIR refractive index (Di Biagio et al., 2017). It is suggested to add discussions on whether the use of regionally varying refractive indices (as done by the Di Biagio et al. dust CRI database) could improve the retrieval, at least as a future development. If such sensitivity tests have already been performed for earlier MAPIR versions, referencing those results here would suffice.
Although assuming dust to be spherical has been demonstrated to have similar optical properties in TIR as in non-spherical shapes, the authors also calculate the conversion ratio to visible based on the spherical assumption. The impact of dust non-sphericity is not negligible in visible (Huang et al., 2020; Saito et al., 2021). A discussion of whether the spherical assumption introduces systematic biases in the retrieval and in the conversion factor calculation is needed.
Minor comments:
Abstract, line 5: The abstract mentions retrieval of “10 µm AOD and mean altitude” but does not mention that the evaluation is performed using the 550 nm converted AOD. Since most readers will use the 550 nm product, briefly noting the conversion and its associated uncertainty in the abstract would set appropriate expectations.
Sect. 2.1.5, lines 170–178: The surface emissivity bias in CAMEL v2 over the Sahara during summer is noted but not quantified in terms of its impact on AOD retrieval. Since the surface emissivity uncertainty analysis in Sect. 3.7 shows up to 0.05 AOD uncertainty; the authors should discuss whether this uncertainty is dominated by the CAMEL bias or by intrinsic emissivity variability. If the former, would using an alternative emissivity product (e.g., MODIS-based monthly emissivity) reduce this uncertainty?
Sect. 2.2.4, lines 285–300: The reduction from 8 to 4 DOM streams is validated with a maximum AOD difference of 0.015. It would be helpful to state the conditions under which this test was performed (e.g., dust AOD range, viewing geometry). At large viewing angles or high optical depths, the sensitivity to stream number may increase.
Sect. 3.3.3, Figure 6: The colorbar range (0.1 to 1.0) is too smooth to tell the variation. Since most stations have R > 0.6, consider using a narrower range (e.g., 0.4 to 1.0) or a colormap with limited intervals to better distinguish good from mediocre stations.
Sect. 3.9, IASI-A vs. IASI-C: The discussion of the Metop-A orbital drift (lines 793–809) is important, and the recommendation to switch to IASI-C in October 2019 is well justified. Consider providing a concrete prescription in the data usage guidelines, e.g., “For long-term analyses, use IASI-A from July 2007 to September 2019 and IASI-C from October 2019 onward.” Users would benefit from this explicit guidance rather than having to piece it together from the discussion.
References:
Capelle, V., Chédin, A., Pondrom, M., Crevoisier, C., Armante, R., Crepeau, L., and Scott, N. A.: Infrared dust aerosol optical depth retrieved daily from IASI and comparison with AERONET over the period 2007–2016, Remote Sens. Environ., 206, 15–32, https://doi.org/10.1016/j.rse.2017.12.008, 2018.
Clarisse, L., Clerbaux, C., Franco, B., Hadji-Lazaro, J., Whitburn, S., Kopp, A. K., Hurtmans, D., and Coheur, P.-F.: A decadal data set of global atmospheric dust retrieved from IASI satellite measurements, J. Geophys. Res. Atmos., 124, 1618–1647, https://doi.org/10.1029/2018JD029701, 2019.
Di Biagio, C., Formenti, P., Balkanski, Y., et al.: Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing, Atmos. Chem. Phys., 17, 1901–1929, https://doi.org/10.5194/acp-17-1901-2017, 2017.
Huang, Y., Kok, J. F., Kandler, K., Lindqvist, H., Nousiainen, T., Sakai, T., Adebiyi, A., and Jokinen, O.: Climate Models and Remote Sensing Retrievals Neglect Substantial Desert Dust Asphericity, Geophys Res Lett, 47, e2019GL086592, https://doi.org/10.1029/2019GL086592, 2020.
Saito, M., Yang, P., Ding, J., and Liu, X.: A Comprehensive Database of the Optical Properties of Irregular Aerosol Particles for Radiative Transfer Simulations, J Atmos Sci, 78, 2089-2111, 10.1175/jas-d-20-0338.1, 2021.
Zheng, J., Zhang, Z., Yu, H., Garnier, A., Song, Q., Wang, C., Di Biagio, C., Kok, J. F., Derimian, Y., and Ryder, C.: Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations, Atmos. Chem. Phys., 23, 8271–8304, https://doi.org/10.5194/acp-23-8271-2023, 2023.
Zheng, J., Yu, H., Zhou, Y., Shi, Y., Zhang, Z., Di Biagio, C., Formenti, P., and Smirnov, A.: A novel retrieval of global dust optical depth and effective diameter based on MODIS thermal infrared observations, Remote Sens. Environ., 332, 115083, https://doi.org/10.1016/j.rse.2025.115083, 2026.
Citation: https://doi.org/10.5194/egusphere-2026-924-RC2 - Separation of retrieval quality from the TIR-to-VIS conversion uncertainty. In sect. 3.3, the authors compare MAPIR converted dust AOD at 550 nm against AERONET SDA coarse-mode AOD at 500 nm, using a fixed conversion factor of 1.78. The authors mentioned that the AOD-dependent bias could be a constant bias if a different conversion factor (2.85) were used. This means that the central validation metric conflates two distinct sources of uncertainty: the quality of the 10 µm retrieval itself and the accuracy of the wavelength conversion, which depends on assumed particle size and refractive index (Capelle et al., 2018; Clarisse et al., 2019; Zheng et al., 2023, 2026).
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,030 | 466 | 61 | 1,557 | 104 | 120 |
- HTML: 1,030
- PDF: 466
- XML: 61
- Total: 1,557
- BibTeX: 104
- EndNote: 120
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This manuscript presents a comprehensive and valuable update of the MAPIR algorithm (v5.1) for dust aerosol retrieval from IASI observations. The work is technically solid, the dataset is highly relevant for the community, and the effort to build a long-term, consistent TIR dust product is particularly commendable. The paper also demonstrates a strong level of expertise and provides a thorough description of the methodology and evaluation.
However, despite these clear strengths, I have several major concerns that should be addressed before publication. In my view, some of these issues are substantial and currently limit the robustness and interpretability of the results. In particular, questions related to the treatment of spectral noise, the handling of known issues in the IASI-C dataset, and the validation strategy need to be clarified and/or strengthened.
For these reasons, I recommend major revisions. I believe that addressing the comments below would significantly strengthen the manuscript and make it a strong and impactful contribution to the field.
Major comments:
Minor comments:
Specific comments