| 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.
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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.
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