IASI is a Michelson Interferometer developed by the Centre National d'Études Spatiales (CNES) in collaboration with the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Mounted on the MetOp series of polar-orbiting satellites, IASI is designed to measure atmospheric temperature and humidity profiles with high accuracy and resolution, for use in weather forecast models. Its data has in addition been used to retrieve numerous other atmospheric gases such as carbon dioxide, methane, nitrous oxide, ozone, carbon monoxide and many other traces gases e.g.. Finally, IASI is also used to retrieve mineral aerosol (desert dust and volcanic ash) atmospheric load, mean altitude or vertical profile, particle composition and size e.g..

The IASI instruments fly onboard the Metop satellite platforms on a polar sun-synchronous orbit approximately 817 km above Earth, with Equator crossing times at 09:30 (descending mode) and 21:30 (ascending mode) mean local solar time, allowing for nearly global coverage twice daily per instrument. IASI operates as a nadir-viewing instrument with a swath width of 2200 km, capable of off-nadir measurements up to a 48.3° viewing angle on either side of the satellite track. It scans 30 elementary fields of view, each consisting of four circular pixels with a 12 km ground diameter at nadir, which extends to an ellipse of 39 km by 20 km at the swath's edges. IASI measures radiances across a spectral range from 645 to 2760 cm⁻¹, with a spectral resolution of 0.5 cm⁻¹ after apodization and a radiometric noise of about 0.2 K in the TIR atmospheric window , used in MAPIR retrievals. Each spectrum is sampled every 0.25 cm⁻¹, yielding a total of 8461 radiance channels.

IASI data is perfectly suited for both long-term and NRT monitoring of the atmosphere, with continuous data since 2007 through three identical very stable IASI instruments launched in 2006, 2012 and 2018, one new generation instrument (IASI-NG) launched in summer 2025 and two others planned for further continuation of the observations until at least 2040 .

In this work, we use data from IASI/Metop-A (in short, IASI-A, data from July 2007 to September 2021) and IASI/Metop-C (in short, IASI-C, data since October 2019, including almost 2 years overlap with IASI-A). For computing time mitigation, we do not use data from IASI/Metop-B which overlaps with the other instruments. For IASI-A we use level 1c radiance from the IASI Principal Components Scores (PCS) Fundamental Data Record (FDR) Release 1 , and surface temperature, atmospheric temperature and humidity profiles, land flag and surface elevation from the IASI All Sky Temperature and Humidity Profiles – Climate Data Record (CDR) Release 1.1 . For IASI-C, we use the similar level 1c PCS from the NRT data and surface temperature, atmospheric temperature and humidity profiles, land flag and surface elevation from the NRT Combined Sounding product, both in the “native” file format, delivered with a small delay through the EUMETSAT Data Store . Those will further be referred to as OFL (“offline”) for shortness. From that data, we select surface temperature and atmospheric temperature and humidity obtained with the Piece Wise Linear Regression version 3 (PWLR3) algorithm. This ensures consistency with IASI-A, as the PWLR3 algorithm was used for reprocessing the IASI-A CDR. Table summarizes all this information.

There has been an update on 30 March 2023 in the NRT PCS, with the use of a new eigenvector basis, improved noise filtering and the launch of the hybrid PCS (now including local PCS and eigenvectors to better catch unexpected local spectral signatures not well represented in the global PCS and eigenvectors). We currently do not use those additional local PCS as the dust signature is well represented in the global eigenvector basis. This update on 30 March 2023 represents the only discontinuity in the complete processing chain for the new MAPIR version 5.1, and bears no detectable impact (see Sect. ).

The setup described above refers to the MAPIR-climate setup, which is analysed and validated in this work. The MAPIR-NRT setup is based on the same algorithm, but uses the NRT-distributed IASI data, in which the level 2 is slightly different from the OFL data. Indeed, in the OFL data both the PWLR3 and OEM results are provided, while in NRT only one set of data is provided: the OEM if it succeeded, and the PWLR3 if the OEM failed. The MAPIR-NRT data are therefore slightly different from the MAPIR-climate data, and will contain inconsistencies when the IASI OEM is modified. The MAPIR-NRT data is planned for NRT distribution through the EUMETCast system, within work from the EUMETSAT Atmospheric Composition Satellite Application Facility (AC SAF), and will be validated in that context when enough data will have been generated.

The dust aerosol properties are fixed throughout time and space in MAPIR retrievals. The algorithm uses spherical particles, with the GEISA-HITRAN dust-like Refractive Index (RI) and a mono-modal log-normal Particle Size Distribution (PSD) with mean radius of 0.6 µm, geometric standard deviation of 2 (corresponding to an effective particle size of 2 µm). Those properties are used in a Mie code to calculate the particle properties required for the radiative transfer code (see Sect. ) and for conversion between TIR and VIS (see Sect. and ). The impact of the spherical assumption is expected to be minimal in the TIR, while known significant in VIS and therefore also in any comparison or conversion between TIR and VIS obtained dust properties/amounts. This is discussed in more details in Sect. .

The LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies) database utilizes data from the CALIOP instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite, to provide a monthly average of high-resolution (60 m) vertical profiles of dust extinction at visible wavelengths, globally on a 1° × 1° horizontal grid, from 2007 to 2014 .

This data is used as a priori for MAPIR dust profile retrievals, after some pre-treatment. First, the high-resolution extinction profiles are regridded to the low resolution MAPIR retrieval vertical grid (10 layers, 1 km thick, from 0 to 10 km altitude). Second, the monthly data are averaged over the 8 years to obtain a mean monthly data set. Then, to assure having data in each grid cell and continuity between adjacent cells, a running mean of the data set is calculated along 5° × 5°. Finally, the extinction at 532 nm is converted to concentration (particles cm⁻³) using a Mie code and the same dust particle optical properties used in the retrieval (see Sect. ) and the obtained concentrations in each layer are clipped to the minimum value of 2 particle cm⁻³ and a maximum value of 100 particles cm⁻³. This minimal concentration is new in MAPIR version 5.1 and its justification is provided in Sect. .

Radiative Transfer for TOVS (TIROS Operational Vertical Sounder) or RTTOV is a fast radiative transfer model (RTM) developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF) . It is designed to simulate satellite radiances for various atmospheric, surface, and cloud conditions in the infrared, visible, and microwave spectral regions. RTTOV uses pre-computed transmittance coefficients to efficiently calculate radiative transfer. The model also allows computing derivatives of the radiance with respect to some atmospheric gases. Finally, the model allows including aerosols with user-defined optical properties, and computing derivatives of the radiance with respect to the aerosol concentration.

In this work, RTTOV version 13 is used for all radiative transfer computations, with the corresponding IASI version 13 predictor coefficients calculated on 101 levels . Only water vapour is set as variable gas (mandatory) and its vertical profiles originate from the IASI level 2 data described in Sect. and interpolated on the internal RTTOV grid. All other gases have only low absorption in the MAPIR spectral bands e.g. and are modelled with fixed concentrations using the RTTOV defaults values as described in ). The specific aerosol properties used in this work are described in Sect. . No solar sources are included (irrelevant in the TIR). Scattering is computed using the Discrete Ordinate Method (DOM) with 4 streams. The plane parallel approximation is used and the surface is modelled as lambertian, as both are mandatory for using the DOM in RTTOV. Vertical profile interpolation, if needed, is done using the RTTOV default method. All input data are tested before running RTTOV. When needed, profiles are extrapolated up to top of atmosphere by maintaining the ratio to the default RTTOV profile instead of repeating the last known value. Concentrations are clipped to limits for the coefficient regression. All other RTTOV options are left to their default value.

In this work, we use surface emissivity internally available in RTTOV version 13 : (1) the IREMIS model for sea surfaces with surface wind speed set to 0 because the data is not available (impact on dust retrievals is below 0.01 AOD at 10 µm for a wind speed up to 100 km h⁻¹), and (2) the Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) climatology version 2 for land surfaces, with snow fraction set to 0 because the information is not contained in IASI data. Even with such a snow fraction forced to 0, the climatology outputs an emissivity spectrum containing the snow component in areas where and periods when snow is expected. This assumption would therefore only be wrong for dust clouds over occasional snow cover. There is an automatic satellite viewing angle correction in IREMIS (turned on). For land surface emissivity, there is a possibility in RTTOV to correct for the satellite viewing angle, but it is the output of one short study and it leads to spurious retrieval results, therefore it is not turned on in MAPIR. The sea versus land discrimination is done using the corresponding flag available in the IASI level 2 data.

The pre-processing of MAPIR v5.1 consists in reading the IASI radiances, surface temperature and atmospheric temperature and humidity profiles (described in Sect. ), then screening them using the available quality indicators or RTTOV internal boundaries. The quality indicators are slightly different depending on the source of the data (CDR for IASI-A or OFL for IASI-C). All criteria are listed in Table . All of them need to be met for a IASI observation to enter the retrieval. It is rare that observations are rejected due to these criteria (see further Sect. ).

In addition, the surface temperature and pressure and all atmospheric profiles (pressure, temperature, water vapour) must be within the acceptable boundaries for RTTOV v13 . Therefore, the surface temperature and the atmospheric profile of water vapour are internally clipped (within RTTOV – this is an input parameter) to acceptable values. On the other hand, the surface pressure and atmospheric profiles of pressure and temperature cannot be corrected with sufficient certainty and are not retrieved in MAPIR. Given their impact on the retrieval results, if one of those values exceeds the RTTOV boundaries, the IASI observation is rejected.

The retrieval scheme is based on the OEM . The state vector, containing the retrieved parameters, is composed of the vertical profile of the logarithm of the dust aerosol concentration in 10 layers centred from 0.5 to 9.5 km altitude above sea level (by steps of 1 km) and of the surface temperature. Using the logarithmic space ensures that a negative concentration is never obtained, as such unphysical conditions would not be acceptable for RTTOV. The starting value of the state vector is the same as the a priori, which comes from the LIVAS climatology for the dust concentration profile (see Sect. ), and from the IASI PWLR3 level 2 data for the surface temperature (see Sect. ).

MAPIR uses three spectral windows, on both sides of the well-known “V-shape” signature of minerals in the TIR e.g.. The first two spectral windows are highly sensitive to mineral aerosols, and also bear sensitivity to surface temperature. They range from 905 to 927 cm⁻¹ and from 1098 to 1123 cm⁻¹. The third spectral window ranges from 1202.75 to 1204.75 cm⁻¹, and is mostly sensitive to the surface temperature, with still a small sensitivity to mineral aerosols. The spectral noise is set to 0.5 K in the first two windows and 0.2 K in the third window. This is a small noise inflation with respect to the instrument spectral noise reported to be 0.2 K in the first two windows, 0.03 K in the third by or 0.25, 0.33 and 0.07 K respectively by . This so-called “noise inflation” consists of setting up the OEM with a larger spectral noise than the reported instrument noise, allowing to take into account the errors/uncertainties that can not be quantified otherwise (e.g. those related to the RTM, or to surface emissivity). The precise value is determined empirically, aiming at the lowest possible value (but never below the reported instrument noise) that allows most retrievals to converge to physically realistic values.

All radiative transfer computations are performed with RTTOV (see Sect. ). The radiance derivatives with respect to the state parameters are calculated during each retrieval main loop, to account for their non-linearity. Those derivatives are used to compute the state vector for the next iteration, following the OEM formalism.

Because of the high non-linearity of the radiative transfer with respect to the retrieved parameters, the Levenberg-Marquardt modification of the Gauss-Newton iterative method is adopted, with a γ dumping factor starting at 1, increased by a factor of 10 when the cost function has increased during an iteration, and decreased by a factor of 2 if the cost function has decreased during an iteration. The retrieval convergence is tested using the standard OEM formalism, with a threshold of 0.1 both in the state vector and the observation spaces. Most retrievals converge in less than 6 iterations, and the maximum allowed is set to 20.

Finally, averaging kernels and uncertainties are provided, following the OEM formalism and the Levenberg-Marquard modification, using equations from , as in the previous version of MAPIR . The result covariance matrix is computed separately for the uncertainties linked to the spectral noise, to the smoothing error, and to the input temperature and humidity profiles. The information content of the retrievals (degrees of freedom or DOF) is the trace of the averaging kernel matrix, and reports the number of independent pieces of information present in each single profile. In good cases (strong event and/or large temperature contrast between the surface and the dust layer), the retrievals may reach up to 2.5 DOF. They are distributed in the vertical range depending on the surface and atmospheric temperature, but also depend on the dust concentration profile because the retrieval is performed in the logarithmic space e.g..

The post-processing consists in computing the dust AOD with associated uncertainty and the dust mean altitude, both from the full vertical profile. Uncertainty covariance matrices are computed, and finally quality and cloud flags are added.

The calculation of the dust AOD from the concentration profile Cdust is simply a vertical summation of the dust layers to a dust column, which is then multiplied by the cross-section obtained from the dust PSD and RI used for the retrieval (see Sect. ). This can be formalised using the transformation operator L: a row of the same size as the vertical profile, containing a repetition of the cross-section value, multiplied by the layer height (which is currently constant in MAPIR). The AOD is then simply Cdust⋅L. The cross-section depends on the wavelength, and the dust AOD is reported both at 10 µm (close to the retrieval spectral windows, although 10 µm is not in those windows) and at 550 nm (a standard wavelength for AOD reporting). The ratio between the AOD at 550 nm and that at 10 µm is 1.78. A discussion of that conversion takes place in Sect. .

The dust AOD uncertainty UAOD is calculated from the OEM-obtained uncertainty covariance matrix S on the profile, using the transformation operator L: UAOD=L×S×L⊤. In addition, an evaluation of the AOD uncertainty due to surface emissivity uncertainty is provided in Sect. , however this added uncertainty would be very costly to quantify for each retrieval and is therefore not part of the pixel-wise reported uncertainty.

The mean altitude of the dust is calculated as the altitude for which half the dust particles are below and half are above. Obviously, this parameter is meaningless when multiple dust layers are present. In addition, the mean altitude is only reported when the dust AOD exceeds 0.05 and the information content is larger than 1.25. Lower information content means that there is not enough information in the vertical range to provide more than just an integrated vertical column, and the mean altitude usually ends up being the middle of the retrieval range, i.e. about 5 km.

The data used to build the cloud flag is again slightly different for the CDR IASI-A and the OFL IASI-C data. In the first case, only the cloud fraction is available (from the level 1 data) and the cloud-free criterion is that the fraction is less than 10 %. In the second case, the same holds true and in addition, the level 2 cloud flag must be 1 or 2 (respectively clear sky and cloud free with small contamination possible; ). This cloud flag is not used during pre- or post-processing and is only added for information.

The MAPIR data quality flag should be used to filter the retrieved data, before using them. The goal is to remove the spurious results, either due to non-convergence (reaching 20 iterations) or converging but with large spectral residuals (root-mean-square of the residuals larger than 0.75 K) or unreasonable values of the retrieved parameters (surface temperature below 200 K or above 350 K, dust AOD at 10 µm larger than 5). Those underline a high probability that even though mathematically the convergence is reached, something is wrong (which can be any of the input data, or the presence of a cloud), and the retrieval result does not reasonably reflect the reality.

Finally, a test has been added to further remove thin clouds. The dust aerosol retrieval converges on most cloudy observations, even though the spectral signature is different. If the cloud is thick, the aerosol retrieval provides a very high concentration, leading to saturation of the signal in the TIR, together with a very low surface temperature. At that point, the dust signal would be similar to a cloud. This is easy to filter out, as the retrieved parameters are unrealistic. If the cloud is thin, the retrieval provides a small dust concentration, together with a surface temperature slightly biased low. This is more complex to filter out. In the current setup, we exploit the fact that the EUMETSAT PWLR3 surface temperature (used as a priori in our retrievals, see Sect. ) is an all-sky statistical product, with good performances even under a cloud or aerosols . The MAPIR-retrieved surface temperature will on the other hand be biased low in most cloud cases (as clouds reduce the observed radiance and are not modelled in MAPIR retrievals), and should bear no bias in the dust aerosol cases (as those are correctly modelled in MAPIR retrievals). The criteria used for filtering thin clouds is therefore that the retrieved surface temperature can not be more than 2.5 K lower than the a priori surface temperature. Obviously, this filter is far from perfect and has not been validated due to the lack of required reference data. It removes some thin dust observations, while retaining some thin cloud observations. However, it performs better in that discrimination than using the IASI cloud fraction (and flag), which were wrongly flagging intense dust plumes. The latter is easily seen when analysing IASI cloud data for example during the June 2020 extreme Godzilla event (Saharan emissions transported towards America) and comparing with any so-called True Colour imagery from other satellite sensors.

A first set of changes impacts the amount of data treated by the retrieval, through the removal of almost all prefilters. This also drives other changes to reduce single retrieval computational time and improve post-retrieval filtering. In MAPIR v5.1, we have removed the pre-filtering for the presence of dust outside the dust belt, which was previously performed by looking at brightness temperature differences to detect the dust V-shaped spectral signature. This filtering was found to miss most of the low intensity events, where the spectral slopes are low, as well as special conditions where the spectral V-shaped signature is inverted (when the dust layer is hotter than the surface). In addition, the cloud filtering prior to running the retrieval has been removed because the EUMETSAT IASI cloud flag is wrongly categorizing intense or unusual dust events as clouds. It was decided to run the retrieval on all IASI observations and remove the cloud-contaminated scenes as post-processing, as detailed in Sect. . Finally, the retrieval is now running globally instead of only up to 60° latitude for version 4.1.

All these changes increase the number of retrievals to be run by an order of magnitude. To mitigate the impact on processing costs, different ways to reduce the retrieval duration were investigated. The number of streams in the multiple scattering DOM was reduced from 8 to 4, gaining about a factor of 4 in computing time. This reduction also led to slight changes in the retrieved dust AOD, which is smaller by a maximum of 0.015 at 10 µm in the performed tests. Such a small impact of the number of streams is explained by the minor contribution of scattering to the total radiative impact of dust in the TIR, largely dominated by absorption and emission. An additional upgrade leading to a slight gain computation time is a technical improvement to the whole processing chain, in particular in the pre-processing part of reading, colocating and quality filtering the IASI level 1 spectra and level 2 data. At the end, MAPIR v5.1 treats about 11 times more data, requiring only about 2.5 times more computing resources.

Further changes include:

The new algorithm version extends the vertical range of the retrieval up to 10 km altitude (it was 7 km for version 4.1), allowing to better represent the dust distribution over high altitude areas such as the Tibetan Plateau, where dust is often found e.g..

AERONET is a globally distributed network of ground-based remote sensing aerosol monitoring stations established by NASA (National Aeronautics and Space Administration) and PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire) in collaboration with numerous international partners e.g.. Utilizing sun photometers, AERONET provides continuous, high-quality, and standardized measurements of aerosol optical properties, including AOD, size distribution, and refractive index e.g..

In this work, AERONET data is used as ground-based “truth” for comparison with the IASI MAPIR dust AOD. We used the AERONET version 3 solar and lunar level 2 (cloud screened and quality assured) Spectral Deconvolution Algorithm (SDA) version 4.1 . From this data, we select the coarse mode total AOD at 500 nm (further referred to as CAOD, containing dust coarse mode aerosols and when present sea salt/marine aerosols) for comparison with the MAPIR dust AOD converted to 550 nm. All AERONET stations with median CAOD of at least 0.05 were considered in this analysis. This avoids comparing data from areas with very little dust or with mostly marine aerosols, which would then mostly be comparing noise levels.

The AERONET data is filtered to remove events for which the reported uncertainty is large, following the method initially described in : we keep only data with a reported root-mean square error lower than 0.05+0.15⋅CAOD. That filtering removes about 1/3 of the comparison points overall, especially in East Asia, but does not change the comparison statistics on a global scale. The correlation between MAPIR and AERONET improves at Asian stations (where most of the “bad” data is removed), and slightly worsens at some “transport” stations, where the variability is low, because the filtering removes a significant number of high dust load events, lowering the variability even more. Finally, the AERONET data is filtered to remove mixed events of dust and fine aerosols such as those linked to biomass burning. Indeed, when two different aerosol types are mixed the partitioning between fine and coarse mode in the SDA algorithm is more difficult and the resulting CAOD is plausibly of lower quality. The filtering is performed using the AERONET Fine Mode Fraction: when this fraction exceeds 0.5 the data is rejected. That results, for example, in about 45 % of the IASI-A against AERONET solar comparison points being rejected, mostly of low AOD. Again the global statistics of the comparisons do not change significantly with this additional filtering. Figure shows the impact of both data filters on the number of MAPIR-AERONET colocations per station, for the IASI-A full time series: from 4 % data removed at Ragged Point (Barbados) to 90 % data removed at Silpakorn University (Thailand).

Finally, stations were used only if at least 100 co-locations were found (the method is described in detail in Sect. ), leading to using the 74 stations listed in Table .

Lidar (Light Detection and Ranging) is an active remote sensing technique widely used to study the vertical distribution of atmospheric aerosols. By measuring the backscattered signals from emitted laser pulses at certain wavelengths, it can acquire information on the shape, size and concentration of atmospheric particles. When ground-based, the signal may reach altitudes up to the lower levels of the stratosphere. Lidar is the most accurate means of studying the vertical resolution of aerosols, making it an excellent tool in mineral dust e.g., satellite instrument evaluation e.g. and aerosol classification studies e.g.. The European Aerosol Research Lidar Network, EARLINET, consists of more than 30 ground-based lidar stations distributed across Europe e.g. providing harmonized and quality-assured datasets. The network ensures data consistency through the application of the Single Calculus Chain algorithm e.g., an automated processing framework that standardizes lidar data analysis. In the present study, Level 1 data from the EARLINET station of Limassol, Cyprus (CYC, 34.68° N, 33.04° E, 11 m) e.g. were used, a site strategically located in the Eastern Mediterranean, frequently affected by long-range transported mineral dust originating from the Sahara Desert and the Middle-East. The CYC station consists of a multiwavelength PollyXT lidar e.g. which performs continuous automated measurements under diverse atmospheric conditions, rendering it optimal for evaluation of polar-orbiting satellite data.

CALIOP is a nadir-pointing, dual-wavelength elastic backscatter lidar onboard the CALIPSO satellite, launched in April 2006 as part of the A-Train constellation with equator crossing time of 13:30 and 01:30 local solar time. CALIOP provides global, vertically resolved observations of aerosols by measuring backscattered laser radiation at 532 and 1064 nm . At 532 nm, CALIOP measures both parallel and perpendicular polarized backscatter, enabling the computation of particulate depolarization ratios and supporting aerosol type discrimination, while the 1064 nm channel measures total attenuated backscatter. The instrument has a nominal horizontal sampling of 333 m along track and a vertical resolution of 30–60 m in the troposphere.

This study uses CALIOP Version 4.2 Level 2 aerosol profile products. Aerosol backscatter and extinction coefficients are retrieved from calibrated attenuated backscatter profiles by removing the molecular contribution and applying aerosol-type-dependent lidar ratios. Aerosol subtyping (e.g. dust, smoke, polluted continental) in Version 4.2 is based on an improved classification algorithm that exploits layer-integrated backscatter, depolarization ratio, spectral dependence, and layer altitude . We use the CALIOP aerosol type and 532 nm extinction profiles, to qualitatively compare with IASI MAPIR dust concentration profiles. The data is quality-controlled as advised in : only data with Cloud Aerosol Discrimination (CAD) score between -100 and -20 (avoiding detection artefacts) and with extinction quality flag pertaining to successful retrievals (value of 0, 1, 16 or 18) are kept e.g..

The method used to co-locate the ground-based reference and satellite observations may have a strong influence on the comparison results. Ideally, one would wish for the smallest time and space differences, but one also requires sufficient comparison data for a reasonable statistical analysis. Finally, one can compare each single pair of co-located data (maximising the number of comparisons) or averaged data (reducing the noise in the comparisons). A short literature survey shows (Table ) different approaches in different publications/analyses.

In this work, we have tested different criteria and compared the results: co-locations distances from 25 to 150 km and times from 15 min to 1 h, single point comparisons or averages within the co-location criteria, with and without outlier removal. General conclusions remain very similar for all these. Differences are seen at some stations, with usually an obvious explanation. First, at stations located close to sources, the correlation coefficient tends to decrease when loosening the co-location constraints, attributed to the high horizontal variability of the atmospheric dust in those areas. Second, at stations with only few data, loosening the co-location constraints allows more data to enter the comparisons, leading to different statistics (sometimes better and sometimes worse). Third, at stations with little variability in the dust/coarse aerosol load, far from sources, loosening the co-location constraints improves the statistics as more events are included in the comparisons, enhancing slightly the variability and leading to a slightly better correlation coefficient.

Using mean values in the comparisons instead of comparing single observations smooths the local variability (in time and space), and might lead to misinterpretation when using loose co-location criteria. Indeed, single observation comparisons with loose criteria show a large spread of both MAPIR and AERONET data for a single event (especially close to sources where the variability is larger), while using averaged values only shows a slight correlation coefficient reduction. Having performed comparisons with all types of co-location criteria allows a better understanding of both data sets, and therefore also a better interpretation of the comparison results.

The results shown and discussed here are for maximum 25 km distance between the IASI pixel centre and the AERONET station, maximum 15 min between the observations, and single comparisons (no time or space average). This assumes that the dust aerosol field is homogenous within 25 km and 15 min. Finally, the worst 3% of the comparisons are removed from the statistics for each station, as probable outliers (due to limitations in satellite versus ground-based comparison, considering the high spatiotemporal variability of dust AOD) or cloud misclassification. However, this only minimally affects comparison statistics (e.g. MAPIR IASI-A versus AERONET solar data median bias is 0.011 or 0.012 respectively without and with the outlier removal). The correlation coefficient is reduced when not removing outlier (0.71 versus 0.78), which is expected due to the presence of additional noise in the data. The same comparisons are performed using both solar and lunar AERONET data, but statistics are computed separately as the AERONET data quality might differ between solar and lunar measurements acquired under very different circumstances and with different sensitivity. Stations for which there is less than 100 colocations are removed from the analysis.

In this section, we analyse the comparisons globally, all co-locations from all-stations grouped together. Table provides the comparison statistics for MAPIR IASI-A and C separately (dust AOD converted from 10 µm to 550 nm), and for solar and lunar AERONET observations separately (SDA CAOD). The overall correlation coefficient is almost 0.8 between both IASI and AERONET solar observations, and almost 0.7 for comparisons with AERONET lunar observations, which shows very good capability of IASI and MAPIR to observe dust events and follow their seasonal cycles. The mean absolute bias is close to 0 with a standard deviation of about 0.1, except for IASI-A against lunar AERONET where bias is larger (0.011) and with about twice the standard deviation. The median absolute bias is of about 0.01, positive for all groups except again for IASI-A against AERONET lunar where it is negative. The median relative bias is about 10 % for both IASI instruments against AERONET solar, and about 5 % against AERONET lunar. The median absolute bias is positive in all cases except IASI-A against AERONET lunar. The ratio of comparisons with a bias lower than 0.1 lies between 76 % and 81 % except again for IASI-A against AERONET lunar, where it is only 58 %. We recall here the important considerations from Sect. , which have a strong impact on absolute value comparisons while mostly not affecting correlation.

Figures and show graphically the comparison results of MAPIR converted 550 nm AOD (IASI-A and C separately) versus AERONET (solar and lunar separately). Panels (a, d) show the density plot of MAPIR against AERONET AOD, with its linear regression line and equation. Panels (b, e) show scatter plots of MAPIR bias versus AERONET and data bins with constant number of points (median and interquartile (IQR) are shown). Finally, panels (c, f) show histograms of the MAPIR bias, together with its median and quartiles. These figures show that even though the correlation, mean and median bias highlight very good performance of MAPIR v5.1, there is a clear overestimation by MAPIR v5.1 of the low 550 nm AOD values, and a linear dependence of the MAPIR 550 nm AOD bias with the AOD, the bias being positive for small AOD and becoming negative from AOD of about 0.1. The histograms also show almost Gaussian distributions of the bias, with a maximum skewed towards positive values while the median is close to zero.

The overestimation of low AOD is clearly linked to the lack of IASI sensitivity for very low AOD and therefore also exists in the 10 µm AOD data. In those cases, the deviation from the a priori during the retrieval is difficult. The a priori minimal value was set to 2 particles cm⁻³ all over the retrieval range, corresponding to a dust AOD of about 0.08 at 10 µm, or about 0.14 at 550 nm. The retrieval manages to deviate to lower values in absence of dust, but as will be seen more precisely in Sect. there is a lower threshold that the retrieval does not seem to be able to pass.

The underestimation of high 550 nm AOD, could be linked to an underestimated AOD conversion factor from TIR to VIS (see Sect. ), and the linear dependence of the bias with the AOD aligns with such a possibility. Indeed, using a TIR to VIS conversion factor of 2.85 (instead of the theoretically calculated 1.78) leads to a constant bias (0.1±0.05) of MAPIR 550 nm AOD compared to AERONET solar CAOD. Following the finding from , only a small difference in effective particle size is enough to explain such a difference in conversion factors (e.g. a conversion factor of respectively 1.78 and 2.85, in their work, corresponds to a Reff of respectively about 1.9 and 1.75 µm). Future work should include investigating variable PSD and RI in the retrieval and/or in the conversion. To validate the absolute value of the IASI dust TIR AOD and conversion factor to VIS, it would be necessary to have some reference data directly at 10 µm, which is currently not available. Therefore, these AOD comparisons are interesting as they help understand the product (and are requested by users), but the absolute value of the bias is not truly a product quality assessment. The correlation is however relevant, as it assesses relative changes and not the absolute values.

Comparisons with lunar AERONET data show similar results as for solar for IASI-C (although with much fewer points, and at much fewer stations) but significantly more noise for IASI-A, especially for high AOD. This is most probably due to unfiltered thin clouds, leading to artificially high dust MAPIR AOD in some cases even though these should not appear in the colocations with AERONET data as the latter also undergo a cloud filtering. The absence of such issues for IASI-C comparisons with lunar AERONET highlights that the IASI-C data was partially cloud-filtered before the retrieval, due to the absence of the surface altitude data for most of the cloudy pixels (see Sect. ). This unwanted filtering however also clearly removed some of the high AOD dust cases, as seen on both IASI-C comparison plots for which the last equal number bin (black, in panel c) occurs for a significantly lower AOD than for IASI-A.

Figure shows the Pearson correlation coefficient between the MAPIR IASI-A dust AOD converted to 550 nm and solar AERONET SDA CAOD at 500 nm, for the 68 stations passing the criteria detailed in Sect. . The corresponding map for IASI-C comparisons is very similar, with some different stations due to the different time frame, and a total of 41 stations. The comparisons with AERONET lunar data contain only 8 stations for IASI-A and 7 for IASI-C and the correlation coefficients are again similar, slightly lower for IASI-A as already seen in the global statistics. The limited number of lunar stations in these comparisons arises from the strong constraint of at least 100 colocations at a station to keep it in the analysis. Table summarizes all these statistics by station.

For most stations, the correlation coefficient is about 0.8, including close to sources and after long-range transport towards the Caribbean. This shows the good capability of MAPIR/IASI to identify the dust events, their relative intensity and seasonal cycles. The stations with the worst correlation coefficient (below 0.5, e.g. in Ascension Island, Arica or Manila) show very little AOD variability, and highly discontinuous time series, rendering the statistical analysis unfit in terms of correlation. The same occurs for Zinder airport lying in the Sahara but reporting very episodic data, only for strong events, rendering it again statistically unfit for extraction of a correlation coefficient. There is also a group of stations with a correlation coefficient around 0.6–0.7, lying mostly in the Middle-East. Most of them lack full seasonal cycle observations, and therefore again lack a part of the natural variability, reducing the capability to get a good correlation coefficient from noisy data.

Figure shows the time series of AERONET solar and lunar data, and of IASI-A and IASI-C data split between daytime and night-time, for two AERONET stations: Dakar Belair and Ragged Point. Those stations were selected from the few stations containing solar and lunar AERONET observations, and during the IASI-A and IASI-C overlap time, one relatively close to sources and the other after long-range transport. We emphasize that in those plots, all data are plotted and not only the colocations. This provides a lot of useful information. First, one can see a continuity and consistency between AERONET solar and lunar data, between IASI daytime and nighttime data, and between IASI-A and IASI-C. Second, MAPIR gets the seasonal cycles very well, even after long-range transport. Third, there is a minimal AOD (about 0.05 at 550 nm) under which the MAPIR algorithm does not seem to be able to go, as seen in Sect. . Fourth, for each dust event, the AERONET data contains numerous observations, spanning a range of AODs in a short time range (the vertical lines of grey points/black crosses). This obviously renders noisy the comparisons with the twice-per-day satellite data, unless a very strong time constraint is applied – and this was observed when testing the different colocation criteria. And finally, one can also see in those plots some plausible remaining unfiltered cloudy cases in the MAPIR data: the isolated high MAPIR AOD when AERONET does not show an event.

Figure shows the time series of colocations for the two same stations, separately for IASI-A and IASI-C. For Ragged Point, there are not enough lunar colocations for IASI-A, therefore only IASI-C is shown. Those plots clearly show again the nice continuity in all data sets, and the good capability of IASI-MAPIR to observe seasonal cycles. They also show the MAPIR underestimation of the 550 nm AOD larger than 0.1 and increasing underestimation when the AOD increases, as already discussed in Sect. . However, we do not see here any possible unfiltered clouds in the IASI data. This is due to the strong colocation criteria and the fact that AERONET data is also cloud-cleared, therefore the remaining cloudy MAPIR should not have any colocated AERONET data.

This section shows qualitative illustrations of the IASI MAPIR dust concentration profiles compared to the CALIOP high resolution extinction profiles. The IASI MAPIR data is averaged around every 5 CALIOP observations along its track, with a maximum 50 km distance and 6 h time difference. The second criterion ensures comparing day-time or night-time observations separately. IASI and CALIOP observations always bear a time difference of 3 to 5 h due to their respectively mid-morning (09:30) and mid-afternoon (13:30) orbits, and depending on the IASI pixel position within the cross-path swath.

On Figs. to , the CALIOP data is plotted along its track for selected relevant orbit parts, first as aerosol type (all types included), then as extinction at 532 nm for only the “dust”, “dusty marine” and “polluted dust” types. Then the IASI MAPIR data is also plotted as concentration profiles (the main retrieval output), and a daily MAPIR 10 µm AOD map is provided with the CALIOP track plotted in teal.

For the first example (Fig. ), we look at a night-time Middle-East dust plume on 28 October 2018, with MAPIR IASI-A. One can see a significant dust plume between 50 and 20° N along the selected CALIOP track, with the most intense dust extinction and concentration at about 30° N (Afghanistan/Iran border)), between 1 and 2 km altitude, with smaller dust amounts up to 4 km altitude. The IASI MAPIR dust profiles place the dust at the same location as observed with CALIOP, both horizontally and vertically. With MAPIR, dust is also observed right above the surface at about 45° N along that track, where CALIOP typing determined the presence of continental aerosols. As IASI observations are not sensitive to other aerosol types, it is well plausible that the observed aerosols are a small amount of dust, maybe with different properties than the usual desert dust.

The second example (Fig. ) is the next orbit, on the same day, showing dust over Syria, Jordan, then Egypt and the Sudan and finally in the South of the African continent. A first plume is detected with both CALIOP and MAPIR as most intense around 30° N (in Jordan), again at 1 to 2 km altitude. The plume extension towards 20° N (towards the Red Sea then Egypt and the Sudan) is more intensely observed with MAPIR than with CALIOP, which could be due to the time difference and plausibly looking at the source area. There is a second dust plume detected with MAPIR at 20–30° S (in Botswana and South Africa), for which CALIOP type is “polluted dust”, with a very low extinction, while MAPIR shows significant dust amounts.

The third example (Fig. ) is selected during an unusually intense Atlantic dust transport case, the Godzilla event in June 2020 (the same event already used for some other illustrations). The figure shows that during the event, even after long-range transport, the dust column is very intense and reaches high altitudes with respect to the usual transport altitude. Indeed, CALIOP and MAPIR concur in placing large amounts of dust up to 3–4 km altitude all across the plume's head, close to the Antillas. The CALIOP signal is even saturated and does not provide information below 2 km altitude.

These examples all show the good dust profiling capabilities with IASI data and the MAPIR algorithm, even over land or in unusual situations.

To evaluate the MAPIR dust concentration profile and altitude, the EARLINET lidar measurements are processed through a dedicated retrieval chain. First, the two-step POLIPHON (POlarization LIdar PHOtometer Networking) method is implemented in order to separate the contribution of coarse dust particles from the lidar signal at 532 nm, to best match the TIR sensitivity to coarse particles. The threshold particle depolarization values, which are an essential component of the technique, are derived from the literature . The retrieved lidar dust backscatter profiles are then regridded from the instrument’s native vertical resolution of 7.5 m to 1 km to ensure consistency with the IASI dust profile vertical grid. Lastly, the dust concentration (particlescm-3) is calculated using the following equation, proposed by : M=vτβdSdVd where vτ is the POLIPHON conversion factor for the site of Potenza, v is the volume concentration of dust, τ is the aerosol optical depth at 532 nm, βd is the coarse dust backscatter coefficient derived from POLIPHON, Sd is the lidar ratio, and Vd is the volume of a spherical dust particle with an effective radius of 1.7 µm, a value calculated from AERONET (Aerosol Robotic Network) inversion data for the Potenza site. The lidar ratio of dust is derived from nighttime Raman measurements. The dust mean altitude from the lidar observation is calculated as the median altitude of the profile, implying the altitude at which half of the dust particles are located above and the other half below, following the same approach as implemented within the MAPIR product. It should be noted that an overlap correction is also applied to the lidar data, meaning that when dust is detected at the lowest provided altitude, the concentration is assumed to remain constant down to the surface level.

In this work, two dust transport cases over the EARLINET lidar station at Potenza, Italy (40.6° N, 17.72° E, 760 m a.s.l.) , occurring during 20 June 2023 and 19 June 2024, are presented, while a more extensive analysis is currently under preparation. This station regularly receives mineral dust aerosols transported from the Sahara Desert . The specific cases presented here were identified through a synergistic approach combining lidar and AERONET observations with forecast and back-trajectory models such as the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model e.g.. For the first case, the distance between the nearest IASI pixel and the lidar station was 7.8 km, with a temporal difference of 155 min between the measurements. For the second case, the corresponding values were 10.4 km and 70 min. These spatial and temporal mismatches are considered acceptable for satellite validation studies when the reference observations do not occur at high frequency. Additionally, the MAPIR retrievals are characterized by a DOF value of 1.79 and a 550 nm dust optical depth value of 0.58 for the first case, and a DOF of 1.38 and a dust optical depth of 0.21 for the second case, both indicative of substantial dust loading.

In Fig. a, the dust concentration profiles retrieved from both instruments are shown for altitudes between 1.5 and 6 km, with the respective errors. The lidar retrieval indicates generally lower concentrations, reaching approximately 5 particles cm⁻³, with a well-defined dust layer extending from about 1.5 to 4.5 km and concentrations approaching zero above this altitude range. In contrast, MAPIR retrieves a broader vertical distribution, extending from near the surface up to approximately 5.5 km. Peak concentrations are roughly twice those observed by the lidar, exceeding 15 particles cm⁻³. The dust mean altitudes estimated from both datasets are also displayed as horizontal dashed lines. For this event, the difference between the retrieved mean altitudes is approximately 0.7 km. Overall, MAPIR reproduces the main structure of the observed dust layer reasonably well, although an overestimation of particle concentration is evident. Figure b presents the vertical profile of the absolute difference between MAPIR and lidar concentrations. The largest discrepancy occurs near 4.5 km, where differences reach approximately 12 particles cm⁻³, while smaller deviations are observed throughout the remaining profile, suggesting a generally consistent representation of the dust vertical distribution. Finally, Fig. c shows the spatial distribution of MAPIR dust mean altitude over central Italy. The location of the Potenza lidar station is marked with a star, together with a surrounding radius of 100 km. The IASI pixels are coloured according to the retrieved MAPIR dust mean altitude. Within the selected radius, most pixels report mean altitudes between approximately 1.5 and 5 km, revealing noticeable spatial variability in the vertical placement of the dust layer, thus indicating that both the spatial separation and the temporal mismatch between the two measurements may play a non-negligible role in the observed discrepancies.

Similar plots are presented in Fig. d–f. In this case, the lidar-derived coarse dust layer is less well defined, exhibiting relatively uniform dust concentrations between approximately 1.5 and 6 km altitude. The MAPIR retrievals show a similar vertical distribution, although the dust layer is less distinct than that observed by the lidar. The differences in both mean dust altitude (Fig. d) and dust concentration (Fig. e) are noticeably smaller than those observed in the first case. The spatial variability of the MAPIR-derived mean dust altitude around the station is comparable between the two cases.

Overall, these examples suggest that MAPIR is able to accurately reproduce vertical profiles with a structure similar to the ground-truth lidar observations. A slight overestimation of dust particle concentrations is observed in one case, while substantially better agreement is found in the second. Regarding mean dust altitude, both instruments provide similar values in both cases, with differences of 0.64 km and 0.8 km, respectively. Finally, it should be noted that a comprehensive multi-station validation effort is currently in preparation, which will extend the findings presented here and provide statistically robust results .