The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles Atmospheric Measurement Techniques Year : 2019

The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation

Sieglinde Callewaert
  • Function : Author
Sophie Vandenbussche
Nicolas Kumps
  • Function : Author
Arve Kylling
  • Function : Author
Xiaoxia Shang
  • Function : Author
Mika Komppula
  • Function : Author
Martine de Mazière
  • Function : Author


The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared (TIR) radiances using Rodgers' optimal estimation method (OEM). We describe the new version 4.1 and evaluation results. Main differences with respect to previous versions are the Levenberg-Marquardt modification of the OEM, the use of the logarithm of the concentration in the retrieval and the use of Radiative Transfer for TOVS (RTTOV) for in-line radiative transfer calculations. The dust aerosol concentrations are retrieved in seven 1 km thick layers centered at 0.5 to 6.5 km. A global data set of the daily dust distribution was generated with MAPIR v4.1 covering September 2007 to June 2018, with further extensions planned every 6 months. The post-retrieval quality filters reject about 16 % of the retrievals, a huge improvement with respect to the previous versions in which up to 40 % of the retrievals were of bad quality. The median difference between the observed and fitted spectra of the good-quality retrievals is 0.32 K, with lower values over oceans. The information content of the retrieved profiles shows a dependence on the total aerosol load due to the assumption of a lognormal state vector. The median degrees of freedom in dusty scenes (min 10 µm AOD of 0.5) is 1.4. An evaluation of the aerosol optical depth (AOD) obtained from the integrated MAPIR v4.1 profiles was performed against 72 AErosol RObotic NETwork (AERONET) stations. The MAPIR AOD correlates well with the ground-based data, with a mean correlation coefficient of 0.66 and values as high as 0.88. Overall, there is a mean AOD (550 nm) positive bias of only 0.04 with respect to AERONET, which is an extremely good result. The previous versions of MAPIR were known to largely overestimate AOD (about 0.28 for v3). A second evaluation exercise was performed comparing the mean aerosol layer altitude from MAPIR with the mean dust altitude from Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). A small underestimation was found, with a mean difference of about 350 m (standard deviation of about 1 km) with respect to the CALIOP cumulative extinction altitude, which is again considered very good as the vertical resolution of MAPIR is 1 km. In the comparisons against AERONET and CALIOP, a dependence of MAPIR on the quality of the temperature profiles used in the retrieval is observed. Finally, a qualitative comparison of dust aerosol concentration profiles was done against lidar measurements from two ground-based stations (M'Bour and Al Dhaid) and from the Cloud-Aerosol Transport System (CATS) instrument on board the International Space Station (ISS). MAPIR v4.1 showed the ability to detect dust plumes at the same time and with a similar extent as the lidar instruments. This new MAPIR version shows a great improvement of the accuracy of the aerosol profile retrievals with respect to previous versions, especially so for the integrated AOD. It now offers a unique 3-D dust data set, which can be used to gain more insight into the transport and emission processes of mineral dust aerosols.
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insu-03686276 , version 1 (03-06-2022)





Sieglinde Callewaert, Sophie Vandenbussche, Nicolas Kumps, Arve Kylling, Xiaoxia Shang, et al.. The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm: version 4.1 description and evaluation. Atmospheric Measurement Techniques, 2019, 12, pp.3673-3698. ⟨10.5194/amt-12-3673-2019⟩. ⟨insu-03686276⟩
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