ALBERT

Description: Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol-layer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP-derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

Description: Aerosol layer height is an essential parameter to understand the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol layer height as derived from passive thermal and solar satellite sensors measurements, have been compared with aerosol layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust layer height was estimated from thermal IASI measurements by four different algorithms (BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust layer height from the CALIOP data. Thus two CALIOP derived layer heights were used: the cumulative extinction height defined to be the height where the CALIOP extinction column is half of the total extinction column; and the geometric mean height which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing to geometric mean height there is a shift by −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.4 km. When looking at different conditions (day, night, land, ocean) there are is more detail in variabilities (e.g. all algorithms overestimate more at night than at day).

Description: Mineral dust aerosols are a key player in the climate system. Their emissions are not yet characterised enough to ensure their good representation in climate models. The work presented here aims at a better characterisation of dust sources by a new analysis method. We use the three-dimensional dust aerosols distribution from the Infrared Atmospheric Sounding Interferometer (IASI), obtained with the Mineral Aerosols Profiling from Infrared Radiances (MAPIR) algorithm. The availability of vertical information on top of total column information allows to better separate emissions from transport. However, the presence of dust at the surface could also be due to low altitude transport, or to deposition processes. Therefore, to strengthen the analysis, we have completed it with an analysis of wind speed and surface state parameters (land cover, vegetation, moisture). For the more complex case of the Sahel, we have also analysed the soil type and the wind direction patterns. Our analysis highlights the well-known Saharan hot-spots, but also a less well-known significant emission place west of the Bodélé depression. The study of Sahel dust sources is a new feature for satellite-based analyses. Our results are coherent with those drawn from local ground-based measurements, allowing to extend our analysis to the entire Sahel area with confidence. We also provide a morning versus evening comparison, helping to distinguish the different emission mechanisms in play, and a small year-to-year variation analysis.

Description: The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm retrieves vertical dust concentration profiles from cloud-free IASI thermal infrared (TIR) radiances using the Rodgers Optimal Estimation Method (OEM). We describe the new version 4.1 and validation 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 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 six months. The post-retrieval quality filters reject about 16 % of the retrievals, a huge improvement with respect to the previous versions where 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 dependency on the total aerosol load due to the assumption of a log-normal state vector. The median degrees of freedom in dusty scenes (min 10 µm AOD of 0.5) is 1.4. A validation of the aerosol optical depth (AOD) obtained from the integrated MAPIR v4.1 profiles was performed against 72 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 (500 nm) negative 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 validation exercise was performed comparing the mean aerosol layer altitude from MAPIR with the mean dust altitude from 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 dependency 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 CATS instrument onboard the 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 in the transport and emission processes of mineral dust aerosols.

Description: 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.

Description: Mineral dust aerosol is a key player in the climate system. Determining dust sources and the spatio-temporal variability of dust emission fluxes is essential for estimating the impact of dust on the atmospheric radiation budget, cloud and precipitation formation processes, the bio-productivity and, ultimately, the carbon cycle. Although much effort has been put into determining dust sources from satellite observations, geo-locating active dust sources is still challenging and uncertainties in space and time are evident. One major source of uncertainty is the lack of clear differentiation between near-source dust aerosol and transported dust aerosol. In order to reduce this uncertainty, we use 3D information on the distribution of dust aerosol suspended in the atmosphere calculated from spectral measurements obtained by the Infrared Atmospheric Sounding Interferometer (IASI) by using the Mineral Aerosols Profiling from Infrared Radiance (MAPIR) algorithm. In addition to standard dust products from satellite observations, which provide 2D information on the horizontal distribution of dust, MAPIR allows for the retrieval of additional information on the vertical distribution of dust plumes. This ultimately enables us to separate between near-source and transported dust plumes. Combined with information on near-surface wind speed and surface properties, low-altitude dust plumes can be assigned to dust emission events and low-altitude transport regimes can be excluded. Consequently, this technique will reduce the uncertainty in automatically geo-locating active dust sources. The findings of our study illustrate the spatio-temporal distribution of North African dust sources based on 9 years of data, allowing for the observation of a full seasonal cycle of dust emissions, differentiating morning and afternoon/evening emissions and providing a first glance at long-term changes. In addition, we compare the results of this new method to the results from Schepanski et al. (2012), who manually identified dust sources from Spinning Enhanced Visible and InfraRed Imager (SEVIRI) red–green–blue (RGB) images. The comparison illustrates that each method has its strengths and weaknesses that must be taken into account when using the results. This study is of particular importance for understanding future environmental changes due to a changing climate.