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The TROPOspheric Monitoring Instrument (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver ALH estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV aerosol index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying ALHs. The influence of the surface and clouds is discussed, and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted. A case-by-case analysis of the data from the four selected cases (mostly around the Saharan region with approximately 800 co-located TROPOMI pixels and CALIOP profiles in June and December 2018) shows that ALHs retrieved from TROPOMI using the operational Sentinel-5 Precursor Level-2 ALH algorithm is lower than CALIOP aerosol extinction heights by approximately 0.5 km. Looking at data beyond these cases, it is clear that there is a significant difference when it comes to retrievals over land, where these differences can easily go over 1 km on average. [ABSTRACT FROM AUTHOR]

To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near-infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve the aerosol layer height for a single ground pixel. This paper proposes a forward modelling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results comparing the forward model to the neural network alternative show an encouraging outcome with good agreement between the two when they are applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the European Space Agency (ESA) Sentinel-5 Precursor mission. With an enhancement of the computational speed by 3 orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity. [ABSTRACT FROM AUTHOR]

The Tropospheric Monitoring Instrument's (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver aerosol layer height estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV absorbing index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying aerosol layer heights. The influence of the surface and clouds are discussed and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted. [ABSTRACT FROM AUTHOR]

To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve aerosol layer height for a single ground pixel. This paper proposes a forward modeling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results of comparing the forward model to the neural network alternative show encouraging results with good agreements between the two when applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the ESA Sentinel-5 Precursor mission. With an enhancement of the computational speed by three orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity. [ABSTRACT FROM AUTHOR]

The absorbing aerosol index (AAI) is a qualitative parameter directly calculated from satellite-measured reflectance. Its sensitivity to absorbing aerosols in combination with a long-term data record since 1978 makes it an important parameter for climate research. In this study, we attempt to quantify aerosol absorption by retrieving the singlescattering albedo (ω0) at 550 nm from the satellite-measured AAI. In the first part of this study, AAI sensitivity studies are presented exclusively for biomass-burning aerosols. Later on, we employ a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) in order to derive ω0 at 550 nm. Inputs for the radiative transfer calculations include satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol microphysical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMIobserved AAI of this event reached its peak. The Cloud- Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses missed the evolution of the smoke plume over the research region; therefore the aerosol profile is parameterized. The simulated plume is at an altitude of 4.5-4.9 km, which is in good agreement with available CALIOP backscatter coefficient measurements. The data may contain pixels outside the plume, so an outlier detection criterion is applied. The results show that the AAI simulated by DISAMAR is consistent with satellite observations. The correlation coefficients fall into the range between 0.85 and 0.95. The retrieved mean ω0 at 550 nm for the entire plume over the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas the nearest AERONET station reported ω0 between 0.89 and 0.92. The difference in geolocation between the AERONET site and the plume, the assumption of homogeneous plume properties, the lack of the aerosol profile information and the uncertainties in the inputs for radiative transfer calculation are primarily responsible for this discrepancy in ω0. [ABSTRACT FROM AUTHOR]

The absorbing aerosol index (AAI) based on the near Ultra-Violet (near-UV) remote sensing techniques is a qualitative parameter that allows to retrieve aerosol optical properties with confidence. In the first part of this study, a series of AAI sensitivity analysis is presented exclusively on biomass burning aerosols. Later on, this study applies a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) and to derive the aerosol single scattering albedo (ω0). The inputs for the radiative transfer calculations are satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the MODerate-resolution Imaging Spectroradiometer (MODIS), and aerosol micro-physical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI observed AAI of this event reached its peak. The Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) failed to capture the evolution of the smoke plume, therefore the aerosol profile is parameterized. The simulated plume ascends to an altitude of 4.5–4.9 km, which is in good agreement with measurements. Due to the relatively small data size of this case, an outlier detection criterion has to be applied. The results show that the AAI simulated by DISAMAR is consistent with observations. The correlation coefficients are over 0.85. The retrieved mean ω0 at 550 nm is approximately 0.84, slightly smaller than the value of 0.90 measured independently by the AERONET instrument. The relative distance between the AERONET site and the plume, the assumption of homogeneous and static plume properties, the lack of the aerosol profile information, and the uncertainties in observations are primarily responsible for this discrepancy. Except for the observational errors, the impact of remaining error sources on ω0 retrieval is difficult to quantify. [ABSTRACT FROM AUTHOR]

Retrieving aerosol optical thickness and aerosol layer height over a bright surface from measured top of atmosphere reflectance spectrum in the oxygen A-band is known to be challenging, often resulting in large errors. In certain atmospheric conditions and viewing geometries, a loss of sensitivity to aerosol optical thickness has been reported in literature. This loss of sensitivity has been attributed to a phenomenon known as critical surface albedo regime, which is a range of surface albedos for which the top of atmosphere reflectance has minimal sensitivity to aerosol optical thickness. This paper extends the concept of critical surface albedo for aerosol layer height retrievals in the oxygen A-band, and discusses its implications. The underlying physics are introduced by analysing top of atmosphere reflectance spectra obtained using a radiative transfer model. Furthermore, error analysis of the aerosol layer height retrieval algorithm are conducted over dark and bright surfaces to show the dependency on surface reflectance. The analysis shows that the information on aerosol layer height from atmospheric path contribution and the surface contribution to the top of atmosphere are opposite in sign - an increase in surface brightness results in a decrease in information content. In the case of aerosol optical thickness, these contributions are anti-correlated, leading to large retrieval errors in high surface albedo regimes. The consequence of this anti-correlation is demonstrated with measured spectra in the oxygen A-band from GOME-2A instrument on board the Metop-A satellite over the 2010 Russian wildfires incident. [ABSTRACT FROM AUTHOR]