Aerosols are the source of the largest uncertainties in our climate models, blurring our outlook of the future. This has been attributed to the complexity of measuring their properties, which vary over time and space. Atmospheric circulation spreads aerosols across the globe from a point source, which makes satellite-based observations lucrative. At present, there are several aerosol observing missions that deliver aerosol data products in a consistent and operational manner; these missions report several aerosol properties that are important for reducing the contribution of uncertainties to our climate models. What is missing, however, is an operational data product that measures the height of these aerosols at a global scale. Earlier attempts at this use data derived from lidar instruments in space; an example being the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) instrument, which uses lasers to measure atmospheric composition. In the case of aerosols, the amount of backscattered electromagnetic radiation at each atmospheric layer gives an idea of the amount and height of aerosols. The mobility afforded by space-based instruments gives space lidars a leg up over ground-based lidars. However, the coverage of such lidar instruments is merely near-global. This has to do with the fact that while lidars in space can circle the entire globe, their footprint on the ground is very narrow, in the order of several hundred meters to a few kilometers: this is an inherent limitation of the measurement principle. Consequently, a specific patch on Earth is revisited in periods that can range several days. An alternative to space based atmospheric lidars are space based spectral imagers. These are essentially cameras that take snapshots of the Earth, capturing the light and splitting its different electromagnetic frequencies into the scale of nanometers using very precise prisms and detection techniques. The advantage of these instruments over lidars is that they have a very large footprint, covering several thousand kilometers of area in a single _y-by. This allows for daily to even sub-daily coverage of the Earth, as each snapshot covers larger and sometimes overlapping areas. The challenge is to estimate aerosol height using spectral signatures of the Earth’s atmosphere in an operational environment that can handle data coming in from the satellite at a rate of several million pixels every few minutes. This dissertation focuses on delivering the aerosol height data product operationally using computer algorithms. The logic of aerosol height estimation using these so-called spectral snapshots of the atmosphere differ from that using lidars; the instrument does not provide data for different atmospheric layers. This has to be inferred using the chemistry of the oxygen molecule. O2, the second most abundant gas in our atmosphere, has a unique spectral signature in the near-infrared region, comprising of electromagnetic radiation around 765 nm. The chemical structure of the oxygen molecule allows it to absorb some of these radiations, creating a structure of absorption bands. This spectral signature deepens as more light is absorbed by the oxygen: this happens as photons penetrate deeper and deeper into the earth’s atmosphere, unless they hit a barrier. If the photons bounce back from an aerosol layer at a very high altitude, the amount of absorption by oxygen would be low. This ‘depth’ of absorption gives clues on how high an aerosol layer might be present. Computer models can reconstruct this oxygen absorption structure onto a simulated spectrum. One of the control parameters within the model is the height of an aerosol layer. The generated spectral signature of a simulated atmosphere resembling the atmosphere of a pixel in the snapshot from space-based hyperspectral imagers is then compared to the measured spectral signature. This usually results in a non-zero difference, which is caused by errors in the model. These errors can be minimised by using computer algorithms and mathematical information retrieval techniques, resulting in a modeled atmosphere closer to the measurement by changing the height of the aerosol layer, resulting in an aerosol height estimate. In this dissertation, computer algorithms inspired from mathematical models of brain neural networks as well as information retrieval techniques such as least squares are used…@en

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 0.5km. 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 1km on average.

@en

The purpose of this study is to demonstrate the role of aerosol layer height (ALH) in quantifying the single scattering albedo (SSA) from ultraviolet satellite observations for biomass burning aerosols. In the first experiment, we retrieve SSA by minimizing the near-ultraviolet (near-UV) absorbing aerosol index (UVAI) difference between observed values and those simulated by a radiative transfer model. With the recently released S-5P TROPOMI ALH product constraining forward simulations, a significant gap in the retrieved SSA (0.25) is found between radiative transfer simulations with spectral flat aerosols and those with strong spectrally dependent aerosols, implying that inappropriate assumptions regarding aerosol absorption spectral dependence may cause severe misinterpretations of the aerosol absorption. In the second part of this paper, we propose an alternative method to retrieve SSA based on a long-term record of co-located satellite and ground-based measurements using the support vector regression (SVR) approach. This empirical method is free from the uncertainties due to the imperfection of a priori assumptions on aerosol microphysics seen in the first experiment. We present the potential capabilities of SVR using several fire events that have occurred in recent years. For all cases, the difference between SVR-retrieved SSA and AERONET are generally within ±0:05, and over half of the samples are within ±0:03. The results are encouraging, although in the current phase the model tends to overestimate the SSA for relatively absorbing cases and fails to predict SSA for some extreme situations. The spatial contrast in SSA retrieved by radiative transfer simulations is significantly higher than that retrieved by SVR, and the latter better agrees with SSA from MERRA-2 reanalysis. In the future, more sophisticated feature selection procedures and kernel functions should be taken into consideration to improve the SVR model accuracy. Moreover, the high-resolution TROPOMI UVAI and co-located ALH products will guide us to more reliable training data sets and more powerful algorithms to quantify aerosol absorption from UVAI records.

@en

This paper presents a weighted least squares approach to retrieve aerosol layer height from top-of-atmosphere reflectance measurements in the oxygen A band (758-770g nm) over bright surfaces. A property of the measurement error covariance matrix is discussed, due to which photons travelling from the surface are given a higher preference over photons that scatter back from the aerosol layer. This is a potential source of biases in the estimation of aerosol properties over land, which can be mitigated by revisiting the design of the measurement error covariance matrix. The alternative proposed in this paper, which we call the dynamic scaling method, introduces a scene-dependent and wavelength-dependent modification in the measurement signal-to-noise ratio in order to influence this matrix. This method is generally applicable to other retrieval algorithms using weighted least squares. To test this method, synthetic experiments are done in addition to application to GOME-2A and GOME-2B measurements of the oxygen A band over the August 2010 Russian wildfires and the October 2017 Portugal wildfire plume over western Europe.

@en

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 the 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 the top-of-atmosphere reflectance spectrum as a sum of atmospheric path contribution and surface contribution, obtained using a radiative transfer model. Furthermore, error analysis of an aerosol layer height retrieval algorithm is conducted over dark and bright surfaces to show the dependence on surface reflectance. The analysis shows that the derivative with respect to aerosol layer height of the atmospheric path contribution to the top-of-atmosphere reflectance is opposite in sign to that of the surface contribution - an increase in surface brightness results in a decrease in information content. In the case of aerosol optical thickness, these derivatives 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 the GOME-2 instrument on board the Metop-A satellite over the 2010 Russian wildfires incident.@en