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The ultraviolet (UV) Absorbing Aerosol Index (AAI) is widely used as an indicator for the presence of absorbing aerosols in the atmosphere. Here we consider the TROPOMI AAI based on the 340 nm/380 nm wavelength pair. We investigate the effects of clouds on the AAI observed at small and large scales. The large-scale effects are studied using an aggregate of TROPOMI measurements over an area mostly devoid of absorbing aerosols (Pacific Ocean). The study reveals that several structural features can be distinguished in the AAI, such as the cloud bow, viewing zenith angle dependence, sunglint, and a previously unexplained increase in AAI values at extreme viewing and solar geometries. We explain these features in terms of the bidirectional reflectance distribution function (BRDF) of the scene in combination with the different ratios of diffuse and direct illumination of the surface at 340 and 380 nm. To reduce the dependency on the BRDF and homogenize the AAI distribution across the orbit, we present three different AAI retrieval models: the traditional Lambertian scene model (LSM), a Lambertian cloud model (LCM), and a scattering cloud model (SCM). We perform a model study to assess the propagation of errors in auxiliary databases used in the cloud models. The three models are then applied to the same low-aerosol region. Results show that using the LCM and SCM gives on average a higher AAI than the LSM. Additionally, a more homogeneous distribution is retrieved across the orbit. At the small scale, related to the high spatial resolution of TROPOMI, strong local increases and decreases in AAI are observed in the presence of clouds. The BRDF effect presented here is a first step – more research is needed to explain the small-scale cloud effects on the AAI.

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.

Global mapping of satellite tropospheric NO2 vertical column density (VCD), a key gas in air quality monitoring, requires accurate retrievals over complex urban and industrialized areas and under any atmospheric conditions. The high abundance of aerosol particles in regions dominated by anthropogenic fossil fuel combustion, e.g. megacities, and/or biomass-burning episodes, affects the space-borne spectral measurement. Minimizing the tropospheric NO2 VCD biases caused by aerosol scattering and absorption effects is one of the main retrieval challenges from air quality satellite instruments. In this study, the reference Ozone Monitoring Instrument (OMI) DOMINO-v2 product was reprocessed over cloud-free scenes, by applying new aerosol correction parameters retrieved from the 477 nm O2−O2 band, over eastern China and South America for 2 years (2006–2007). These new parameters are based on two different and separate algorithms developed during the last 2 years in view of an improved use of the OMI 477 nm O2−O2 band: the updated OMCLDO2 algorithm, which derives improved effective cloud parameters, the aerosol neural network (NN), which retrieves explicit aerosol parameters by assuming a more physical aerosol model. The OMI aerosol NN is a step ahead of OMCLDO2 because it primarily estimates an explicit aerosol layer height (ALH), and secondly an aerosol optical thickness τ for cloud-free observations. Overall, it was found that all the considered aerosol correction parameters reduce the biases identified in DOMINO-v2 over scenes in China with high aerosol abundance dominated by fine scattering and weakly absorbing particles, e.g. from [ - 20 % : - 40 % ] to [ 0 %:20 % ] in summertime. The use of the retrieved OMI aerosol parameters leads in general to a more explicit aerosol correction and higher tropospheric NO2 VCD values, in the range of [ 0 %:40 % ], than from the implicit correction with the updated OMCLDO2. This number overall represents an estimation of the aerosol correction strategy uncertainty nowadays for tropospheric NO2 VCD retrieval from space-borne visible measurements. The explicit aerosol correction theoretically includes a more realistic consideration of aerosol multiple scattering and absorption effects, especially over scenes dominated by strongly absorbing particles, where the correction based on OMCLDO2 seems to remain insufficient. However, the use of ALH and τ from the OMI NN aerosol algorithm is not a straightforward operation and future studies are required to identify the optimal methodology. For that purpose, several elements are recommended in this paper. Overall, we demonstrate the possibility of applying a more explicit aerosol correction by considering aerosol parameters directly derived from the 477 nm O2−O2 spectral band, measured by the same satellite instrument. Such an approach can, in theory, easily be transposed to the new-generation of space-borne instruments (e.g. TROPOMI on board Sentinel-5 Precursor), enabling a fast reprocessing of tropospheric NO2 data over cloud-free scenes (cloudy pixels need to be filtered out), as well as for other trace gas retrievals (e.g. SO2 , HCHO ).

The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties on these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage to fill this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO) from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2 / ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g. high temperature flaming vs. low temperature smoldering combustion) and burning practice (e.g. the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise of the retrieved ΔXNO2 / ΔXCO signals were quantified with WRF-CHEM experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper northern hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights on regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.

The environmental effects of absorbing aerosols are complex: they warm the surface and the atmosphere on a large scale, while locally they cool the surface. Absorbing aerosols also affect precipitation and cloud formation. A comprehensive understanding of aerosol absorption is important to reduce the uncertainties in aerosol radiative forcing assessments. The ultraviolet aerosol index (UVAI) is a qualitative measure of aerosol absorption provided by multiple satellite missions since 1978. UVAI is directly calculated by the difference between the measured spectral contrast and the simulated ones in the near-UV channel, without assumptions on aerosol properties. This long-term global daily data set is advantageous for many applications. In previous work, we have attempted to derive the single scattering albedo (SSA) from UVAI. In this work, we evaluate the UVAI derived from a chemistry transport model (CTM) with satellite observations. Conventionally, UVAI from a model aerosol fields at a satellite footprint is simulated using a radiative transfer model. In order to do this, one has to make assumptions on the spectral dependence of the aerosol optical properties. The lack of measurements and our poor knowledge of these properties may lead to large uncertainties in the simulated UVAI, and these uncertainties are difficult to quantify. In this work, we propose an alternative method, that is to simulate the UVAI based on Machine Learning (ML) approaches. A training data set is constructed by independent measurements and/or model simulations with strict quality controls. We simulate the UVAI using modelled aerosol properties, the Sun-satellite geometry and the surface parameters. The discrepancy between the retrieved UVAI and the ML predictions can help us to identify the unrealistic inputs of aerosol absorption in the model.

Aerosol vertical distributions are important for aerosol radiative forcing assessments and atmospheric remote sensing research. From our perspective, the aerosol layer height (ALH) is one of the major concerns in quantifying aerosol absorption from the ultra-violet aerosol index (UVAI). The UVAI has a global daily record since 1978, whereas a corresponding ALH data set is still limited. In this paper, we attempted to construct such an ALH data set from aerosol extinction profiles provided by the MERRA-2 aerosol reanalysis, meanwhile we evaluated them, together with several satellite ALH products in terms of the UVAI sensitivity to ALH. In the first part of this paper, we derived ALHs from the MERRA-2 aerosol profiles by four definitions. Through the sensitivity studies, we found that the definition of top boundary aerosol layer height (Haert) is more robust to the changes in extinction profile properties than others. The spatial and temporal variation of Haert are also well associated with the major aerosol sources and the atmospheric dynamics. In the second part, we further evaluated the UVAI altitude dependence on the MERRA-2 ALH as well as several satellite ALH. Among all the satellite ALH products in this paper, the correlation between the TROPOMI oxygen (O2) A-band ALH and UVAI, and that between the GOME-2 absorbing aerosol layer height (AAH) and UVAI are in agreement with our a-priori knowledge that the altitude dependence of UVAI increases with aerosol loadings. The correlation between the MERRA-2 Haert and UVAI also matches well with what we found from observational data sets. This implies the top boundary of the aerosol layer derived from MERRA-2 can be an alternative in case there is no observational ALH data available for quantitively aerosol absorption from UVAI and other UVAI-related applications.

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 single-scattering 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 OMI-observed 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.

This paper presents an exploratory study on the aerosol layer height (ALH) retrieval from the OMI 477 nm O2 − O2 spectral band. We have developed algorithms based on the multilayer perceptron (MLP) neural network (NN) approach and applied them to 3-year (2005–2007) OMI cloud-free scenes over north-east Asia, collocated with MODIS Aqua aerosol product. In addition to the importance of aerosol altitude for climate and air quality objectives, our long-term motivation is to evaluate the possibility of retrieving ALH for potential future improvements of trace gas retrievals (e.g. NO2, HCHO, SO2) from UV–visible air quality satellite measurements over scenes including high aerosol concentrations. This study presents a first step of this long-term objective and evaluates, from a statistic point of view, an ensemble of OMI ALH retrievals over a long time period of 3 years covering a large industrialized continental region. This ALH retrieval relies on the analysis of the O2 − O2 slant column density (SCD) and requires an accurate knowledge of the aerosol optical thickness, τ. Using MODIS Aqua τ(550 nm) as a prior information, absolute seasonal differences between the LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS) and average OMI ALH, over scenes with MODIS τ(550 nm) ≥ 1. 0, are in the range of 260–800 m (assuming single scattering albedo ω0 = 0. 95) and 180–310 m (assuming ω0 = 0. 9). OMI ALH retrievals depend on the assumed aerosol single scattering albedo (sensitivity up to 660 m) and the chosen surface albedo (variation less than 200 m between OMLER and MODIS black-sky albedo). Scenes with τ ≤ 0. 5 are expected to show too large biases due to the little impact of particles on the O2 − O2 SCD changes. In addition, NN algorithms also enable aerosol optical thickness retrieval by exploring the OMI reflectance in the continuum. Comparisons with collocated MODIS Aqua show agreements between −0. 02 ± 0. 45 and −0. 18 ± 0. 24, depending on the season. Improvements may be obtained from a better knowledge of the surface albedo and higher accuracy of the aerosol model. Following the previous work over ocean of Park et al.(2016), our study shows the first encouraging aerosol layer height retrieval results over land from satellite observations of the 477 nm O2 − O2 absorption spectral band.

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.

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.

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.

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 nm–770 nm) over bright surfaces. A property of the measurement error covariance matrix is discussed, due to which photons traveling 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.

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).

A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2−O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2−O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.

This paper presents the retrieval method that was developed to derive tropospheric NO2 columns from UV/VIS spectral measurements obtained with the Spectrolite Breadboard Instrument during the AROMAPEX campaign in Berlin (April 2016). A typical DOAS retrieval approach is followed. For the calculation of air mass factors this study specifically focuses on the impact of the surface reflectance, which varies considerably from pixel to pixel over this urban region. Ground-based aerosol optical thickness measurements are used as prior information. It is shown that retrieved surface reflectance shows good agreement with those derived from Landsat 8 measurements performed on the same day. Furthermore we demonstrate that tropospheric NO2 columns retrieved for pairs of adjacent pixels are self-consistent in the sense that they do not show a substantial systematic dependence on surface reflectance, in contrast to differential slant column densities. Also some cases are identified to illustrate this on a pixel-by-pixel level. An error budget is provided to quantify the impact of various assumptions on the accuracy of the retrieval of surface reflectance and tropospheric NO2 columns. Both in the morning and afternoon flight a NO2 plume is observed stretching out over Berlin from West to East. Peak values between 15 × 1015 and 20 × 1015 molec/cm2 are detected, whereas – at much lower spatial resolution – OMI detects peak values between 9 × 1015 (first overpass) and 4 × 1015 molec/cm2 (second overpass).

We present a sensitivity analysis of the tropospheric NO2 retrieval from the Ozone Monitoring Instrument (OMI) using measurements from the Dutch Aerosol and Nitrogen Dioxide Experiments for Validation of OMI and SCIAMACHY (DANDELIONS) and Intercontinental Chemical Transport Experiment-B (INTEX-B) campaigns held in 2006. These unique campaigns covered a wide range of pollution conditions and provided detailed information on the vertical distribution of NO2. During the DANDELIONS campaign, tropospheric NO2 profiles were measured with a lidar in a highly polluted region of the Netherlands. During the INTEX-B campaign, NO2 profiles were measured using laser-induced fluorescence onboard an aircraft in a range of meteorological and polluted conditions over the Gulf of Mexico and the east Pacific. We present a comparison of measured profiles with a priori profiles used in the OMI tropospheric NO2 retrieval algorithm. We examine how improvements in surface albedo estimates improve the OMI NO2 retrieval. From these comparisons we find that the absolute average change in tropospheric columns retrieved with measured profiles and improved surface albedos is 23% with a standard deviation of 27% and no trend in the improved being larger or smaller than the original. We show that these changes occur in case studies related to pollution in the southeastern United States and pollution outflow in the Gulf of Mexico. We also examine the effects of using improved Mexico City terrain heights on the OMI NO2 product.

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e. dust aerosol, aerosol absorption)., SCOPUS: ar.j, info:eu-repo/semantics/published

A new aerosol retrieval algorithm is presented which computes the spectral optical depth over the ocean from spaceborne radiometers. It includes both multiple scattering and the bi-directional reflectance of the ocean surface. The previous termalgorithmnext term is applied to data from the Along Track Scanning Radiometer 2 (ATSR-2). This radiometer aboard the ERS-2 satellite has 4 bands in the visible and near-infrared. The ATSR-2 has a dual view capability: the reflectance is measured both at nadir and at a forward angle of approximately 55° along track. This feature is used to test the algorithm by comparing independent retrievals from the forward and the nadir view, applied to Southern Hemisphere data from 23 July 1995. The retrieved aerosol optical depths compare favorably. The retrieved aerosol optical depths and spectral behavior are in agreement with expected values in clean marine environments.

A new aerosol optical depth retrieval algorithm is presented that uses the two-angle view capability of the Along Track Scanning Radiometer 2 (ATSR-2). By combining the two-angle view and the spectral information this so-called dual view algorithm separates between aerosol and surface contributions to the top of the atmosphere radiance. First validation of the dual view algorithm was performed during the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX), which was conducted at the mid-Atlantic coast of the United States in July 1996. The satellite retrieved spectral aerosol optical depth is in good agreement with the aerosol optical depth from ground-based Sun/sky radiometers in three out of four cases. This shows the potential of aerosol retrieval over land using two-angle view satellite radiometry. Copyright 1998 by the American Geophysical Union.

Crystallisation of laboratory aerosols of ammonium nitrate and of internal mixtures of this salt with ammonium sulphate were investigated using humidity controlled nephelometry. The aerosol was produced via nebulizing of solutions and then dried to 25% RH, which is a realistic minimum value for ambient air. Pure ammonium nitrate and aerosols consisting of internal mixtures of ammonium nitrate and ammonium sulphate with a mixing ratio of more than 1.2 did not crystallize, which is in contrast to assumptions that crystallisation of such mixtures occurs in the atmosphere. Addition of realistic amounts of 'soot' did not promote crystallisation. Aerosols with equal ratios of ammonium nitrate to ammonium sulphate crystallized and showed a (first) deliquescence point close to that of ammonium nitrate. Ambient aerosol with a similar mixing ratio behaved similarly. Because the laboratory tests demonstrated that the crystallisation of ammonium nitrate is not promoted by soot but only by the ammonium sulphate, it is concluded that the ambient aerosol particles consisted of internal mixtures of ammonium nitrate and ammonium sulphate.

Satellite retrieved aerosol optical properties are compared to aircraft measurements for a case study during the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). Two satellite instruments are used: the Along Track Scanning Radiometer 2 (ATSR-2) and the advanced very high resolution radiometer (AVHRR). The aerosol optical depth in the mid-visible (0.555 μm) retrieved from the ATSR-2 data agrees within 0.03 with colocated sunphotometer measurements. Also, the spectral behavior of the aerosol optical depth is retrieved accurately. Good correlation is found between aerosol optical depths for AVHRR channel 1 (0.64 μm) and sunphotometer derived values, but the satellite retrieved values are 0.05 to 0.15 lower. The Angstrom wavelength exponent is determined both from the ATSR-2 and the AVHRR data. The ATSR-2 derived Angstrom exponents are in good agreement with the values computed from the sunphotometer data. The Angstrom exponents determined from AVHRR data show very large variations. Both the ATSR-2 and the AVHRR aerosol optical depth images show a large gradient. Vertical profile data of temperature, relative humidity, and particle scattering indicate that this gradient is probably caused by changes in the dry aerosol properties, rather than a change in the relative humidity. Copyright 1999 by the American Geophysical Union.