This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

This paper introduces the recent European Aerosol Research Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and five single-site intercomparison activities, 21 EARLINET systems from 18 EARLINET stations were intercompared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualified which served as traveling standards thereafter. EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607 nm. It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained. Mean signal deviations in predefined height ranges are typically below ±2 %. Particle backscatter and extinction coefficients agree within ±2 × 10⁻⁴ km⁻¹ sr⁻¹ and ± 0.01 km⁻¹, respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deficiencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced confidence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.

The solar radiative absorption by an aerosol layer above clouds is quantified using passive satellite spectrometry from the ultraviolet (UV) to the shortwave infrared (SWIR). UV‐absorbing aerosols have a strong signature that can be detected using UV reflectance measurements, even when above clouds. Since the aerosol extinction optical thickness decreases rapidly with increasing wavelength for biomass burning aerosols, the properties of the clouds below the aerosol layer can be retrieved in the SWIR, where aerosol extinction optical thickness is sufficiently small. Using radiative transfer computations, the contribution of the clouds to the reflected radiation can be modeled for the entire solar spectrum. In this way, cloud and aerosol effects can be separated for a scene with aerosols above clouds. Aerosol microphysical assumptions and retrievals are avoided by modeling only the pure (aerosol‐free) cloud spectra. An algorithm was developed using the spaceborne spectrometer Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). The aerosol direct radiative effect (DRE) over clouds over the South Atlantic Ocean west of Africa, averaged through August 2006 was found to be 23 ± 8 Wm⁻² with a mean variation over the region in this month of 22 Wm⁻². The largest aerosol DRE over clouds found in that month was 132 ± 8 Wm⁻². The algorithm can be applied to any instrument, or a combination of instruments, that measures UV, visible and SWIR reflectances at the top of the atmosphere (TOA) simultaneously.

The aerosol direct radiative effect (DRE) of African smoke was analyzed in cloud scenes over the southeast Atlantic Ocean, using Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations and Hadley Centre Global Environmental Model version 2 (HadGEM2) climate model simulations. The observed mean DRE was about 30–35 W m⁻² in August and September 2006–2009. In some years, short episodes of high‐aerosol DRE can be observed, due to high‐aerosol loadings, while in other years the loadings are lower but more prolonged. Climate models that use evenly distributed monthly averaged emission fields will not reproduce these high‐aerosol loadings. Furthermore, the simulated monthly mean aerosol DRE in HadGEM2 is only about 6 W m⁻² in August. The difference with SCIAMACHY mean observations can be partly explained by an underestimation of the aerosol absorption Ångström exponent in the ultraviolet. However, the subsequent increase of aerosol DRE simulation by about 20% is not enough to explain the observed discrepancy between simulations and observations.