In this paper we introduce the new concept of directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface retrieved from satellite observations.This surface DLER describes Lambertian (isotropic) surface reflection which is extended with a dependence on the satellite viewing geometry.We apply this concept to data of the GOME-2 satellite instruments to create a global database of the reflectivity of the Earth's surface, providing surface DLER for 26 wavelength bands between 328 and 772 nm as a function of the satellite viewing angle via a second-degree polynomial parameterisation.The resolution of the database grid is 0.25 • by 0.25 • , but the real, intrinsic spatial resolution varies over the grid from 1.0 • by 1.0 • to 0.5 • by 0.5 • down to 0.25 • by 0.25 • by applying dynamic gridding techniques.The database is based on more than 10 years (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) of GOME-2 data from the MetOp-A and MetOp-B satellites.The relation between DLER and bi-directional reflectance distribution function (BRDF) surface reflectance is studied using radiative transfer simulations.For the shorter wavelengths (λ < 500 nm), there are significant differences between the two.For instance, at 463 nm the difference can go up to 6 % at 30 • solar zenith angle.The study also shows that, although DLER and BRDF surface reflectances have different properties, they are comparable for the longer wavelengths (λ > 500 nm).Based on this outcome, the GOME-2 surface DLER is compared with MODIS surface BRDF data from MODIS band 1 (centred around 645 nm) using both case studies and global comparisons.The conclusion of this validation is that the GOME-2 DLER compares well to MODIS BRDF data and that it does so much better than the non-directional LER database.The DLER approach for describing surface reflectivity is therefore an important improvement over the standard isotropic (non-directional) LER approaches used in the past.The GOME-2 surface DLER database can be used for the retrieval of atmospheric properties from GOME-2 and from previous satellite instruments like GOME and SCIA-MACHY.It will also be used to support retrievals from the future Sentinel-5 UVNS (ultraviolet, visible, near-infrared, and short-wave infrared) satellite instrument.

The Sentinel-5 Precursor (S5P) mission, launched in October 2017, started the operational atmospheric composition measurements from space as part of the European Copernicus programme. The payload of the S5P mission is the TROPOspheric Monitoring Instrument (TROPOMI) that provides key information on air quality, climate and the ozone layer with high spatial resolution and daily global coverage. In this presentation we provide an overview of the operational TROPOMI geophysical products including O3, NO2, SO2, HCHO, CO, CH4, as well as UV, cloud and aerosol properties. The European teams responsible for the operational products are organized in three groups covering: (i) retrieval algorithms, (ii) data processors being used in the S5P ground-segment for the generation of the operational TROPOMI products, and (iii) routine validation of S5P products using fiducial reference measurements. It is planned to maintain this project organization during the complete mission in order to ensure the timely provision of state-of-science data products that are continuously improved and validated. Initial versions of the TROPOMI products were already available a few weeks after launch and were presented at the first light event that took place in December 2017. The retrieval algorithms and data processors were optimized during the commissioning phase that lasted until April 2018 and the results of the preliminary validation were presented in June 2018. Finally the first set of operational S5P products was released to the public in July 2018. The release of the remaining S5P products is organized in a staggered approach and will take place during the second part of 2018 and early 2019. The work on TROPOMI/S5P geophysical products is funded by EU Copernicus, ESA and national contributions from The Netherlands, Germany, Belgium and Finland.

The primary goal of this paper is to introduce two new surface reflectivity climatologies. The two databases contain the Lambertian‐equivalent reflectivity (LER) of the Earth's surface, and they are meant to support satellite retrieval of trace gases and of cloud and aerosol information. The surface LER databases are derived from the Global Ozone Monitoring Experiment (GOME)‐2 and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments and can be considered as improved and extended descendants of earlier surface LER climatologies based on the Total Ozone Mapping Spectrometer (TOMS), GOME‐1, and Ozone Monitoring Instrument (OMI) instruments. The GOME‐2 surface LER database consists of 21 wavelength bands that span the wavelength range from 335 to 772 nm. The SCIAMACHY surface LER database covers the wavelength range between 335 and 1670 nm in 29 wavelength bands. The two databases are made for each month of the year, and their spatial resolution is 1° × 1°. In this paper we present the methods that are used to derive the surface LER; we analyze the spatial and temporal behavior of the surface LER fields and study the amount of residual cloud contamination in the databases. For several surface types we analyze the spectral surface albedo and the seasonal variation. When compared to the existing surface LER databases, both databases are found to perform well. As an example of possible application of the databases we study the performance of the Fast Retrieval Scheme for Clouds from the Oxygen A‐band (FRESCO) cloud information retrieval when it is equipped with the new surface albedo databases. We find considerable improvements. The databases introduced here can not only improve retrievals from GOME‐2 and SCIAMACHY but also support those from other instruments, such as TROPOspheric Monitoring Instrument (TROPOMI), to be launched in 2017.

This paper presents a comparison of vertical ozone profiles retrieved by the Ozone ProfilE Retrieval Algorithm (OPERA) (versions 1.14–1.24) from the Global Ozone Monitoring Experiment-2 (GOME-2) measurements on board the Meteorological operational Metop-A satellite with spaceborne high-vertical-resolution ozone profiles by Global Ozone Monitoring by Occultation of Stars (GOMOS), Optical Spectrograph and Infrared Imager System (OSIRIS) and Microwave Limb Sounder (MLS). The comparison, with global coverage, focuses on the stratosphere and the lower mesosphere and covers the period from March 2008 until the end of 2011. The comparison shows an agreement of GOME-2 ozone profiles with those of GOMOS, OSIRIS and MLS within ±15 % in the altitude range from 15 km up to ∼ 35–40 km depending on latitude. The GOME-2 bias with respect to the reference instruments depends on season, with the strongest dependence observed at high latitudes. The GOME-2 ozone profiles retrieved from non-degradation corrected radiances have a tendency to a systematic negative bias with respect to the reference data above ∼ 30 km. We have studied the influence of solar zenith angle and the effect of instrumental degradation correction. In addition, we have studied GOME-2 performance in Arctic ozone depletion case and demonstrated that GOME-2 data provide valuable information about ozone profiles.

The three Global Ozone Monitoring Experiment-2 instruments will provide unique and long data sets for atmospheric research and applications. The complete time period will be 2007–2022, including the period of ozone depletion as well as the beginning of ozone layer recovery. Besides ozone chemistry, the GOME-2 (Global Ozone Monitoring Experiment-2) products are important e.g. for air quality studies, climate modelling, policy monitoring and hazard warnings. The heritage for GOME-2 is in the ERS/GOME and Envisat/SCIAMACHY instruments. The current Level 2 (L2) data cover a wide range of products such as ozone and minor trace gas columns (NO2, BrO, HCHO, H2O, SO2), vertical ozone profiles in high and low spatial resolution, absorbing aerosol indices, surface Lambertian-equivalent reflectivity database, clear-sky and cloud-corrected UV indices and surface UV fields with different weightings and photolysis rates. The Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) processes and disseminates data 24/7. Data quality is guaranteed by the detailed review processes for the algorithms, validation of the products as well as by a continuous quality monitoring of the products and processing. This paper provides an overview of the O3M SAF project background, current status and future plans for the utilisation of the GOME-2 data. An important focus is the provision of summaries of the GOME-2 products including product principles and validation examples together with sample images. Furthermore, this paper collects references to the detailed product algorithm and validation papers.

Abstract. An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O2 A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterised by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies, and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O2 A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values no longer realistically represent actual extinction profiles. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). If one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large, in such a way that the retrieved height of the single layer is above the two-layer profile. Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2013) and Crisp et al. (2012), and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between −4 and −8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index.

Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction ngstrom exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (ngstrom exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5A degrees x 5A degrees regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

For the retrieval of the vertical distribution of ozone in the atmosphere the Ozone ProfilE Retrieval Algorithm (OPERA) has been further developed. The new version (1.26) of OPERA is capable of retrieving ozone profiles from UV–VIS observations of most nadir-looking satellite instruments like GOME, SCIAMACHY, OMI and GOME-2. The setup of OPERA is described and results are presented for GOME and GOME-2 observations. The retrieved ozone profiles are globally compared to ozone sondes for the years 1997 and 2008. Relative differences between GOME/GOME-2 and ozone sondes are within the limits as specified by the user requirements from the Climate Change Initiative (CCI) programme of ESA (20% in the troposphere, 15% in the stratosphere). To demonstrate the performance of the algorithm under extreme circumstances, the 2009 Antarctic ozone hole season was investigated in more detail using GOME-2 ozone profiles and lidar data, which showed an unusual persistence of the vortex over the Río Gallegos observing station (51° S, 69.3° W). By applying OPERA to multiple instruments, a time series of ozone profiles from 1996 to 2013 from a single robust algorithm can be created.

Arctic column ozone reached record low values (∼310 DU) during March of 2011, exposing Arctic ecosystems to enhanced UV‐B. We identify the cause of this anomaly using the Oslo CTM2 atmospheric chemistry model driven by ECMWF meteorology to simulate Arctic ozone from 1998 through 2011. CTM2 successfully reproduces the variability in column ozone, from week to week, and from year to year, correctly identifying 2011 as an extreme anomaly over the period. By comparing parallel model simulations, one with all Arctic ozone chemistry turned off on January 1, we find that chemical ozone loss in 2011 is enhanced relative to previous years, but it accounted for only 23% of the anomaly. Weakened transport of ozone from middle latitudes, concurrent with an anomalously strong polar vortex, was the primary cause of the low ozone When the zonal winds relaxed in mid‐March 2011, Arctic column ozone quickly recovered.

Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressure contains information on aerosol layer pressure. For cloud free scenes, the derived FRESCO cloud pressure is close to the aerosol layer pressure, especially for optically thick aerosol layers. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressure may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO and AAI data, an estimate for the aerosol layer pressure can be given.

Atmospheric remote sensing from satellites is essential for the long-term, continuous monitoring of the ozone hole and is critical in order to evaluate stratospheric ozone recovery. During the last decade, thermal infra-red (TIR) sensors have demonstrated their enhanced capability in capturing both the spatial and temporal variability of the ozone hole during the polar night, in contrast to instruments measuring in the ultraviolet-visible (UV-vis) range of the spectrum which need sunlight to operate. In this paper we present a study of the ozone hole as observed by the Infra-red Atmospheric Sounding Interferometer (IASI) on-board the MetOp-A European satellite platform from the beginning of data dissemination, August 2008, to the end of December 2010. Here we demonstrate IASI's ability to capture the seasonal characteristics of the ozone hole. We compare IASI ozone total columns and vertical profiles with those of the Global Ozone Monitoring Experiment 2 (GOME-2) (also on-board MetOp-A) and electrochemical concentration cell (ECC) ozone sonde measurements for the ozone hole region and period. The IASI and GOME-2 ozone total columns were found to be in excellent agreement for this region with a correlation coefficient of 0.97, for September, October and November 2009. IASI on average, exhibits a positive bias of approximately 7 % compared to the GOME-2 measurements over the entire ozone hole period. Comparisons between IASI and ozone sonde measurements were also found to be in good agreement with the percentage difference between both ozone profile measurements being less than ±30 % over the altitude range of 0–40 km. The vertical structure of the ozone hole is in particular captured remarkably good by IASI.