Abstract. 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 (NO 2 , BrO, HCHO, H 2 O, SO 2 ), 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. We discuss uncertainty quantification for aerosoltype selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval. This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions. The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosoltype selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

Abstract. This paper presents a

<p>In this presentation we consider uncertainty in Look-up table (LUT) based technique for retrieving aerosol optical depth (AOD) and aerosol type using TROPOMI/S5P measurements.<br>The LUTs are multi-dimensional tables containing aerosol microphysical properties and they have been constructed using libRadtran simulations. <br>Especially we study difficulty in aerosol microphysical model selection that reflects the retrieval uncertainty. As a source of uncertainty we have also acknowledged so called model discrepancy originating from imperfect forward modeling. <br>The methodology considered is based on Bayesian inference where the retrieved AOD estimate is given as maximum a posterior (MAP) value and uncertainties are described as posterior density functions. We have also combined statistically the most appropriate aerosol microphysical models by Bayesian model averaging when the selection of single best-fitting model is not clear.<br>The motivation is to consider difficulty in aerosol model selection and obtain realistic uncertainty estimates.<br>We have applied this methodology to OMI/Aura measurements in our earlier studies. Here we present results when used higher resolution measurements from TROPOMI/S5P and studied the methodology covering various aerosol conditions including wild fire and dust events.</p>

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