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The article focuses on a study which seeks to validate aerosol optical thickness (AOT) and Ångström exponent acquired with the Satellite Aerosol Retrieval (SAER) algorithm for Moderate resolution Imaging Spectroradiometer (MODIS) Aqua and Terra data over land surfaces in Europe. It performs an inter-comparison of the diverse algorithm. It explores the impact of aerosols on climate AOT.

The article presents a study which examines the retrieval and validation of the aerosol optical thickness (AOT) land product at the infrared channel of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor aboard EUMETSAT's Meteosat Second Generation (MSG) meteorological satellite. It analyzes the validity of the algorithm using ground-based measurements (AERONET) for stations in Europe. It further explores the capability of the SEVIRI product to follow the aerosol diurnal cycle.

Atmospheric inversions are used to derive constraints on the net sources and sinks of CO 2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO 2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO 2 inversions in LUMIA. [ABSTRACT FROM AUTHOR]

In this paper, we describe the implementation of the Semi-Analytical Cloud Retrieval Algorithm (SACURA), to obtain scaled cloud optical thickness (SCOT) from satellite imagery acquired with the SEVIRI instrument and surface UV irradiance levels. In estimation of SCOT particular care is given to the proper specification of the background (i.e. cloud-free) spectral albedo and the retrieval of the cloud water phase from reflectance ratios in SEVIRI's 0.6 µm and 1.6 µm spectral bands. The SACURA scheme is then applied to daytime SEVIRI imagery over Europe, for the month of June 2006, at 15-min time increments. The resulting SCOT fields are compared with values obtained by the CloudSat experimental satellite mission, yielding a negligible bias, correlation coefficients ranging from 0.51 to 0.78, a root mean square difference of 1 to 2 SCOT increments. These findings compare favourably to results from similar intercomparison exercises reported in the literature. Based on the retrieved SCOT from SEVIRI and radiative transfer modelling approach, simple parameterisations are proposed to estimate the surface UV-A and UV-B irradiance. The validation of the modelled UV-A and UV-B irradiance against the measurements over two Belgian stations, Redu and Ostend, indicate good agreement with the high correlation, index of agreement and low bias. The SCOT fields estimated by implementing SACURA on imagery from geostationary satellite are reliable and its impact on surface UV irradiance levels is well produced. [ABSTRACT FROM AUTHOR]

Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursor concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of this pollutant for the time period between 2000 and 2015. Using a nonparametric timescale decomposition methodology, the long-term, seasonal and short-term variation in ozone observations were extracted. A clustering algorithm was applied to the different timescale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site-type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of deseasonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000–2015. A multidimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term timescale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time, downward trends for peak ozone concentrations were detected for all site types. In addition, a reduction of the amplitude in the seasonal cycle of ozone and a shift in the occurrence of the seasonal maximum towards earlier time of the year were observed. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursor emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are among other factors driven by regional climatic conditions. [ABSTRACT FROM AUTHOR]

A new cirrus detection algorithm for the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG), MeCiDA, is presented. The algorithm uses the seven infrared channels of SEVIRI and thus provides a consistent scheme for cirrus detection at day and night. MeCiDA combines morphological and multi-spectral threshold tests and detects optically thick and thin ice clouds. The thresholds were determined by a comprehensive theoretical study using radiative transfer simulations for various atmospheric situations as well as by manually evaluating actual satellite observations. The cirrus detection has been optimized for mid- and high latitudes but it could be adapted to other regions as well. The retrieved cirrus masks have been validated by comparison with the Moderate Resolution Imaging Spectroradiometer (MODIS) Cirrus Reflection Flag. To study possible seasonal variations in the performance of the algorithm, one scene per month of the year 2004 was randomly selected and compared with the MODIS flag. 81% of the pixels were classified identically by both algorithms. In a comparison of monthly mean values for Europe and the North-Atlantic MeCiDA detected 29.3% cirrus coverage, while the MODIS SWIR cirrus coverage was 38.1%. A lower detection efficiency is to be expected for MeCiDA, as the spatial resolution of MODIS is considerably better and as we used only the thermal infrared channels in contrast to the MODIS algorithm which uses infrared and visible radiances. The advantage of MeCiDA compared to retrievals for polar orbiting instruments or previous geostationary satellites is that it permits the derivation of quantitative data every 15 min, 24 h day. This high temporal resolution allows the study of diurnal variations and life cycle aspects. MeCiDA is fast enough for near real-time applications. [ABSTRACT FROM AUTHOR]

We present a new algorithm for the near-real time retrieval -- within 3 h of the actual satellite measurement -- of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). The retrieval is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO2 from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori information -- profile shapes and stratospheric background NO2 -- is now immediately available upon arrival (within 80 min of observation) of the OMI NO2 slant columns and cloud data at KNMI. Slant columns for NO2 are retrieved using differential optical absorption spectroscopy (DOAS) in the 405-465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O2-O2 collision complex near 477 nm. Online availability of stratospheric slant columns and NO2 profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO2 information from all previously observed orbits. OMI NO2 slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7x1015 molec cm-2. Cloud parameters from OMI's O2-O2 algorithm have similar frequency distributions as retrieved from SCIAMACHY's Fast Retrieval Scheme for Cloud Observables (FRESCO) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. A sequence of OMI observations over Europe in October 2005 shows OMI's capability to track changeable NOx air pollution from day to day in cloud-free situations. [ABSTRACT FROM AUTHOR]