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The Multi-Spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which is obtained from EarthCARE's active sensors, in order to obtain a full three-dimensional view of cloud and aerosol conditions. Mesoscale weather systems, in particular, will be characterized. The discovery of a non-compliance of the MSI visible–near-infrared–shortwave infrared (VNS) camera’s visible (VIS) and shortwave infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across-track of up to 14 nm (VIS) and 20 nm (SWIR1) led to the need for an analysis regarding its impact on MSI Level-2A aerosol and cloud products. A significant influence of the spectral misalignment effect (SMILE) on MSI retrievals is identified due to the spectral variation in gas absorption, surface reflectance, and aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapor and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapor and ozone absorption. We found relative errors in the top-of-atmosphere (TOA) signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level-2 retrievals or correcting the Level-1 signal will improve MSI aerosol and cloud product quality.

The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the “truth” is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided.

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is the first mission that will provide measurements from active profiling, passive imaging and a broadband radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. This framework enables full error propagation given by the uncertainties in measurements and a priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations.

Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is a combination of multiple active and passive instruments on a single platform. The Atmospheric Lidar (ATLID) provides vertical information of clouds and aerosol particles along the satellite track. In addition, the Multi-Spectral Imager (MSI) collects multi-spectral information from the visible to the infrared wavelengths over a swath width of 150 km across the track. The ATLID–MSI Column Products processor (AM-COL) described in this paper combines the high vertical resolution of the lidar along track and the horizontal resolution of the imager across track to better characterize a three-dimensional scene. ATLID Level 2a (L2a) data from the ATLID Layer Products processor (A-LAY), MSI L2a data from the MSI Cloud Products processor (M-CLD) and the MSI Aerosol Optical Thickness processor (M-AOT), and MSI Level 1c (L1c) data are used as input to produce the synergistic columnar products: the ATLID–MSI Cloud Top Height (AM-CTH) and the ATLID–MSI Aerosol Column Descriptor (AM-ACD). The coupling of ATLID (measuring at 355 nm) and MSI (at ≥670 nm) provides multi-spectral observations of the aerosol properties. In particular, the Ångström exponent from the spectral aerosol optical thickness (AOT 355/670 nm) adds valuable information for aerosol typing. The AOT across track, the Ångström exponent and the dominant aerosol type are stored in the AM-ACD product. The accurate detection of the cloud top height (CTH) with lidar is limited to the ATLID track. The difference in the CTH detected by ATLID and retrieved by MSI is calculated along track. The similarity of MSI pixels across track with those along track is used to transfer the calculated CTH difference to the entire MSI swath. In this way, the accuracy of the CTH is increased to achieve the EarthCARE mission's goal of deriving the radiative flux at the top of the atmosphere with an accuracy of 10 W m−2 for a 100 km2 snapshot view of the atmosphere. The synergistic CTH difference is stored in the AM-CTH product. The quality status is provided with the products. It depends, e.g., on day/night conditions and the presence of multiple cloud layers. The algorithm was successfully tested using the common EarthCARE test scenes. Two definitions of the CTH from the model truth cloud extinction fields are compared: an extinction-based threshold of 20 Mm−1 provides the geometric CTH, and a cloud optical thickness threshold of 0.25 describes the radiative CTH. The first CTH definition was detected with ATLID and the second one with MSI. The geometric CTH is always higher than or equal to the radiative CTH.

Objectives: Since the beginning of the COVID-19 pandemic, efforts have been made to contain the spread of the virus. However, the epidemiological burden of other respiratory viruses during the pandemic is unclear. We aim to address the epidemiology of respiratory viruses on adults/adolescents since the beginning of the pandemic. Methods: We systematically searched five databases and performed a meta-analysis to explore the pooled prevalence of respiratory viruses in different geographical regions, age groups, and periods and compared the prevalence between COVID-19 cases and non-COVID-19 patients. Results: Enteroviruses/rhinoviruses were highly prevalent compared to other viruses. Different viruses were dominant in different regions. No significant differences in prevalence were found between different age groups, except for human metapneumovirus. There was an increase in prevalence of non-SARS-CoV-2 viruses in the second half of the pandemic (July 2021-December 2022). Comparison of COVID-19 and non-COVID patients showed a higher prevalence in the non-COVID group, significant for influenza, seasonal coronaviruses, and human parainfluenza viruses. Conclusion: Our findings indicate that enteroviruses/rhinoviruses were less impacted by healthcare measures compared with other respiratory viruses. The relaxation of measures in the second half led to an increased pooled prevalence of infections. Several factors may explain the lower prevalence among individuals infected with COVID-19.

The EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) satellite mission will provide new insights into aerosol–cloud–radiation interactions by means of synergistic observations of the Earth's atmosphere from a collection of active and passive remote sensing instruments, flying on a single satellite platform. The Multi-Spectral Imager (MSI) will provide visible and infrared images in the cross-track direction with a 150 km swath and a pixel sampling at 500 m. The suite of MSI cloud algorithms will deliver cloud macro- and microphysical properties complementary to the vertical profiles measured from the Atmospheric Lidar (ATLID) and the Cloud Profiling Radar (CPR) instruments. This paper provides an overview of the MSI cloud mask algorithm (M-CM) being developed to derive the cloud flag, cloud phase and cloud type products, which are essential inputs to downstream EarthCARE algorithms providing cloud optical and physical properties (M-COP) and aerosol optical properties (M-AOT). The MSI cloud mask algorithm has been applied to simulated test data from the EarthCARE end-to-end simulator and satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as from the Spinning Enhanced Visible InfraRed Imager (SEVIRI). Verification of the MSI cloud mask algorithm to the simulated test data and the official cloud products from SEVIRI and MODIS demonstrates a good performance of the algorithm. Some discrepancies are found, however, for the detection of thin cirrus clouds over bright surfaces like desert or snow. This will be improved by tuning of the thresholds once real observations are available.

The Hybrid End-To-End Aerosol Classification (HETEAC) model for the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is introduced. The model serves as the common baseline for the development, evaluation, and implementation of EarthCARE algorithms. It guarantees the consistency of different aerosol products from the multi-instrument platform and facilitates the conformity of broad-band optical properties needed for EarthCARE radiative-closure assessments. While the hybrid approach ensures that the theoretical description of aerosol microphysical properties is consistent with the optical properties of the measured aerosol types, the end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical, and radiative properties. Four basic aerosol components with prescribed microphysical properties are used to compose various natural and anthropogenic aerosols of the troposphere. The components contain weakly and strongly absorbing fine-mode and spherical and non-spherical coarse-mode particles and thus are representative for pollution, smoke, sea salt, and dust, respectively. Their microphysical properties are selected such that good coverage of the observational phase space of intensive, i.e., concentration-independent, optical aerosol properties derived from EarthCARE measurements is obtained. Mixing rules to calculate optical and radiative properties of any aerosol blend composed of the four basic components are provided. Applications of HETEAC in the generation of test scenes, the development of retrieval algorithms for stand-alone and synergistic aerosol products from EarthCARE's atmospheric lidar (ATLID) and multi-spectral imager (MSI), and for radiative-closure assessments are introduced. Finally, the implications of simplifying model assumptions and possible improvements are discussed, and conclusions for future validation and development work are drawn.

For understanding Arctic climate change, it is critical to quantify and address uncertainties in climate data records on clouds and radiative fluxes derived from long-term passive satellite observations. A unique set of observations collected during the PS106 expedition of the research vessel Polarstern (28 May to 16 July 2017) by the OCEANET facility, is exploited here for this purpose and compared with the CERES SYN1deg ed. 4.1 satellite remote-sensing products. Mean cloud fraction (CF) of 86.7 % for CERES SYN1deg and 76.1 % for OCEANET were found for the entire cruise. The difference of CF between both data sets is due to different spatial resolution and momentary data gaps, which are a result of technical limitations of the set of shipborne instruments. A comparison of radiative fluxes during clear-sky (CS) conditions enables radiative closure (RC) for CERES SYN1deg products by means of independent radiative transfer simulations. Several challenges were encountered to accurately represent clouds in radiative transfer under cloudy conditions, especially for ice-containing clouds and low-level stratus (LLS) clouds. During LLS conditions, the OCEANET retrievals were particularly compromised by the altitude detection limit of 155 m of the cloud radar. Radiative fluxes from CERES SYN1deg show a good agreement with ship observations, having a bias (standard deviation) of −6.0 (14.6) and 23.1 (59.3) W m−2 for the downward longwave (LWD) and shortwave (SWD) fluxes, respectively. Based on CERES SYN1deg products, mean values of the radiation budget and the cloud radiative effect (CRE) were determined for the PS106 cruise track and the central Arctic region (70–90∘ N). For the period of study, the results indicate a strong influence of the SW flux in the radiation budget, which is reduced by clouds leading to a net surface CRE of −8.8 and −9.3 W m−2 along the PS106 cruise and for the entire Arctic, respectively. The similarity of local and regional CRE supports the consideration that the PS106 cloud observations can be representative of Arctic cloudiness during early summer.

The clear-sky radiative effect of aerosol–radiation interactions is of relevance for our understanding of the climate system. The influence of aerosol on the surface energy budget is of high interest for the renewable energy sector. In this study, the radiative effect is investigated in particular with respect to seasonal and regional variations for the region of Germany and the year 2015 at the surface and top of atmosphere using two complementary approaches. First, an ensemble of clear-sky models which explicitly consider aerosols is utilized to retrieve the aerosol optical depth and the surface direct radiative effect of aerosols by means of a clear-sky fitting technique. For this, short-wave broadband irradiance measurements in the absence of clouds are used as a basis. A clear-sky detection algorithm is used to identify cloud-free observations. Considered are measurements of the short-wave broadband global and diffuse horizontal irradiance with shaded and unshaded pyranometers at 25 stations across Germany within the observational network of the German Weather Service (DWD). The clear-sky models used are the Modified MAC model (MMAC), the Meteorological Radiation Model (MRM) v6.1, the Meteorological–Statistical solar radiation model (METSTAT), the European Solar Radiation Atlas (ESRA), Heliosat-1, the Center for Environment and Man solar radiation model (CEM), and the simplified Solis model. The definition of aerosol and atmospheric characteristics of the models are examined in detail for their suitability for this approach. Second, the radiative effect is estimated using explicit radiative transfer simulations with inputs on the meteorological state of the atmosphere, trace gases and aerosol from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. The aerosol optical properties (aerosol optical depth, Ångström exponent, single scattering albedo and asymmetry parameter) are first evaluated with AERONET direct sun and inversion products. The largest inconsistency is found for the aerosol absorption, which is overestimated by about 0.03 or about 30 % by the CAMS reanalysis. Compared to the DWD observational network, the simulated global, direct and diffuse irradiances show reasonable agreement within the measurement uncertainty. The radiative kernel method is used to estimate the resulting uncertainty and bias of the simulated direct radiative effect. The uncertainty is estimated to −1.5 ± 7.7 and 0.6 ± 3.5 W m−2 at the surface and top of atmosphere, respectively, while the annual-mean biases at the surface, top of atmosphere and total atmosphere are −10.6, −6.5 and 4.1 W m−2, respectively. The retrieval of the aerosol radiative effect with the clear-sky models shows a high level of agreement with the radiative transfer simulations, with an RMSE of 5.8 W m−2 and a correlation of 0.75. The annual mean of the REari at the surface for the 25 DWD stations shows a value of −12.8 ± 5 W m−2 as the average over the clear-sky models, compared to −11 W m−2 from the radiative transfer simulations. Since all models assume a fixed aerosol characterization, the annual cycle of the aerosol radiation effect cannot be reproduced. Out of this set of clear-sky models, the largest level of agreement is shown by the ESRA and MRM v6.1 models.

Vitamin D supplementation and its impact on immunoregulation are widely investigated. We aimed to assess the prevention and treatment efficiency of vitamin D supplementation in the context of coronavirus disease 2019 (COVID-19) and any disease-related complications. For this systematic review and meta-analysis, we searched databases (PubMed, Embase, Scopus, Web of Science, The Cochrane Library, medRxiv, Cochrane COVID-19 Study Register, and ClinicalTrial.gov) for studies published between 1 November 2019 and 17 September 2021. We considered randomized trials (RCTs) as potentially eligible when patients were tested for SARS-CoV-2 infection and received vitamin D supplementation versus a placebo or standard-of-care control. A random-effects model was implemented to obtain pooled odds ratios for the effect of vitamin D supplementation on the main outcome of mortality as well as clinical outcomes. We identified a total of 5,733 articles, of which eight RCTs (657 patients) met the eligibility criteria. Although no statistically significant effects were reached, the use of vitamin D supplementation showed a trend for reduced mortality [odds ratio (OR) 0.74, 95% confidence interval (CI) 0.32–1.71, p = 0.48] compared with the control group, with even stronger effects, when vitamin D was administered repeatedly (OR 0.33, 95% CI 0.1–1.14). The mean difference for the length of hospitalization was −0.28 (95% CI −0.60 to 0.04), and the ORs were 0.41 (95% CI 0.15–1.12) and 0.52 (95% CI 0.27–1.02) for ICU admission and mechanical ventilation, respectively. In conclusion, vitamin D supplementation did not improve the clinical outcomes in COVID-19 patients, but trends of beneficial effects were observed. Further investigations are required, especially studies focusing on the daily administration of vitamin D.

The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1 km2 compared to the standard 3×3 km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6 µm, 0.8 µm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6 µm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6 µm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains.

Reliable reference measurements over the ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Within the framework of the Maritime Aerosol Network, shipborne reference datasets have been collected over the Atlantic Ocean since 2004 with Microtops Sun photometers. These were recently complemented by measurements with the multi-spectral GUVis-3511 shadowband radiometer during five cruises with the research vessel Polarstern. The aerosol optical depth (AOD) uncertainty estimate of both shipborne instruments of ±0.02 can be confirmed if the GUVis instrument is cross calibrated to the Microtops instrument to account for differences in calibration, and if an empirical correction to account for the broad shadowband as well as the effects of forward scattering is introduced. Based on these two datasets, a comprehensive evaluation of aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on NASA's Earth Observing System satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat satellite, and the Copernicus Atmosphere Monitoring Service reanalysis (CAMS RA) is presented. For this purpose, focus is given to the accuracy of the AOD at 630 nm in combination with the Ångström exponent (AE), discussed in the context of the ambient aerosol type. In general, the evaluation of MODIS AOD from the official level-2 aerosol products of C6.1 against the Microtops AOD product confirms that 76 % of data points fall into the expected error limits given by previous validation studies. The SEVIRI-based AOD product exhibits a 25 % larger scatter than the MODIS AOD products at the instrument's native spectral channels. Further, the comparison of CAMS RA and MODIS AOD versus the shipborne reference shows similar performance for both datasets, with some differences arising from the assimilation and model assumptions. When considering aerosol conditions, an overestimation of AE is found for scenes dominated by desert dust for MODIS and SEVIRI products versus the shipborne reference dataset. As the composition of the mixture of aerosol in satellite products is constrained by model assumptions, this highlights the importance of considering the aerosol type in evaluation studies for identifying problematic aspects.

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).

Infrared “Desert Dust” composite imagery taken by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), onboard the Meteosat Second Generation (MSG) series of satellites above the equatorial East Atlantic, has been widely used for more than a decade to identify and track the presence of dust storms from and over the Sahara Desert, the Middle East, and southern Africa. Dust is characterised by distinctive pink colours in the Desert Dust false-colour imagery; however, the precise colour is influenced by numerous environmental properties, such as the surface thermal emissivity and skin temperature, the atmospheric water vapour content, the quantity and height of dust in the atmosphere, and the infrared optical properties of the dust itself. For this paper, simulations of SEVIRI infrared measurements and imagery have been performed using a modelling system, which combines dust concentrations simulated by the aerosol transport model COSMO-MUSCAT (COSMO: COnsortium for Small-scale MOdelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) with radiative transfer simulations from the RTTOV (Radiative Transfer for TOVS) model. Investigating the sensitivity of the synthetic infrared imagery to the environmental properties over a 6-month summertime period from 2011 to 2013, it is confirmed that water vapour is a major control on the apparent colour of dust, obscuring its presence when the moisture content is high. Of the three SEVIRI channels used in the imagery (8.7, 10.8, and 12.0 µm), the channel at 10.8 µm has the highest atmospheric transmittance and is therefore the most sensitive to the surface skin temperature. A direct consequence of this sensitivity is that the background desert surface exhibits a strong diurnal cycle in colour, with light blue colours possible during the day and purple hues prevalent at night. In dusty scenes, the clearest pink colours arise from high-altitude dust in dry atmospheres. Elevated dust influences the dust colour primarily by reducing the contrast in atmospheric transmittance above the dust layer between the SEVIRI channels at 10.8 and 12.0 µm, thereby boosting red and pink colours in the imagery. Hence, the higher the dust altitude, the higher the threshold column moisture needed for dust to be obscured in the imagery: for a sample of dust simulated to have an aerosol optical depth (AOD) at 550 nm of 2–3 at an altitude of 3–4 km, the characteristic colour of the dust may only be impaired when the total column water vapour is particularly moist (⪆39 mm). Meanwhile, dust close to the surface (altitude  km) is only likely to be apparent when the atmosphere is particularly dry and when the surface is particularly hot, requiring column moisture ⪅13 mm and skin temperatures ⪆314 K, and is highly unlikely to be apparent when the skin temperature is ⪅300 K. Such low-altitude dust will regularly be almost invisible within the imagery, since it will usually be beneath much of the atmospheric water vapour column. It is clear that the interpretation of satellite-derived dust imagery is greatly aided by knowledge of the background environment.

Number concentrations of ice-nucleating particles (NINP) in the Arctic were derived from ground-based filter samples. Examined samples had been collected in Alert (Nunavut, northern Canadian archipelago on Ellesmere Island), Utqiaġvik, formerly known as Barrow (Alaska), Ny-Ålesund (Svalbard), and at the Villum Research Station (VRS; northern Greenland). For the former two stations, examined filters span a full yearly cycle. For VRS, 10 weekly samples, mostly from different months of one year, were included. Samples from Ny-Ålesund were collected during the months from March until September of one year. At all four stations, highest concentrations were found in the summer months from roughly June to September. For those stations with sufficient data coverage, an annual cycle can be seen. The spectra of NINP observed at the highest temperatures, i.e., those obtained for summer months, showed the presence of INPs that nucleate ice up to −5 ∘C. Although the nature of these highly ice-active INPs could not be determined in this study, it often has been described in the literature that ice activity observed at such high temperatures originates from the presence of ice-active material of biogenic origin. Spectra observed at the lowest temperatures, i.e., those derived for winter months, were on the lower end of the respective values from the literature on Arctic INPs or INPs from midlatitude continental sites, to which a comparison is presented herein. An analysis concerning the origin of INPs that were ice active at high temperatures was carried out using back trajectories and satellite information. Both terrestrial locations in the Arctic and the adjacent sea were found to be possible source areas for highly active INPs.

Satellite imagery of atmospheric mineral dust is sensitive to the optical properties of the dust, governed by the mineral refractive indices, particle size, and particle shape. In infrared channels the imagery is also sensitive to the dust layer height and to the surface and atmospheric environment. Simulations of mineral dust in infrared Desert Dust imagery from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) have been performed, using the COSMO-MUSCAT (COSMO: COnsortium for Small-scale MOdelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) dust transport model and the Radiative Transfer for TOVS (RTTOV) program, in order to investigate the sensitivity of the imagery to assumed dust properties. This paper introduces the technique and performs initial validation and comparisons with SEVIRI measurements over North Africa for daytime hours during 6 months covering June and July of 2011–2013. Using T-matrix scattering theory and assuming the dust particles to be spherical or spheroidal, wavelength- and size-dependent dust extinction values are calculated for a number of different dust refractive index databases, along with several values of the particle aspect ratio, denoting the particle shape. The consequences for the infrared extinction values of both the particle shape and the particle orientation are explored: this analysis shows that as the particle asphericity increases, the extinctions increase if the particles are aligned horizontally, and decrease if they are aligned vertically. Randomly oriented spheroidal particles have very similar infrared extinction properties as spherical particles, whereas the horizontally and vertically aligned particles can be considered to be the upper and lower bounds on the extinction values. Inputting these values into COSMO-MUSCAT-RTTOV, it is found that spherical particles do not appear to be sufficient to describe fully the resultant colour of the dust in the infrared imagery. Comparisons of SEVIRI and simulation colours indicate that of the dust types tested, the dust refractive index dataset produced by Volz (1973) shows the most similarity in the colour response to dust in the SEVIRI imagery, although the simulations have a smaller range of colour than do the observations. It is also found that the thermal imagery is most sensitive to intermediately sized particles (radii between 0.9 and 2.6 µm): larger particles are present in too small a concentration in the simulations, as well as with insufficient contrast in extinction between wavelength channels, to have much ability to perturb the resultant colour in the SEVIRI dust imagery.

Meckel’s diverticulum (MD) is the persistence of the omphalomesenteric duct. It is usually asymptomatic but may present with bleeding, infections, and intestinal obstruction. It also may be a content of a hernia sac, a so-called Littre hernia. Herein, we will present the case of a 75-year-old female, who suffered from a painful swelling of the right inguinal region. Ultrasound imaging detected an inguinal hernia with incarcerated blind ending small bowel. Immediately, a laparoscopy was conducted. We diagnosed a right femoral hernia with an incarcerated MD. A TAPP (transabdominal preperitoneal) procedure was performed and the MD tangential stapled. Due to an uneventful postoperative course, the patient left the hospital after two days. An incarceration of a MD in a femoral hernia is rare. Tangential resection of the MD with simultaneous hernia repair in a TAPP technique seems to be a sufficient approach, when it is conducted by an experienced surgeon.

Images of measured radiance in different channels of the geostationary Meteosat-9 SEVIRI instrument are analysed with respect to the representativeness of the observations of eight cloud observatories in Europe (e.g. measurements from cloud radars or microwave radiometers). Cloudy situations are selected to get a time series for every pixel in a 300 km × 300 km area centred around each ground station. Then a cross correlation of each time series to the pixel nearest to the corresponding ground site is calculated. In the end a correlation length is calculated to define the representativeness. It is found that measurements in the visible and near infrared channels, which respond to cloud physical properties, are correlated in an area with a 1 to 4 km radius, while the thermal channels, that correspond to cloud top temperature, are correlated to a distance of about 20 km. This also points to a higher variability of the cloud microphysical properties inside a cloud than of the cloud top temperature. The correlation length even increases for the channels at 6.2, 7.3 and 9.7 μm. They respond to radiation from the upper atmospheric layers emitted by atmospheric gases and higher level clouds, which are more homogeneous than low-level clouds. Additionally, correlations at different distances, corresponding to the grid box sizes of forecast models, were compared. The results suggest the possibility of comparisons between instantaneous cloud observations from ground sites and regional forecast models and ground-based measurements. For larger distances typical for global models the correlations decrease, especially for short-wave measurements and corresponding cloud products. By comparing daily means, the correlation length of each station is increased to about 3 to 10 times the value of instantaneous measurements and also the comparability to models grows.

We introduce the development of EarthCARE Level 2 layer products derived from profile measurements of the high-spectral-resolution lidar ATLID and column products obtained from combined information of ATLID and the Multi-Spectral Imager (MSI). Layer products include cloud top height as well as aerosol layer boundaries and mean optical properties along the satellite nadir track. Synergistic column products comprise cloud top height, Ångström exponent, and aerosol type both along-track and across the MSI swath.

In the year 2007 a General Observation Period (GOP) has been performed within the German Priority Program on Quantitative Precipitation Forecasting (PQP). By optimizing the use of existing instrumentation a large data set of in-situ and remote sensing instruments with special focus on water cycle variables was gathered over the full year cycle. The area of interest covered central Europe with increasing focus towards the Black Forest where the Convective and Orographically-induced Precipitation Study (COPS) took place from June to August 2007. Thus the GOP includes a variety of precipitation systems in order to relate the COPS results to a larger spatial scale. For a timely use of the data, forecasts of the numerical weather prediction models COSMO-EU and COSMO-DE of the German Meteorological Service were tailored to match the observations and perform model evaluation in a near real-time environment. The ultimate goal is to identify and distinguish between different kinds of model deficits and to improve process understanding.

We introduce the Hybrid End-To-End Aerosol Classification (HETEAC) model for the upcoming EarthCARE mission. The model serves as the common baseline for development, evaluation, and implementation of EarthCARE algorithms. It shall ensure the consistency of different aerosol products from the multi-instrument platform as well as facilitate the conform specification of broad-band optical properties necessary for the EarthCARE radiative closure efforts. The hybrid approach ensures the theoretical description of aerosol microphysics consistent with the optical properties of various aerosol types known from observations. The end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical and radiative properties.