First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94%, a false-alarm rate of 12% and an overall correctness of classification of 97%. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets

How thermodynamic environments control stratocumulus microphysics and interactions with aerosols

Aerosol–cloud interactions are central to climate system changes and depend on meteorological conditions. This study identifies distinct thermodynamic regimes and proposes a conceptual framework for interpreting aerosol effects. In the analysis, ten years (2003–2012) of daily satellite-derived aerosol and cloud products are combined with reanalysis data to identify factors controlling Southeast Atlantic stratocumulus microphysics. Considering the seasonal influence of aerosol input from biomass burning, thermodynamic environments that feature contrasting microphysical cloud properties and aerosol–cloud relations are classified. While aerosol impact is stronger in unstable environments, it is mostly confined to situations with low aerosol loading (aerosol index AI ≲ 0.15), implying a saturation of aerosol effects. Situations with high aerosol loading are associated with weaker, seasonally contrasting aerosol-droplet size relationships, likely caused by thermodynamically induced processes and aerosol swelling

A satellite‐based climatology of fog and low stratus formation and dissipation times in central Europe

Knowledge of fog and low stratus (FLS) cloud patterns and life cycles is important for traffic safety, for the production of solar energy and for the analysis of cloud processes in the climate system. While meteorological stations provide information on FLS, a data set describing FLS formation and dissipation times on large spatial and temporal scales does not exist yet. In this study, we use logistic regression to extract FLS formation and dissipation times from a satellite-based 10-year FLS data set covering central Europe. The resulting data set is the first to provide a geographic perspective on FLS formation and dissipation at a continental scale. The patterns found show a clear dependency of FLS formation and dissipation times on topography. In mountainous areas, FLS forms in the night and dissipates in the morning. In river valleys, the typical FLS life cycle shifts to formation after sunrise and dissipation in the afternoon. Seasonal patterns of FLS for mation and dissipation show similar FLS formation and dissipation times in winter and autumn, and in spring and summer, with longer events in the former two seasons

A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data

Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments

The galactose elimination capacity and mortality in 781 Danish patients with newly-diagnosed liver cirrhosis: a cohort study

<p>Abstract</p> <p>Background</p> <p>Despite its biologic plausibility, the association between liver function and mortality of patients with chronic liver disease is not well supported by data. Therefore, we examined whether the galactose elimination capacity (GEC), a physiological measure of the total metabolic capacity of the liver, was associated with mortality in a large cohort of patients with newly-diagnosed cirrhosis.</p> <p>Methods</p> <p>By combining data from a GEC database with data from healthcare registries we identified cirrhosis patients with a GEC test at the time of cirrhosis diagnosis in 1992–2005. We divided the patients into 10 equal-sized groups according to GEC and calculated all-cause mortality as well as cirrhosis-related and not cirrhosis-related mortality for each group. Cox regression was used to adjust the association between GEC and all-cause mortality for confounding by age, gender and comorbidity, measured by the Charlson comorbidity index.</p> <p>Results</p> <p>We included 781 patients, and 454 (58%) of them died during 2,617 years of follow-up. Among the 75% of patients with a decreased GEC (<1.75 mmol/min), GEC was a strong predictor of 30-day, 1-year, and 5-year mortality, and this could not be explained by confounding (crude hazard ratio for a 0.5 mmol/min GEC increase = 0.74, 95% CI 0.59–0.92; adjusted hazard ratio = 0.64, 95% CI 0.51–0.81). Further analyses showed that the association between GEC and mortality was identical for patients with alcoholic or non-alcoholic cirrhosis etiology, that it also existed among patients with comorbidity, and that GEC was only a predictor of cirrhosis-related mortality. Among the 25% of patients with a GEC in the normal range (≥ 1.75 mmol/min), GEC was only weakly associated with mortality (crude hazard ratio = 0.79, 95% CI 0.59–1.05; adjusted hazard ratio = 0.80, 95% CI 0.60–1.08).</p> <p>Conclusion</p> <p>Among patients with newly-diagnosed cirrhosis and a decreased GEC, the GEC was a strong predictor of short- and long-term all-cause and cirrhosis-related mortality. These findings support the expectation that loss of liver function increases mortality.</p

Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning

Understanding the processes that determine low-cloud properties and aerosol–cloud interactions (ACIs) is crucial for the estimation of their radiative effects. However, the covariation of meteorology and aerosols complicates the determination of cloud-relevant influences and the quantification of the aerosol–cloud relation. This study identifies and analyzes sensitivities of cloud fraction and cloud droplet effective radius to their meteorological and aerosol environment in the atmospherically stable southeast Atlantic during the biomass-burning season based on an 8-day-averaged data set. The effect of geophysical parameters on clouds is investigated based on a machine learning technique, gradient boosting regression trees (GBRTs), using a combination of satellite and reanalysis data as well as trajectory modeling of air-mass origins. A comprehensive, multivariate analysis of important drivers of cloud occurrence and properties is performed and evaluated. The statistical model reveals marked subregional differences of relevant drivers and processes determining low clouds in the southeast Atlantic. Cloud fraction is sensitive to changes of lower tropospheric stability in the oceanic, southwestern subregion, while in the northeastern subregion it is governed mostly by surface winds. In the pristine, oceanic subregion large-scale dynamics and aerosols seem to be more important for changes of cloud droplet effective radius than in the polluted, near-shore subregion, where free tropospheric temperature is more relevant. This study suggests the necessity to consider distinct ACI regimes in cloud studies in the southeast Atlantic