Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers

The newest and upcoming geostationary passive imagers have thermal infrared channels comparable to those of more established instruments, but their spectral response functions still differ significantly. Therefore, retrievals developed for a certain type of radiometer cannot simply be applied to another imager. Here, a set of spectral band adjustment factors is determined for MSG/SEVIRI, Himawari-8/AHI, and MTG1/FCI from a training dataset based on MetOp/IASI hyperspectral observations. These correction functions allow to turn the observation of one sensor into an analogue observation of another sensor. This way, the same satellite retrieval—that has been usually developed for a specific instrument with a specific spectral response function—can be applied to produce long time series that go beyond one single satellite/satellite series or to cover the entire geostationary ring in a consistent way. It is shown that the mean uncorrected brightness temperature differences between corresponding channels of two imagers can be >1 K, in particular for the channels centered around 13.4 μm in the carbon dioxide absorption band and even when comparing different imager realizations of the same series, such as the four SEVIRI sensors aboard MSG1 to MSG4. The spectral band adjustment factors can remove the bias and even reduce the standard deviation in the brightness temperature difference by more than 80%, with the effect being dependent on the spectral channel and the complexity of the correction function. Further tests include the application of the spectral band adjustment factors in combination with (a) a volcanic ash cloud retrieval to Himawari-8/AHI observations of the Raikoke eruption 2019 and a comparison to an ICON-ART model simulation, and (b) an ice cloud retrieval to simulated MTG1/FCI test data with the outcome compared to the retrieval results using real MSG3/SEVIRI measurements for the same scene

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications

Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions

The spaceborne detection of volcanic ash clouds at infrared wavelengths helps to avoid regions with enhanced volcanic ash concentrations that pose a threat to aviation. Current volcanic ash data retrievals require detailed information on microphysical properties and the refractive index of volcanic ash, which are highly variable. Uncertainties in the latter currently limit the quality of volcanic ash nowcasts. Here, we introduce a novel method to calculate the complex refractive indices of volcanic ashes at wavelengths from 5 to 15 μm from measurements of their individual components based on generic petrological ash compositions. Thereby the refractive indices for volcanic glasses and bulk volcanic ashes of different chemical compositions are derived. The variability of the latter is mainly influenced by the silica content and the porosity and to a minor degree by the glass-to-crystals ratio. Calculating optical properties exhibits an equally large impact of bulk composition and grain size distribution, whereas particle shape is considered less important for particle sizes of the order 1 μm. Using these optical properties to determine brightness temperature differences between the 11 μm and 12 μm channels we show that the effect of ash composition is non-negligible for modern satellite instruments. Particularly, the dependence of the volcanic ash on the silica content (and to a much smaller extent on the glass-to-crystals ratio) is observable in its refractive index, its optical properties and the brightness temperature difference, indicating that composition might be retrievable to some degree by remote sensing methods

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called "VADUGS" (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m-2 for concentrations smaller than 2.0 g m-2 and small overestimations in the range 5 %-50 % for moderate viewing angles 35-65°, but up to 300 % for satellite viewing zenith angles close to 90 or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40 and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented

A tailored multi-model ensemble for air traffic management: Demonstration and evaluation for the Eyjafjallajökull eruption in May 2010

High quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modeling with only one model realization. Given the inherent uncertainty of such approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallaj&ouml;kull eruption in May 2010. Its use for air traffic management is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained with background modeling constrained with satellite data (a posteriori source term), (ii) its lower bound estimate, and (iii) its upper bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a fine-resolved flight level grid. These maps can be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation. Cost-optimized consideration of such hazards will result in much less impact on flight cancellations, reroutings, and traffic flow congestions.</p

Interplay between phosphorylation and palmitoylation mediates plasma membrane targeting and sorting of GAP43.

Phosphorylation and lipidation provide posttranslational mechanisms that contribute to the distribution of cytosolic proteins in growing nerve cells. The growth-associated protein GAP43 is susceptible to both phosphorylation and S-palmitoylation and is enriched in the tips of extending neurites. However, how phosphorylation and lipidation interplay to mediate sorting of GAP43 is unclear. Using a combination of biochemical, genetic, and imaging approaches, we show that palmitoylation is required for membrane association and that phosphorylation at Ser-41 directs palmitoylated GAP43 to the plasma membrane. Plasma membrane association decreased the diffusion constant fourfold in neuritic shafts. Sorting to the neuritic tip required palmitoylation and active transport and was increased by phosphorylation-mediated plasma membrane interaction. Vesicle tracking revealed transient association of a fraction of GAP43 with exocytic vesicles and motion at a fast axonal transport rate. Simulations confirmed that a combination of diffusion, dynamic plasma membrane interaction and active transport of a small fraction of GAP43 suffices for efficient sorting to growth cones. Our data demonstrate a complex interplay between phosphorylation and lipidation in mediating the localization of GAP43 in neuronal cells. Palmitoylation tags GAP43 for global sorting by piggybacking on exocytic vesicles, whereas phosphorylation locally regulates protein mobility and plasma membrane targeting of palmitoylated GAP43

Predictors of binge drinking in adolescents: ultimate and distal factors - a representative study

<p>Abstract</p> <p>Background</p> <p>As epidemiological surveys have shown, binge drinking is a constant and wide-spread problem behavior in adolescents. It is not rare to find that more than half of all adolescents engage in this behavior when assessing only the last 4 weeks of time independent of the urbanity of the region they live in. There have been several reviews on predictors of substance consumption in adolescents in general, but there has been less high quality research on predictors of binge drinking, and most studies have not been theoretically based. The current study aimed to analyze the ultimate and distal factors predicting substance consumption according to Petraitis' theory of triadic influence. We assessed the predictive value of these factors with respect to binge drinking in German adolescents, including the identification of influence direction.</p> <p>Methods</p> <p>In the years 2007/2008, a representative written survey of N = 44,610 students in the 9^thgrade of different school types in Germany was carried out (net sample). The return rate of questionnaires was 88% regarding all students whose teachers or school directors had agreed to participate in the study. In this survey, prevalence of binge drinking was investigated as well as potential predictors from the social/interpersonal, the attitudinal/environmental, and the intrapersonal fields (3 factors of Petraitis). In a multivariate logistic regression analysis, these variables were included after testing for multicollinearity in order to assess their ability to predict binge drinking.</p> <p>Results</p> <p>Prevalence of binge drinking in the last 30 days was 52.3% for the surveyed adolescents with a higher prevalence for boys (56.9%) than for girls (47.5%). The two most influential factors found to protect against binge drinking with <it>p </it>< .001 were low economic status and importance of religion. The four most relevant risk factors for binge drinking (<it>p </it>< .001) were life-time prevalence of school absenteeism/truancy, academic failure, suicidal thoughts, and violence at school in the form of aggressive behavior of teachers. The model of Petraitis was partly confirmed for Binge Drinking in German adolescents and the direction of influence factors was clarified.</p> <p>Conclusions</p> <p>Whereas some of the risk and protective factors for binge drinking are not surprising since they are known for substance abuse in general, there are two points that could be targeted in interventions that do not focus on adolescents alone: (a) training teachers in positive, reassuring behavior and constructive criticism and (b) a focus on high risk adolescents either because they have a lack of coping strategies when in a negative mood or because of their low academic achievement in combination with absenteeism from school.</p

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation

Volcanic ash clouds can damage aircrafts during flight and, thus, have the potential to disrupt air traffic on a large scale, making their detection and monitoring necessary. The new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) using the geostationary instrument MSG/SEVIRI and artificial neural networks is introduced in a companion paper. It performs pixelwise classifications and retrieves (indirectly) the mass column concentration, the cloud top height and the effective particle radius. VACOS is comprehensively validated using simulated test data, CALIOP retrievals, lidar and in situ data from aircraft campaigns of the DLR and the FAAM, as well as volcanic ash transport and dispersion multi model multi source term ensemble predictions. Specifically, emissions of the eruptions of Eyjafjallajökull (2010) and Puyehue-Cordón Caulle (2011) are considered. For ash loads larger than 0.2 g m−2 and a mass column concentration-based detection procedure, the different evaluations give probabilities of detection between 70% and more than 90% at false alarm rates of the order of 0.3–3%. For the simulated test data, the retrieval of the mass load has a mean absolute percentage error of ~40% or less for ash layers with an optical thickness at 10.8 μm of 0.1 (i.e., a mass load of about 0.3–0.7 g m−2, depending on the ash type) or more, the ash cloud top height has an error of up to 10% for ash layers above 5 km, and the effective radius has an error of up to 35% for radii of 0.6–6 μm. The retrieval error increases with decreasing ash cloud thickness and top height. VACOS is applicable even for overlaying meteorological clouds, for example, the mean absolute percentage error of the optical depth at 10.8 μm increases by only up to ~30%. Viewing zenith angles &gt;60° increase the mean percentage error by up to ~20%. Desert surfaces are another source of error. Varying geometrical ash layer thicknesses and the occurrence of multiple layers can introduce an additional error of about 30% for the mass load and 5% for the cloud top height. For the CALIOP data, comparisons with its predecessor VADUGS (operationally used by the DWD) show that VACOS is more robust, with retrieval errors of mass load and ash cloud top height reduced by &gt;10% and &gt;50%, respectively. Using the model data indicates an increase in detection rate in the order of 30% and more. The reliability under a wide spectrum of atmospheric conditions and volcanic ash types make VACOS a suitable tool for scientific studies and air traffic applications related to volcanic ash clouds