Radiative budget and cloud radiative effect over the Atlantic from ship-based observations

The aim of this study is to determine cloud-type resolved cloud radiative budgets and cloud radiative effects from surface measurements of broadband radiative fluxes over the Atlantic Ocean. Furthermore, based on simultaneous observations of the state of the cloudy atmosphere, a radiative closure study has been performed by means of the ECHAM5 single column model in order to identify the model's ability to realistically reproduce the effects of clouds on the climate system. An extensive database of radiative and atmospheric measurements has been established along five meridional cruises of the German research icebreaker Polarstern. Besides pyranometer and pyrgeometer for downward broadband solar and thermal radiative fluxes, a sky imager and a microwave radiometer have been utilized to determine cloud fraction and cloud type on the one hand and temperature and humidity profiles as well as liquid water path for warm non-precipitating clouds on the other hand. Averaged over all cruise tracks, we obtain a total net (solar + thermal) radiative flux of 144 W m−2 that is dominated by the solar component. In general, the solar contribution is large for cirrus clouds and small for stratus clouds. No significant meridional dependencies were found for the surface radiation budgets and cloud effects. The strongest surface longwave cloud effects were shown in the presence of low level clouds. Clouds with a high optical density induce strong negative solar radiative effects under high solar altitudes. The mean surface net cloud radiative effect is −33 W m−2. For the purpose of quickly estimating the mean surface longwave, shortwave and net cloud effects in moderate, subtropical and tropical climate regimes, a new parameterisation was created, considering the total cloud amount and the solar zenith angle. The ECHAM5 single column model provides a surface net cloud effect that is more cooling by 17 W m−2 compared to the radiation observations. This overestimation in solar cooling is mostly caused by the shortwave impact of convective clouds. The latter show a large overestimation in solar cooling of up to 114 W m−2. Mean cloud radiative effects of cirrus and stratus clouds were simulated close to the observations

Inference of a mesoscopic population model from population spike trains

To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single neuron scale to empirical population data. To close this gap, we propose to fit such data at a meso scale, using a mechanistic but low-dimensional and hence statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous `pools' of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived, and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent to a mesoscopic population model impact the accuracy of the inferred single-neuron parameters

Bands, resonances, edge singularities and excitons in core level spectroscopy investigated within the dynamical mean field theory

Using a recently developed impurity solver we exemplify how dynamical mean field theory captures band excitations, resonances, edge singularities and excitons in core level x-ray absorption (XAS) and core level photo electron spectroscopy (cPES) on metals, correlated metals and Mott insulators. Comparing XAS at different values of the core-valence interaction shows how the quasiparticle peak in the absence of core-valence interactions evolves into a resonance of similar shape, but different origin. Whereas XAS is rather insensitive to the metal insulator transition, cPES can be used, due to nonlocal screening, to measure the amount of local charge fluctuation

Episode of unusual high solar ultraviolet radiation over central Europe due to dynamical reduced total ozone in May 2005

In late May 2005 unusual high levels of solar ultraviolet radiation were observed over central Europe. In Northern Germany the measured irradiance of erythemally effective radiation exceeded the climatological mean by more than about 20%. An extreme low ozone event for the season coincided with high solar elevation angles and high pressure induced clear sky conditions leading to the highest value of erythemal UV-radiation ever observed over this location in May since 1994. This hereafter called "ozone mini-hole" was caused by an elevation of tropopause height accompanied with a poleward advection of ozone-poor air from the tropics. The resultant increase in UV-radiation is of particular significance for human health. Dynamically induced low ozone episodes that happen in late spring can considerably enhance the solar UV-radiation in mid latitudes and therefore contribute to the UV-burden of people living in these regions

Neue Einsichten in Lehren, Lernen und Kompetenz

Ziel des Beitrags ist es, die Arbeiten der Autoren im Bereich Lehr-Lern-Forschung zusammenzufassen und ein handlungstheoretisch begründetes Konzept für eine lern-lehr-theoretische Didaktik vorzustellen. Ausgehend von einem allgemeinen begrifflichen Rahmen für eine Lern-Lehr-Theorie untersuchen wir die Ansätze der ›lerntheoretischen‹ Didaktik (Heimann, Otto & Schulz 1965) und der kritisch-konstruktiven Didaktik (Klafki 1980), die bis heute in der bundesdeutschen Lehrkräfteausbildung eine wichtige Rolle spielen. Festzuhalten ist, dass beide Ansätze lern-lehr-theoretisch nicht begründet sind, den Lern-Lehr-Zusammenhang vielmehr nur aus der Perspektive der Lehrenden thematisieren. Unter Rückgriff auf lern-lehr-theoretische Vorarbeiten der Gruppe um Eigler (1976) formulieren wir drei Kernfragen und ein Konzept einer Lern-Lehr-Theorie, das von einem mehrdimensionalen Handlungsbegriff ausgeht, Information vom Verstehen her bestimmt und herausarbeitet, dass aufgrund des unauflöslichen Zusammenspiels von Handeln und Information Verstehen der Verständigung bedarf, also Interaktion und Kommunikation zentrale Voraussetzungen für Lernen und Lehren sind. Ein solcher handlungstheoretischer Zugang zeigt auch die Schwachstellen und Inkonsistenzen der Taxonomie von Anderson & Krathwohl (2001) sowie der deutschen Übersetzung von ›knowledge‹ im Allgemeinen und ›declarative‹ sowie ›procedural knowledge‹ im Besonderen und ihre Irrelevanz für berufliche Kompetenzforschung. Das entwickelte Konzept nutzen wir, die derzeit breit rezipierten Ansätze des ›Cognitive Apprenticeship‹ (Collins, Brown & Newman 1989) und der ›Gestaltung integrierter Lernumgebungen‹ (Reinmann & Mandl 2006) zu analysieren und zu zeigen, dass beide die lerntheoretische Begründung und Stringenz des ›meaningful verbal learning‹ (Ausubel 1968) und des guided discovery learning‹ (Bruner 1966) nicht erreichen. Dieses Ergebnis führt uns zu der These, dass die derzeit betriebene Bachelorisierung der Lehrkräfteausbildung Gefahr läuft, trotz inzwischen umfangreicher lern-lehr-theoretischen Erkenntnisse vom Ziel ›mastering the teaching model‹ auf ein ›modelling the master teacher‹ (Stolurow 1965) zurückzufallen. (DIPF/Orig.)New Insights in Teaching, Learning and Competence The aim of this article is to summarize the authors\u27 work in the field of teaching and learning research and to present a concept of learning and teaching theoretical didactics based on the theory of action. We set out from a general conceptual framework of learning and teaching theory and discuss the approaches of ›learning theoretical‹ didactics (Heimann, Otto & Schulz 1965) and critical-constructivist didactics (Klafki 1980), which still play an important part in German teacher education. It has to be concluded that these two approaches have no foundation in learning and teaching theory and instead address the relationship of learning and teaching only from the teachers\u27 perspectives. Recurring to prior work in the field of learning and teaching theory by the group around Eigler (1976) we formulate three fundamental questions and a concept of learning and teaching theory which presupposes a multidimensional concept of action, defines information by reference to understanding, and points out that due to the inextricable interaction of action and information any understanding needs communication, which means that interaction and communication are essential prerequisites for learning and teaching. This action theoretical approach also reveals the weak points and inconsistencies of the taxonomy by Anderson and Krathwohl (2001) and of the German translations of ›knowledge‹ in general and ›declarative‹ and ›procedural knowledge‹ in particular as well as their irrelevance for research on vocational competence development. We employ the concept developed here to analyse the models of ›Cognitive Apprenticeship‹ (Collins, Brown & Newman 1989) and ›design of integrated learningenvironments‹ (Reinmann & Mandl 2006), which currently receive broad attention, and to show that these two concepts do not attain the learning theoretical soundness and consistency of ›meaningful verbal learning‹ (Ausubel 1968) and ›guided discovery learning‹ (Bruner 1966). This result leads to the conclusion that the current shift to Bachelor programmes in teacher education entails the risk that despite the extensive knowledge in learning and teaching theory available today the objective of ›mastering the teaching model‹ is once again replaced with ›modelling the master teacher‹ (Stolurow 1965). (DIPF/orig.

Modeling of wave-induced irradiance variability in the upper ocean mixed layer

A Monte Carlo based radiative transfer model has been developed for calculating the availability of solar radiation within the top 100 m of the ocean. The model is optimized for simulations of spatial high resolution downwelling irradiance Ed fluctuations that arise from the lensing effect of waves at the water surface. In a first step the accuracy of simulation results have been verified by measurements of the oceanic underwater light field and through intercomparison with an established radiative transfer model. Secondly the potential depth-impact of nonlinear shaped single waves, from capillary to swell waves, is assessed by considering the most favorable conditions for light focusing, i.e. monochromatic light at 490 nm, very clear oceanic water with a low chlorophyll a content of 0.1 mg m−3 and high sun elevation. Finally light fields below irregular wave profiles accounting for realistic sea states were simulated. Our simulations suggest that under open ocean conditions light flashes with 50 % irradiance enhancements can appear down to 35 m depth, and light variability in the range of ±10 % compared to the mean Ed is still possible in 100 m depth

Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning

Single-molecule localization microscopy constructs super-resolution images by the sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and a new training algorithm which enables this deep network to solve the Bayesian inverse problem of detecting and localizing single molecules. Our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. Our training algorithm combines simulation-based supervised learning with autoencoder-based unsupervised learning to make it more robust against mismatch in the generative model. We demonstrate the performance of our method on datasets imaged using a variety of point spread functions and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy in data with low fluorophore density, they are confounded by high densities. Our method significantly outperforms the state of the art at high densities and thus, enables faster imaging than previous approaches. Our work also more generally shows how to train deep networks to solve challenging Bayesian inverse problems in biology and physics

Measurement of solid precipitation with an optical disdrometer

A study about measurements of solid precipitation using an optical disdrometer is presented. The optical disdrometer is an improved version of the ODM 470 disdrometer. It allows to measure hydrometeors within a size range of 0.4 to 22 mm in diameter. <br><br> The main advantage of this instrument is its ability to estimate accurately precipitation even under strong wind conditions (Großklaus, 1996). To measure solid precipitation a geometrical model was developed to determine the mean cross-sectional area of snow crystals for different predefined shapes and sizes. It serves to develop an algorithm, which relates the mean cross sectional area of snow crystals to their maximum dimension, liquid water content, and terminal velocity. The algorithm was applied to disdrometer measurements during winter 1999/2000 in Uppsala/Sweden. Resulting precipitation was compared to independent measurements of a Geonor gauge and to manual measurements. In terms of daily precipitation the disdrometer shows a reliable performance

High-density correlation energy expansion of the one-dimensional uniform electron gas

We show that the expression of the high-density (i.e small-$r_s$) correlation energy per electron for the one-dimensional uniform electron gas can be obtained by conventional perturbation theory and is of the form \Ec(r_s) = -\pi^2/360 + 0.00845 r_s + ..., where $r_s$ is the average radius of an electron. Combining these new results with the low-density correlation energy expansion, we propose a local-density approximation correlation functional, which deviates by a maximum of 0.1 millihartree compared to the benchmark DMC calculations.Comment: 7 pages, 2 figures, 3 tables, accepted for publication in J. Chem. Phy

The impact of ice crystal shapes, size distributions and spatial structures of cirrus clouds on solar radiative fluxes

The solar radiative properties of cirrus clouds depend on ice particle shape, size, and orientation, as well as on the spatial cloud structure. Radiation schemes in atmospheric circulation models rely on estimates of cloud optical thickness only. In the present work, a Monte Carlo radiative transfer code is applied to various cirrus cloud scenarios to obtain the radiative response of uncertainties in the above-mentioned microphysical and spatial cloud properties (except orientation). First, plane-parallel homogeneous (0D) clouds with different crystal shapes (hexagonal columns, irregular polycrystals) and 114 different size distributions have been considered. The resulting variabilities in the solar radiative fluxes are in the order of a few percent for the reflected and about 1% for the diffusely transmitted fluxes. Largest variabilities in the order of 10% to 30% are found for the solar broadband absorptance. However, these variabilities are smaller than the flux differences caused by the choice of ice particle geometries. The influence of cloud inhomogeneities on the radiative fluxes has been examined with the help of time series of Raman lidar extinction coefficient profiles as input for the radiative transfer calculations. Significant differences between results for inhomogeneous and plane-parallel clouds were found. These differences are in the same order of magnitude as those arising from using extremely different crystal shapes for the radiative transfer calculations. From this sensitivity study, the ranking of cirrus cloud properties according to their importance in solar broadband radiative transfer is optical thickness, ice crystal shape, ice particle size, and spatial structure