The Weather Research and Forecasting system version 3 (WRFv3) is an open source and state of the art numerical Regional Climate Model used in climate related sciences. These years the model has been successfully optimized on a wide variety of clustered compute nodes connected with high speed interconnects. This is currently the most used hardware architecture for high-performance computing (Shainer et al., 2009). As such, understanding the influence of hardware like the CPU, its interconnects, or the software on WRFs performance is crucial for saving computing time. This is important because computing time in general is rare, resource intensive, and hence very expensive. This paper evaluates the influence of different compilers on WRFs performance, which was found to differ up to 26 %. The paper also evaluates the performance of different Message Passing Interface library versions, a software which is needed for multi CPU runs, and of different WRF versions. Both showed no significant influence on the performance for this test case on the used High Performance Cluster (HPC) hardware. Emphasis is also laid on the applied non-standard method of performance measuring, which was required because of performance fluctuations between identical runs on the used HPC. Those are caused by contention for network resources, a phenomenon examined for many HPCs (Wright et al., 2009).

Introduction: The composition of the microbiome is subject to a variety of factors, such as eating behavior and the history of medical treatment. The interest in the impact of the microbiome on the stress response is mainly explained by the lack of development of new effective treatments for stress-related diseases. This scoping review aims to present the current state of research regarding the impact of bacterial strains in the gut on the stress response in humans in order to not only highlight these impacts but to also suggest potential intervention options. Methods: We included full-text articles on studies that: (a) were consistent with our research question; and (b) included the variable stress either using biomedical parameters such as cortisol or by examining the subjective stress level. Information from selected studies was synthesized from study designs and the main findings. Results: Seven studies were included, although they were heterogenous. The results of these studies do not allow a general statement about the effects of the selected bacterial strains on the stress response of the subjects and their precise pathways of action. However, one of the works gives evidence that the consumption of probiotics leads to a decrease in blood pressure and others show that stress-induced symptoms (including abdominal pain and headache) in healthy subjects could be reduced. Conclusion: Due to different intake period and composition of the bacterial strains administered to the subjects, the studies presented here can only provide a limited meaningful judgement. As these studies included healthy participants between the ages of 18 and 60 years, a generalization to clinical populations is also not recommended. In order to confirm current effects and implement manipulation of the microbiome as a treatment method for clinical cases, future studies would benefit from examining the effects of the intestinal microbiome on the stress response in a clinical setting.

Remote sensing has widely been used in urban climatology since it has the advantage of a simultaneous synoptic view of the full urban surface. Methods include the analysis of surface temperature patterns, spatial (biophysical) indicators for urban heat island modelling, and flux measurements. Another approach is the automated classification of urban morphologies or structural types. In this study it was tested, whether Local Climate Zones (a new typology of thermally 'rather' homogenous urban morphologies) can be automatically classified from multisensor and multitemporal earth observation data. Therefore, a large number of parameters were derived from different datasets, including multitemporal Landsat data and morphological profiles as well as windowed multiband signatures from an airborne IFSAR-DHM. The results for Hamburg, Germany, show that different datasets have high potential for the differentiation of urban morphologies. Multitemporal thermal data performed very well with up to 96.3 % overall classification accuracy with a neuronal network classifier. The multispectral data reached 95.1 % and the morphological profiles 83.2 %.The multisensor feature sets reached up to 97.4 % with 100 selected features, but also small multisensoral feature sets reached good results. This shows that microclimatic meaningful urban structures can be classified from different remote sensing datasets. Further, the potential of the parameters for spatiotemporal modelling of the mean urban heat island was tested. Therefore, a comprehensive mobile measurement campaign with GPS loggers and temperature sensors on public buses was conducted in order to gain in situ data in high spatial and temporal resolution.