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- Geerts, Bart, et al.
(författare)
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The COMBLE Campaign : A Study of Marine Boundary Layer Clouds in Arctic Cold-Air Outbreaks
- 2022
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Ingår i: Bulletin of The American Meteorological Society - (BAMS). - 0003-0007 .- 1520-0477. ; 103:5, s. E1371-E1389
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Tidskriftsartikel (refereegranskat)abstract
- One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary layer structure, aerosol, cloud, and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling testbed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.
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| 3. |
- Arias, C, et al.
(författare)
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Diversity in orthopaedics and traumatology: a global perspective
- 2020
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Ingår i: EFORT open reviews. - : Bioscientifica. - 2058-5241 .- 2396-7544. ; 5:10, s. 743-752
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Tidskriftsartikel (refereegranskat)abstract
- Europe represents true diversity, with cultural, linguistic and geopolitical variation spanning a large geographical area. Politics for many of its 750 million inhabitants revolves around the European Union (EU) and its 27 member states. The overarching goal of the EU is to promote peace and the values of the union (inclusion, tolerance, justice, solidarity and non-discrimination).1,2 EFORT was created to connect orthopaedic associations across Europe, fostering relationships between member countries that celebrated diversity and facilitated the exchange of knowledge. Whilst the global landscape changes and politics attempts to interfere in how we live our lives, it is important to remember that a strong organization is a diverse one that evolves over time. Various initiatives exist across the global landscape to support diversity in terms of culture; gender; black, Asian and minority ethnic (BAME) groups; disability groups; lesbian, gay, bisexual, transgender and queer (or questioning) and others (LGBTQ+); and the ‘ageing’ surgeon. This article explores the creation of some of these initiatives and how they have been supported by different orthopaedic organizations. Cite this article: EFORT Open Rev 2020;5:743-752. DOI: 10.1302/2058-5241.5.200022
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- Kollias, Stefanos, et al.
(författare)
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Machine learning for analysis of real nuclear plant data in the frequency domain
- 2022
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Ingår i: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- Machine Learning is used in this paper for noise-diagnostics to detect defined anomalies in nuclear plant reactor cores solely from neutron detector measurements. The proposed approach leverages advanced diffusion-based core simulation tools to generate large amounts of simulated data with different types of driving perturbations originating at all theoretically possible locations in the core. Specifically the CORE SIM+ modelling framework is employed, which generates these data in the frequency domain. We train using these vast quantities of simulated data state-of-the-art machine and deep learning models which are used to successfully perform semantic segmentation, classification and localisation of multiple simultaneously occurring in-core perturbations. Actual plant data are then considered, provided by two different reactors, including no labels about perturbation existence. A domain adaptation methodology is subsequently developed to extend the simulated setting to real plant measurements, which uses self-supervised, or unsupervised learning, to align the simulated data with the actual plant data and detect perturbations, whilst classifying their type and estimating their location. Experimental studies illustrate the successful performance of the developed approach and extensions are described that indicate a great potential for further research.
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