Novel Applications of State-of-the-Art Gamma-Ray Imaging Technique: From Nuclear Decommissioning and Radioprotection to Radiological Characterization and Safeguards

Gamma-ray imaging is a powerful technique subjected to important research efforts in nonmedical fields, providing information about the possible spatial distribution of radioactive materials emitting photons and potential contamination spots, in generic area survey or to specific component analyses. This capability opens up a range of possible applications in nuclear installations and radioactive waste management sites, where radiation survey protocols and radiological characterization of items may be highly and positively impacted by this technique. In this work, a new-generation 3-D pixelated CdZnTe gamma-ray imaging and spectrometry detector has been used in the context of the TRIGA RC-1 Research Reactor at the ENEA Casaccia Research Centre to test several applications where gamma-ray imaging can provide valuable information otherwise unknown (with equivalent level of accuracy and effort). Experiments carried out range from radiological survey, where hotspots are identified and radioactive items are sorted from conventional waste to improvements in the quantification of gamma emitters via gamma-spectrometry analysis, and from safeguards and nonproliferation purposes (e.g., providing methods to assess the amount of special nuclear material (SNM), which remains fixed and unchanged in time) up to radiation protection issues (e.g., identification of unexpected contributions to personnel total exposure). The results obtained in this experimental campaign, as well as the validations provided by comparison with "traditional" methods, demonstrate the applicability of state-of-the-art gamma-ray imaging systems to the presented tasks, with consequences that could positively impact the current radiation survey routines and radiological characterization protocols followed at ENEA TRIGA RC-1 as well as other installations

The value-add of tailored seasonal forecast information for industry decision-making

There is a growing need for more systematic, robust and comprehensive in-formation on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments which focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification and address both the quantitative (e.g. economic) and qualitative (e.g. social) value of climate services. The twelve case studies which formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps, focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies it was pos-sible to apply quantitative economic valuation methods: econometric modelling was used for five case studies while three case studies used both cost-loss (relative economic value) analysis and avoided costs. The case studies illustrate the challenges in attempting to produce quantitative estimates of the economic value add of these forecasts. At the same time, many of them highlight how practical value for users – transcending the actual economic value – can be enhanced, for example, through the provision of climate services as an exten-sion to their current use of weather forecasts and with the visualisation tailored towards the user

Development of an operational seasonal forecast in Colombia and Peru by mean of statistical downscaling of the SEAS5-Copernicus data

&amp;lt;p&amp;gt;Enel, as most of the Energy Players, has an important exposure on weather risk due to the indirect effect of the power demand and to the direct effects on renewable production. A large component of such risk comes from the hydroelectric production, this is especially true in Southern America where, in some countries, it can represent up to 70% of the total production. We present a practical development of an operational chain to extract information from the seasonal forecasts produced by SEAS5. It works on some catchments in Colombia and Peru with the aim to provide an ensemble forecast of monthly precipitations at a high resolution from the fields at low resolution provided by Copernicus. To produce the high-resolution fields of precipitations we developed a procedure based on Lorenz et al. (2021); for our scope, the biases of the SEAS5 forecasts&amp;lt;strong&amp;gt; &amp;lt;/strong&amp;gt;are corrected following a reference climatology obtained from the SEAS5 hindcasts that is calibrated over the cumulative distribution function calculated be mean of historical measurements of the IDEAM weather stations. The method and preliminary results as well as the validation will be shown in this work.&amp;lt;/p&amp;gt;</jats:p

A Parameterization of Sticking Efficiency for Collisions of Snow and Graupel with Ice Crystals: Theory and Comparison with Observations

A new parameterization of sticking efficiency for aggregation of ice crystals onto snow and graupel is presented. This parameter plays a crucial role for the formation of ice precipitation and for electrification processes. The parameterization is intended to be used in atmospheric models simulating the aggregation of ice particles in glaciated clouds. It should improve the ability to forecast snow.Based on experimental results and general considerations of collision processes, dependencies of the sticking efficiency on temperature, surface area, and collision kinetic energy of impacting particles are derived. The parameters have been estimated from some laboratory observations by simulating the experiments and minimizing the squares of the errors of the prediction of observed quantities. The predictions from the new scheme are compared with other available laboratory and field observations. The comparisons show that the parameterization is able to reproduce the thermal behavior of sticking efficiency, observed in published laboratory studies, with a peak around -15 degrees C corresponding to dendritic vapor growth of ice.Finally, a new theory of sticking efficiency is proposed. It explains the empirically derived parameterization in terms of a probability distribution of the work that would be required to separate two contacting particles colliding in all possible ways among many otherwise identical collisions of the same pair with a given initial collision kinetic energy. For each collision, if this work done would exceed the initial collision kinetic energy, then there is no separation after impact. The probability of that occurring equals the sticking efficiency

Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project

&amp;lt;p&amp;gt;A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.&amp;lt;/p&amp;gt; </jats:p