1. Challenges for sustained observing and forecasting systems in the Mediterranean SeaJoaquín Tintoré, Nadia Pinardi, Enrique Álvarez-Fanjul, Eva Aguiar, Diego Álvarez-Berastegui, Marco Bajo, Rosa Balbin, Roberto Bozzano, Bruno Buongiorno Nardelli, Vanessa Cardin, Vlado Malačič, 2019, pregledni znanstveni članek Povzetek: The Mediterranean community represented in this paper is the result of more than 30 years of EU and nationally funded coordination, which has led to key contributions in science concepts and operational initiatives. Together with the establishment of operational services, the community has coordinated with universities, research centers, research infrastructures and private companies to implement advanced multi-platform and integrated observing and forecasting systems that facilitate the advancement of operational services, scientific achievements and mission-oriented innovation. Thus, the community can respond to societal challenges and stakeholders needs, developing a variety of fit-for-purpose services such as the Copernicus Marine Service. The combination of state-of-the-art observations and forecasting provides new opportunities for downstream services in response to the needs of the heavily populated Mediterranean coastal areas and to climate change. The challenge over the next decade is to sustain ocean observations within the research community, to monitor the variability at small scales, e.g., the mesoscale/submesoscale, to resolve the sub-basin/seasonal and inter-annual variability in the circulation, and thus establish the decadal variability, understand and correct the model-associated biases and to enhance model-data integration and ensemble forecasting for uncertainty estimation. Better knowledge and understanding of the level of Mediterranean variability will enable a subsequent evaluation of the impacts and mitigation of the effect of human activities and climate change on the biodiversity and the ecosystem, which will support environmental assessments and decisions. Further challenges include extending the science-based added-value products into societal relevant downstream services and engaging with communities to build initiatives that will contribute to the 2030 Agenda and more specifically to SDG14 and the UN's Decade of Ocean Science for sustainable development, by this contributing to bridge the science-policy gap. The Mediterranean observing and forecasting capacity was built on the basis of community best practices in monitoring and modeling, and can serve as a basis for the development of an integrated global ocean observing system. Ključne besede: observing and forecasting systems, sustained observations, ocean variability, FAIR data, climate, operational services, science with and for society, SDG's Objavljeno v DiRROS: 06.08.2024; Ogledov: 101; Prenosov: 82
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2. On the potential of ensemble forecasting for the prediction of meteotsunamis in the Balearic Islands : sensitivity to atmospheric model parameterizationsBaptiste Mourre, A. Santana, A. Buils, L. Gautreau, Matjaž Ličer, Agusti Jansá, B. Casas, B. Amengual, Joaquín Tintoré, 2021, izvirni znanstveni članek Povzetek: This study investigates the potential of ensemble forecasting using full realistic high-resolution nested atmosphere–ocean models for the prediction of meteotsunamis in Ciutadella (Menorca, Spain). The sensitivity of model results to the parameterizations of the atmospheric model is assessed considering the ten most significant recent meteotsunami events for which observations are available. Different schemes adapted to high-resolution Weather Research and Forecasting model simulations were used for the representation of cumulus, microphysics, planetary boundary layer and longwave and shortwave radiations. Results indicate a large spread of the ensemble simulations in terms of the final magnitude of the meteotsunamis. While the modeling system is shown to be able to realistically trigger tsunamigenic atmospheric disturbances in more than 90% of the situations, the small-scale characteristics of these disturbances are significantly modified with the change of parameterizations, leading to significant differences in the magnitude of the simulated sea-level response. No preferred set of parameterizations can be identified that leads to either the largest or the most realistic magnitudes in the majority of situations. Instead, the performance of a given set of parameterizations is found to change with the meteotsunami event under consideration. Importantly, the observed magnitude of the extreme sea-level oscillations lies within the range of a nine-member ensemble in 70% of the cases. This ensemble approach would then allow to generate a realistic range of possibilities in the majority of events, thus improving the realism of meteotsunami predictions compared to single deterministic forecasts. Ključne besede: meteotsunamis prediction, atmosphere, ocean modeling, ensemble forecasting, atmospheric model parameterizations Objavljeno v DiRROS: 19.07.2024; Ogledov: 117; Prenosov: 105
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3. Models for forecasting the traffic flow within the city of LjubljanaGašper Petelin, Rok Hribar, Gregor Papa, 2023, izvirni znanstveni članek Povzetek: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data. Ključne besede: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison Objavljeno v DiRROS: 28.09.2023; Ogledov: 567; Prenosov: 258
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4. Combining an occurrence model and a quantitative model for the prediction of the sanitary felling of Norway spruce because of bark beetlesMaarten De Groot, Nikica Ogris, 2022, izvirni znanstveni članek Povzetek: The European spruce bark beetle (Ips typographus L.) is an eruptive forest pest that has caused a great deal of damage in the last decades because of increasing climatic extremes. In order to effectively manage outbreaks of this pest, it is important to predict where they will occur in the future. In this study we developed a predictive model of the sanitary felling of Norway spruce (Picea abies (L.) H. Karst.) because of bark beetles. We used a time series of sanitary felling because of bark beetles from 1996 to 2020 in Slovenia. For the explanatory variables, we used soil, site, climate, geographic, and tree damage data from the previous year. The model showed that sanitary felling is negatively correlated with slope, soil depth, soil cation exchange capacity, and Standard Precipitation Index (less sanitary felling in wet years). On the other hand, soil base saturation percentage, temperature, sanitary felling because of bark beetles from the previous year, sanitary felling because of other abiotic factors from the previous year, and the amount of spruce were positively correlated with the sanitary felling of Norway spruce due to bark beetles. The model had an R2 of 0.38. A prediction was performed for 2021 combining an occurrence model and a quantitative model. The model can be used to predict the amount of sanitary felling of Norway spruce due to bark beetles and to refine the risk map for the next year, which can be used for forest management planning and economic loss predictions. Ključne besede: sanitary felling, prediction, Ips typographus, Picea abies, Slovenia, forecasting, insect outbreak forest pest Objavljeno v DiRROS: 21.02.2022; Ogledov: 798; Prenosov: 683
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5. A comparison of models for forecasting the residential natural gas demand of an urban areaRok Hribar, Primož Potočnik, Jurij Šilc, Gregor Papa, 2019, izvirni znanstveni članek Povzetek: Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events. The models were trained and tested on gas-consumption data gathered in the city of Ljubljana, Slovenia. Machine-learning models were considered, such as linear regression, kernel machine and artificial neural network. Additionally, empirical models were developed based on data analysis. Two most accurate models were found to be recurrent neural network and linear regression model. In realistic setting such trained models can be used in conjunction with a weather-forecasting service to generate forecasts for future gas demand. Ključne besede: demand forecasting, buildings, energy modeling, forecast accuracy, machine learning Objavljeno v DiRROS: 15.03.2019; Ogledov: 2481; Prenosov: 1144
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