1. Coastal high-frequency radars in the Mediterranean : Applications in support of science priorities and societal needsEmma Reyes, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Vanessa Cardin, Daniela Cianelli, Giuseppe Ciraolo, Matjaž Ličer, 2022, review article Abstract: The Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals. Keywords: coastal monitoring, Mediterranean Sea, multi-platform observing systems, oceanography Published in DiRROS: 05.08.2024; Views: 109; Downloads: 102
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2. Coastal high-frequency radars in the Mediterranean : Status of operations and a framework for future developmentPablo Lorente, Eva Aguiar, Michele Bendoni, Maristella Berta, Carlo Brandini, Alejandro Cáceres-Euse, Fulvio Capodici, Daniela Cianelli, Giuseppe Ciraolo, Lorenzo Corgnati, Matjaž Ličer, 2022, review article Abstract: Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with high-frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near-real time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavors between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to do the following: (i) harmonize deployment and maintenance practices; (ii) standardize data, metadata, and quality control procedures; (iii) centralize data management, visualization, and access platforms; and (iv) develop practical applications of societal benefit that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events, the investigation of transport processes, and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socioeconomic, technical, and scientific challenges to be faced during the implementation of this integrated HFR regional network. Keywords: coastal regions, Mediterranean Sea, multi-platform observing systems, oceanography Published in DiRROS: 05.08.2024; Views: 99; Downloads: 112
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3. DELWAVE 1.0 : deep learning surrogate model of surface wave climate in the Adriatic BasinPeter Mlakar, Antonio Ricchi, Sandro Carniel, Davide Bonaldo, Matjaž Ličer, 2024, original scientific article Abstract: We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal. Keywords: surrogate modelling, deep learning, DEep Learning WAVe Emulating model, DELWAVE, Simulating WAves Nearshore, SWAN Published in DiRROS: 05.08.2024; Views: 101; Downloads: 73
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4. Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere-ocean couplingMatjaž Ličer, Peter Smerkol, Anja Fettich, Michalis Ravdas, Alexandros Papapostolou, Anneta Mantziafou, Benedikt Strajnar, Jure Cedilnik, Maja Jeromel, Jure Jerman, Sašo Petan, Vlado Malačič, Sarantis Sofianos, 2016, original scientific article Abstract: We have studied the performances of (a) a two-way coupled atmosphere%ocean modeling system and (b) one-way coupled ocean model (forced by the atmosphere model), as compared to the available in situ measurements during and after a strong Adriatic bora wind event in February 2012, which led to extreme air%sea interactions. The simulations span the period between January and March 2012. The models used were ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) (4.4 km resolution) on the atmosphere side and an Adriatic setup of Princeton ocean model (POM) (1%=30%1%=30 angular resolution) on the ocean side. The atmosphere%ocean coupling was implemented using the OASIS3-MCT model coupling toolkit. Two-way coupling ocean feedback to the atmosphere is limited to sea surface temperature. We have compared modeled atmosphere%ocean fluxes and sea temperatures from both setups to platform and CTD (conductivity, temperature, and depth) measurements from three locations in the northern Adriatic.We present objective verification of 2m atmosphere temperature forecasts using mean bias and standard deviation of errors scores from 23 meteorological stations in the eastern part of Italy. We show that turbulent fluxes from both setups differ up to 20° during the bora but not significantly before and after the event. When compared to observations, two-way coupling ocean temperatures exhibit a 4 times lower root mean square errors (RMSE) than those from one-way coupled system. Two-way coupling improves sensible heat fluxes at all stations but does not improve latent heat loss. The spatial average of the two-way coupled atmosphere component is up to 0.3 °C colder than the one-way coupled setup, which is an improvement for prognostic lead times up to 20 h. Daily spatial average of the standard deviation of air temperature errors shows 0.15 °C improvement in the case of coupled system compared to the uncoupled. Coupled and uncoupled circulations in the northern Adriatic are predominantly wind-driven and show no significant mesoscale differences. Keywords: sea, marine water, numerical modeling, physical oceanography, dense water, bora wind, Adriatic sea, Mediterranean sea, Adriatic shelf Published in DiRROS: 26.07.2024; Views: 117; Downloads: 94
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5. Lagrangian modelling of a person lost at sea during the Adriatic scirocco storm of 29 October 2018Matjaž Ličer, Solène Estival, Catalina Reyes-Suarez, Davide Deponte, Anja Fettich, 2020, original scientific article Abstract: On 29 October 2018 a windsurfer's mast broke about 1 km offshore from Istria during a severe scirocco storm in the northern Adriatic Sea. He drifted in severe marine conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory and to gain insight into his swimming and paddling strategy. Part of survivor's trajectory was verified using high-frequency radar surface current observations as inputs for Lagrangian temporal back-propagation from the beaching site. Back-propagation simulations were found to be largely consistent with the survivor's reconstruction. We then attempted a Lagrangian forward-propagation simulation of his trajectory by performing a leeway simulation using the OpenDrift tracking code using two object types: (i) person in water in unknown state and (ii) person with a surfboard. In both cases a high-resolution (1 km) setup of the NEMO v3.6 circulation model was employed for the surface current component, and a 4.4 km operational setup of the ALADIN atmospheric model was used for wind forcing. The best performance is obtained using the person-with-a-surfboard object type, giving the highest percentage of particles stranded within 5 km of the beaching site. Accumulation of particles stranded within 5 km of the beaching site saturates 6 h after the actual beaching time for all drifting-particle types. This time lag most likely occurs due to poor NEMO model representation of surface currents, especially in the final hours of the drift. A control run of wind-only forcing shows the poorest performance of all simulations. This indicates the importance of topographically constrained ocean currents in semi-enclosed basins even in seemingly wind-dominated situations for determining the trajectory of a person lost at sea. Published in DiRROS: 22.07.2024; Views: 126; Downloads: 85
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6. DINCAE 1.0 : a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observationsAlexander Barth, Aida Alvera-Azcárate, Matjaž Ličer, Jean-Marie Beckers, 2020, original scientific article Abstract: A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction. Published in DiRROS: 19.07.2024; Views: 133; Downloads: 92
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7. Integrated sea storm management strategy : the 29 October 2018 event in the Adriatic SeaChristian Ferrarin, Andrea Valentini, Martin Vodopivec, Dijana Klaric, Giovanni Massaro, Marco Bajo, Francesca De Pascalis, Amedeo Fadini, Michol Ghezzo, Stefano Menegon, Lidia Bressan, Silvia Unguendoli, Anja Fettich, Jure Jerman, Matjaž Ličer, Lidija Fustar, Alvise Papa, Enrico Carraro, 2020, original scientific article Abstract: Addressing coastal risks related to sea storms requires an integrative approach which combines monitoring stations, forecasting models, early warning systems, and coastal management and planning. Such great effort is sometimes possible only through transnational cooperation, which becomes thus vital to face, effectively and promptly, the marine events which are responsible for damage impacting the environment and citizens' life. Here we present a shared and interoperable system to allow a better exchange of and elaboration on information related to sea storms among countries. The proposed integrated web system (IWS) is a combination of a common data system for sharing ocean observations and forecasts, a multi-model ensemble system, a geoportal, and interactive geo-visualisation tools to make results available to the general public. The multi-model ensemble mean and spread for sea level height and wave characteristics are used to describe three different sea condition scenarios. The IWS is designed to provide sea state information required for issuing coastal risk alerts over the analysed region as well as for being easily integrated into existing local early warning systems. This study describes the application of the developed system to the exceptional storm event of 29 October 2018 that caused severe flooding and damage to coastal infrastructure in the Adriatic Sea. The forecasted ensemble products were successfully compared with in situ observations. The hazards estimated by integrating IWS results in existing early warning systems were confirmed by documented storm impacts along the coast of Slovenia, Emilia-Romagna and the city of Venice. For the investigated event, the most severe simulated scenario results provide a realistic and conservative estimation of the peak storm conditions to be used in coastal risk management. Keywords: sea storms, integrated web system (IWS) Published in DiRROS: 19.07.2024; Views: 108; Downloads: 107
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8. 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, original scientific article Abstract: 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. Keywords: meteotsunamis prediction, atmosphere, ocean modeling, ensemble forecasting, atmospheric model parameterizations Published in DiRROS: 19.07.2024; Views: 110; Downloads: 100
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9. Multi-platform study of the extreme bloom of the barrel jellyfish Rhizostoma pulmo (Cnidaria: Scyphozoa) in the northernmost gulf of the Mediterranean Sea (Gulf of Trieste) in April 2021Nydia Catalina Reyes Suárez, Valentina Tirelli, Laura Ursella, Matjaž Ličer, Massimo Celio, Vanessa Cardin, 2022, original scientific article Abstract: On 7 April 2021, an exceptional bloom of the scyphomedusa Rhizostoma pulmo was observed in the Gulf of Trieste (Italy). Blooms of this species in the northern Adriatic Sea have been reported since the late 1800s: the density of jellyfish observed in 2021 reached more than 10 specimens per square metre. We analyse the bloom from a multi-platform approach using observations and model data at different timescales. We attempt to explain the intensity of the bloom as a consequence of thermohaline and hydrodynamical conditions in the gulf. Meteo-oceanographic conditions that may have contributed to the exceptional aggregation of jellyfish observed along the northernmost coast of the Adriatic Sea are discussed in detail. Specifically, our results indicate that this bloom was enabled by (1) the presence of a high number of jellyfish in the gulf, probably linked to the anomalously warm sea conditions in spring 2020 and winter 2021, which may have favoured a longer reproductive period and enhanced survival of adult R. pulmo, respectively; and (2) strong wind events, such as the bora wind for the Gulf of Trieste, which enhanced upwelling and mixing processes in the gulf, bringing the jellyfish from the deeper waters to the surface and clustering them along the coast. Keywords: jellyfishes, Gulf of Trieste, Mediterranean Sea, hydrobiology, marine biology Published in DiRROS: 18.07.2024; Views: 129; Downloads: 99
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10. Bacterial indicators are ubiquitous members of pelagic microbiome in anthropogenically impacted coastal ecosystemNeža Orel, Eduard Fadeev, Katja Klun, Matjaž Ličer, Tinkara Tinta, Valentina Turk, 2022, original scientific article Abstract: Coastal zones are exposed to various anthropogenic impacts, such as different types of wastewater pollution, e.g., treated wastewater discharges, leakage from sewage systems, and agricultural and urban runoff. These various inputs can introduce allochthonous organic matter and microbes, including pathogens, into the coastal marine environment. The presence of fecal bacterial indicators in the coastal environment is usually monitored using traditional culture-based methods that, however, fail to detect their uncultured representatives. We have conducted a year-around in situ survey of the pelagic microbiome of the dynamic coastal ecosystem, subjected to different anthropogenic pressures to depict the seasonal and spatial dynamics of traditional and alternative fecal bacterial indicators. To provide an insight into the environmental conditions under which bacterial indicators thrive, a suite of environmental factors and bacterial community dynamics were analyzed concurrently. Analyses of 16S rRNA amplicon sequences revealed that the coastal microbiome was primarily structured by seasonal changes regardless of the distance from the wastewater pollution sources. On the other hand, fecal bacterial indicators were not affected by seasons and accounted for up to 34% of the sequence proportion for a given sample. Even more so, traditional fecal indicator bacteria (Enterobacteriaceae) and alternative wastewater-associated bacteria (Lachnospiraceae, Ruminococcaceae, Arcobacteraceae, Pseudomonadaceae and Vibrionaceae) were part of the core coastal microbiome, i.e., present at all sampling stations. Microbial source tracking and Lagrangian particle tracking, which we employed to assess the potential pollution source, revealed the importance of riverine water as a vector for transmission of allochthonous microbes into the marine system. Further phylogenetic analysis showed that the Arcobacteraceae in our data set was affiliated with the pathogenic Arcobacter cryaerophilus, suggesting that a potential exposure risk for bacterial pathogens in anthropogenically impacted coastal zones remains. We emphasize that molecular analyses combined with statistical and oceanographic models may provide new insights for environmental health assessment and reveal the potential source and presence of microbial indicators, which are otherwise overlooked by a cultivation approach. Published in DiRROS: 16.07.2024; Views: 127; Downloads: 98
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