In January 2020, the storm Gloria hit the Western Mediterranean Sea causing severe coastal damages, destruction of infrastructures, flooding and several casualties. This extreme event was characterized by strong Eastern winds, record-breaking waves heights and periods, and a storm surge that locally beat the record along Valencia’s coastline. This paper analyses the dynamic evolution of sea level during this storm. The study employs both the in situ data and the operational forecasts of the PORTUS early warning system. Tide gauge data are analyzed on the different temporal scales that contribute to total sea level: long-term and seasonal, tides and storm surges, and higher frequency oscillations. It was found that, due to the unusual long wave periods, infragravity waves were generated and dominate the high frequency energy band, contributing significantly to extreme sea level records. This is a relevant finding, since this kind of oscillations are usually associated with larger basins, where swell can develop and propagate. The impact of sea level rise is also analyzed and considered relevant. A multi-model ensemble storm surge forecasting system is employed to study the event. The system was able to correctly forecast the surge, and the measured data were always inside the confidence bands of the system. The differences of the results obtained by the available operational forecasting system integrated into the ensemble, including those from Copernicus Marine Service, are described. All the models provided useful forecasts during the event, but differences with measured data are described and connected with the known limitations in physics (for example, barotropic vs. baroclinic) and set-up of the models (model domain, lack of tides and different inverse barometer implementations at the open boundaries amongst others).

The progressive upgrade of tide gauges to match tsunami warning requirements, tied with an upgrade of tide gauges with 1-min or less sampling and latency, has led to a huge amount of data available worldwide for studies of coastal hazards related to high-frequency sea level oscillations. This upgrade in the observation network poses a challenge in matching the operational data flow, quality control and processing, as well as an opportunity for a more immediate evaluation and understanding of the physical phenomena contained in the raw data, such as meteotsunamis and infragravity waves. The main purpose of this study is to present a new operational tool that enables, for the first time, user-friendly and fast exploitation of an up to now hidden information on high-frequency sea level oscillations. Developed and implemented for 40 tide gauges at the main ports of the Spanish coast, the new tool is based on the automatic analysis of 2-Hz raw data and the online publication of relevant products in near real time. It includes an event detection algorithm and a display calendar to select and review historical events, resulting in a revolutionary advanced toolbox, a new window to phenomena that affects ports operations and infrastructures. This toolbox, combined with the open dataset, provides the first steps for considering HFSLO in the definition of operational risk management. Dealing with these raw data in near real time requires careful selection of appropriate algorithms and quality control procedures, with therefore additional difficulties, that are discussed in this paper.

The sea level station operating since 1996 at Mazagón (Huelva, Spain) has been progressively upgraded to fit tsunami warning requirements, due to its location in one of the main regions at risk. Its radar water level sensor was complemented in 2017, with the addition of a pressure sensor. The performance of both sea level sensors and their response to sea level oscillations, at different frequencies, is assessed. Particular emphasis is put on the effect of extreme events, such as Storm Emma, when alternative methods to obtain 1-min data are tested, in contrast to the one based on arithmetic means. The overall differences are small, for the whole period of study (centered-root-mean-square-error below 1cm, for 5-min and hourly data; similar tidal parameters and sea level oscillations with periods between 30s and 5 min). However, during Storm Emma, the pressure sensor presents sensibly lower readings than the radar, with the centeredroot-mean-square-error rising to 80mm on the 2 nd of March of 2018. A new method to compute 1-min data, based on medians, reduced this value to 10 mm for the same day. Keywords sea level; tide gauge; wave storm; copula 1 Introduction Sea level changes in a coastal area can have significant social, environmental and economical implications (Pugh (1987), Yin et al. (2010); Curtis et al. (2011); Sánchez-Arcilla et al. ( 2016)). In particular, a rise in mean sea levels can potentially reduce the area of usable land in low-lying regions, increase the impact of storms and affect the load capacity of a vessel within a harbor. Moreover, sudden water surface elevation changes (as generated by meteotsunamis or infragravity waves) can further affect these vessels. Tsunamis can definitely cause devastating floods and loss of human lives and properties. Therefore, it is a priority in any coastal country to build a robust coastal sea level network that monitors the ocean extreme conditions and long-term sea level changes, as well as to develop models and guidelines that could better protect these regions (Colosi and Munk (2006)). In Spain, such a network exists under the name REDMAR (Pérez-Gómez et al. ( 2014)), and has been operated by Puertos del Estado (Ports of Spain) since 1992.Radar water level sensors have been more commonly used in recent times as the primary tide gauges to measure the total sea level ( ) , due to their high accuracy and easy maintenance (Woodworth and Smith (2003); Pérez-Gómez et al. (2014); IOC (2016)). The REDMAR network is presently composed of 39 stations located at the main Spanish harbors of the Iberian Peninsula, the Balearic Islands, the Canary Islands and the city of Melilla (north Africa). Its primary instrumentation is the MIROS Range Finder radar water level sensor, a Frequency Modulated -Continuous Wave (FMCW) type of radar ("radar" or "radar sensor"), that transmits a continuous frequency modulated microwave chirp signal and receives the echo from the water surface. The signal propagation delay given by the distance from the antenna to the water surface causes a...

The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semi-supervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).

<p>Ocean wave forecasting is highly demanded by end-users. There is a pressing need for reliable forecasts, to be applied in emergency services, harbour logistics, search-and-rescue operations, renewable energy or pollutant transport. In addition to this wide variety of uses, the coastal zone represents a modelling challenge due to the joint superposition of physical processes that make it a highly dynamic environment (including wind, waves, circulation and air-sea-land interactions).</p><p>In the observational side, remote sensing products such as those derived from Satellite Synthetic Aperture Radar (SAR, e.g. from the Sentinel missions) and High Frequency Radar (HFR, e.g. available at the Copernicus Marine Service - In Situ TAC) offers vast quantities of high-resolution spatio-temporal fields. However, their applicability within the operational ocean forecasts services is not straightforward.</p><p>The Copernicus Marine Service Evolution KAILANI project (2022 - 2024) aims to enhance the Copernicus Marine regional wave forecasts by improving the forcings required by spectral wave models: i.e. wind forcings and surface current fields. This enhancement comes from blending remote sensing observations with wind and surface currents forecasts. Artificial Intelligence Neural Networks (ANNs) has been proposed as the basis for this blending, as they allow to extract complex spatio-temporal features from remote-sensing data.</p><p>The impact on bias and error reduction would be assessed by testing these blended fields under a preoperational environment. The Iberia-Biscay-Ireland (IBI) area has been selected for this Proof of Concept, due to the good coverage of HFR along its coastline. Selection of pilot study sites in areas at the Cantabrian Sea (macrotidal), the Canary Islands (mesotidal), the NW Mediterranean (microtidal), and in the hot spot that is the Gibraltar Strait will ensure that KAILANI applicability ranges different environments.</p><p>This methodology focuses on the post-processing of the forcings. Then, it could be a complement for Data Assimilation algorithms. If successful, the proposed KAILANI methodology could be exportable to different Copernicus Marine Monitoring and Forecasting Centers (MFCs); without significant changes in their numerical codes and operation chain. Finally, the expected enhancement of the delivered coastal wave spectra and their integrated parameters (i.e. wave height, period and direction) will be key to foster downstream nearshore applications.</p>