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Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys’ data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions.

The purpose of this work is to introduce the ISPRA project aiming to realize a permanent GNSS coastal network collocated with the tide gauges of the ISPRA national network. The objective of integrating the two networks is to link the reference points of the tide gauge to an absolute ITRS reference system (International Terrestrial Reference System). In particular, the GNSS network allows the continuous monitoring of vertical movements; the medium-long term series analysis will allow to discriminate between the effect of vertical movements possibly present in the areas where the tide gauges are installed from the sea level variation due as an effect of the climate change. At the same time this network can contribute to the study of: the Italian geodynamic evolution, the subsidence phenomena, the evolution of the Italian coasts and the risk scenarios preparation especially in densely populated coastal areas.

The CALYPSO HF radar network is a permanent and fully operational observing system currently composed of four CODAR SeaSonde stations. The system is providing real-time hourly maps of sea surface currents and waves data in the Malta-Sicily Channel. The present work aims to compare significant wave height measurements by HF Radar to wave data from numerical models and satellite altimeter. This is the first time that this set of wave data are analysed since the four HF radars were installed between 2012 and 2015. Results suggest that CODAR HF Radar wave data are a reliable source of wave information even in the case of extreme events, providing an avenue to improve and complete the offer of services deriving from the CALYPSO system. Comparisons of HF radar data with both numerical sea wave model and satellite altimeter data confirm agreement, in particular for radar measurements in the annular sectors within the central range-cells which are also characterized by a more reliable and homogeneous temporal behaviour.

Global warming is making extreme wave events more intense and frequent. Hence, the importance of monitoring the sea state for marine risk assessment and mitigation is increasing day-by-day. In this work, we exploit the ubiquitous seismic noise generated by energy transfer from the ocean to the solid earth (called microseisms) to infer the sea wave height data provided by hindcast maps. To this aim, we use a combined approach based on statistical analysis and machine learning. In particular, a random forest model shows very promising results in the spatial and temporal reconstruction of sea wave height by microseisms. The observed dependence of input importance from the distance sea grid cell-seismic station suggests how the reliable monitoring of the sea state in a wide area by microseisms needs data recorded by dense networks, comprising stations evenly distributed along the coastlines.

Meteo-marine data are necessary and useful for all tasks and scientific activities of national interest involving the protection, enhancement and improvement of the marine environment. The Institut...

The CALYPSO HF radar network is a permanent and fully operational observing system currently composed of four CODAR HF stations. The system is providing realtime hourly maps of sea surface currents and wave data in the Malta-Sicily Channel since 2012. Significant wave height derived from the HF radar wave measurements are confirmed to be a reliable source of wave information even in case of extreme events. However, it is noticed that the HF radar wave data are subject to differing interfering noise in the signal from unknown sources that may be competing with transmissions in the same frequency band. These interferences lead to frequent gaps and/or outliers that affect the continuity and reliability of the data set. The aim of this work is to estimate missing values and to detect possible outliers building and fitting a Markov chain mixture model on the significant wave height data collected at the four stations. It is verified that the proposed procedure is sufficiently robust since the model estimates succeed to classify radar observations with a high percentage of missing data and to equally highlight spikes and outliers., peer-reviewed

Unsupervised classification of marine data is helpful to identify relevant sea regimes, i.e. specific shapes that the distribution of wind and wave data takes under latent environmental conditions. We cluster multivariate marine data by estimating a multivariate hidden Markov model that integrates multivariate von Mises and normal densities. Taking this approach, we obtain a classification that accounts for the mixed (linear and circular) support of the observations, the temporal autocorrelation of the data, and the occurrence of missing values.

This work focuses on the reconstruction of Sea Surface Temperature (SST) diurnal cycle through combination of numerical model analyses and geostationary satellite measurements. The approach takes advantage of geostationary satellite observations as the diurnal signal source to produce gap-free optimally interpolated (OI) hourly \{SST\} fields using model analyses as first-guess. The resulting \{SST\} anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing one to interpolate \{SST\} anomalies using satellite data acquired at different times of the day. The method is applied to reconstruct the hourly Mediterranean \{SST\} field during summer 2011 using \{SEVIRI\} data and Mediterranean Forecasting System analyses. A synthetic cloud reconstruction experiment demonstrated that the \{OI\} \{SST\} method is able to reconstruct an unbiased \{SST\} field with a RMS = 0.16 °C with respect to \{SEVIRI\} observations. The \{OI\} interpolation estimate, the model first guess and the \{SEVIRI\} data are evaluated using drifter and mooring measurements. Special attention is devoted to the analysis of diurnal warming (DW) events that are particularly frequent in the Mediterranean Sea. The model reproduces quite well the Mediterranean \{SST\} diurnal cycle, except for the \{DW\} events. Due to the thickness of the model surface layer, the amplitude of the model diurnal cycle is often less intense than the corresponding \{SEVIRI\} and drifter observations. The Diurnal \{OI\} \{SST\} (DOISST) field, resulting from the blending of model and \{SEVIRI\} data via optimal interpolation, reproduces well the diurnal cycle including extreme \{DW\} events. The evaluation of \{DOISST\} products against drifter measurements results in a mean bias of − 0.07 °C and a \{RMS\} of 0.56 °C over interpolated pixels. These values are very close to the corresponding statistical parameters estimated from \{SEVIRI\} data (bias = − 0.16 °C, RMS = 0.47 °C). Results also confirm that part of the mean bias between temperature measured by moorings at 1 m depth and the satellite observations can be ascribed to the different nature of the measurements (bulk versus skin).

The Italian Data Buoy Network consists of 15 oceanographic buoys, deployed along Italian coasts, on a seabed 100 meters deep. It collects directional sea wave data, meteorological data, sea surface temperature and in some cases quality parameters. Acoustic Doppler Current Profilers (ADCP) and hyperspectral probes will be added in the future. The network will be also the main framework for tsunami wave study in the central Mediterranean Sea, and an early technological platform to study low-frequency sea motion detected in open sea using high-precision, multichannel, multisatellite GPS/Glonass/Galileo receivers and sea bottom instrumentation equipped with nano-resolution pressure sensors.

A web-based validation platform has been developed at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for the Near Real Time validation of the MyOcean- Mediterranean Monitoring and Forecasting Centre products (Med-MFC). A network for the collection of the in-situ observations, the nested sub-basin forecasting systems model data (provided by the partners of the Mediterranean Operational Oceanography Network, MOON) and the Sea Surface Temperature (SST) satellite data has been developed and is updated every day with the new available data. The network collects temperature, salinity, currents and sea level data. The validation of the biogeochemical forecast products is done by use of ocean colour satellite data produced for the Mediterranean Sea. All the data are organized in an ad hoc database interfaced with a dedicated software which allows interactive visualizations and statistics (CalVal SW). This tool allows to evaluate NRT products by comparison with independent observations for the first time. The heterogeneous distribution and the scarcity of moored observations reflect with large areas uncovered with measurements. Nevertheless, the evaluation of the forecast at the locations of observations could be very useful to discover sub-regions where the model performances can be improved, thus yielding an important complement to the basin-mean statistics regularly calculated for the Mediterranean MFC products using semi-independent observations., peer-reviewed

1. The phenomenon Abstract Sea level oscillations are the superposition of many contributions, the main ones being astronomic and meteorolog- ical low-frequency tides. In the Ligurian Sea meteo-tide components, being most ample than astronomic fluctuations, drive water exchange in harbours. The present note shows the first results on the port of Genoa concerning a coheren- cy study between atmospheric variation and corresponding sea level adjustment (meteorological tide). The Newton- ian forecasting method of meteorological tides is based on measurements of time elapsing between barometric sea level unbalance (∆g) and its meteorological tide compensation (inverse barometer component). Meteorological tide component is independent of the Earth-Moon-Sun gravitational relationships, and parameters related to the shifted water mass are too many to describe the phenomenon analytically (basin topography, barometric strength position and time, chemical water quality, off-shore sea circulation, etc.). Hence meteorological tide cannot be accurately foreseen by atmospheric pressure measurements only. A gravimeter can detect the geodetic imbalance starting time and a tide-gauge can detect the Newtonian compensation (tide wave) coming time. The difference between these two times is the meteorological tide delay. An opportune statistic of this delay provides an experimental law typical for each harbour to forecast the meteo-tide compensation wave delay. This paper describes the methodological proce- dure adopted and the first evidences of the phenomenon in Genoa harbour.

The aim of this paper is to give an overview of the various measurement collections that contribute to the observation of Sea Surface Temperature in Italian Seas. Long term time series are collected since the 19th century but are affected by a large systematic errors. Despite the lack of accuracy, their contribution to the analysis of trends is fundamental into the climate change context. On the other hand, recent systematic observations are more accurate and continuous in space and time. Short term time series better define the status of the temperature during the last years in term of variability and dynamics.