Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

Abstract. This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin

Time-clustering of wave storms in the Mediterranean Sea

In this contribution we identify storm time-clustering in the Mediterranean Sea through a comprehensive analysis of the Allan Factor. This parameter is evaluated from long time series of wave height provided by oceanographic buoy measurements and hindcast re-analysis of the whole basin, spanning the period 1979-2014 and characterized by a horizontal resolution of about 0.1 degree in longitude and latitude and a temporal sampling of one hour (Mentaschi et al., 2015). The nature of the processes highlighted by the AF and the spatial distribution of the parameter are both investigated. Results reveal that the Allan Factor follows different curves at two distinct time scales. The range of time scales between 12 hrs to 50 days is characterised by a departure from the Poisson distribution. For timescales above 50 days, a cyclic Poisson process is identified. The spatial distribution of the Allan Factor reveals that the clustering at smaller time scales is present in the North-West of the Mediterranean, while seasonality is observed in the whole basin. This analysis is believed to be important to assess the local increased flood and coastal erosion risks due to storm clustering

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

Assessment of global wave models on regular and unstructured grids using the Unresolved Obstacles Source Term

The Unresolved Obstacles Source Term (UOST) is a general methodology for parameterizing the dissipative effects of subscale islands, cliffs, and other unresolved features in ocean wave models. Since it separates the dissipation from the energy advection scheme, it can be applied to any numerical scheme or any type of mesh. UOST is now part of the official release of WAVEWATCH III, and the freely available packagealphaBetaLabautomates the estimation of the parameters needed for the obstructed cells. In this contribution, an assessment of global regular and unstructured (triangular) wave models employing UOST is presented. The results in regular meshes show an improvement in model skill, both in terms of spectrum and of integrated parameters, thanks to the UOST modulation of the dissipation with wave direction, and to considering the cell geometry. The improvement is clear in wide areas characterized by the presence of islands, like the whole central-western Pacific Basin. In unstructured meshes, the use of UOST removes the need of high resolution in proximity of all small features, leading to (a) a simplification in the development process of large scale and global meshes, and (b) a significant decrease of the computational demand of accurate large-scale models

Problems in RMSE-based wave model validations

In order to evaluate the reliability of numerical simulations in geophysical applications it is necessary to pay attention when using the root mean square error (RMSE) and two other indicators derived from it (the normalized root mean square error (NRMSE), and the scatter index (SI)). In the present work, in fact, we show on a general basis that, in conditions of constant correlation coefficient, the RMSE index and its variants tend to be systematically smaller (hence identifying better performances of numerical models) for simulations affected by negative bias. Through a geometrical decomposition of RMSE in its components related to the average error and the scatter error it can be shown that the above mentioned behavior is triggered by a quasi-linear dependency between these components in the neighborhood of null bias. This result suggests that smaller values of RMSE, NRMSE and SI do not always identify the best performances of numerical simulations, and that these indicators are not always reliable to assess the accuracy of numerical models. In the present contribution we employ the corrected indicator proposed by Hanna and Heinold (1985) to develop a reliability analysis of wave generation and propagation in the Mediterranean Sea by means of the numerical model WAVEWATCH III®, showing that the best values of the indicator are obtained for simulations unaffected by bias. Evidences suggest that this indicator provides a more reliable information about the accuracy of the results of numerical models. © 2013 Elsevier Ltd

coastal erosion triggered by political and socio economical abrupt changes the case of lalzit bay albania

Countries that undergo abrupt changes in their political regimes, such as the transition from totalitarianism to systems based on democratic principles, experience socio-economic changes that may also have a direct impact on the trans- formation and the anthropic pressure applied to the environment. This can ranges from the scale of small communities to larger spatial scales, such as that of a catchment basin. The rise of a liberal society in countries such as the Eastern European nations, often lacks a structure capable of regulating and planning the development of the territory and the use of natural resources, which should be aimed at conciliating the new development needs with the sustainable man- agement of the environment. This paper describes and analyses the extensive coastal erosion that has taken place over the past thirty years in Lalzit Bay, Albania, which may be attributed to the great social and economic transformations that occurred in the country after the fall of Enver Hoxa's communist regime in 1991, and the consequent changes in land use. These led to a significant reduction in the volume of sediment carried by rivers, which was necessary for the morphological equilibrium of the coast and its natural replenishment

Correction to: Microplastics in seawater: sampling strategies, laboratory methodologies, and identification techniques applied to port environment

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