Long-term variability of the coastal ocean stratification in the Gulf of Naples: Two decades of monitoring the marine ecosystem at the LTER-MC site, between land and open Mediterranean sea

Kokoszka, Florian; Roux, Baptiste; Iudicone, Daniele; Conversano, Fabio; d’Alcalà, Maurizio Ribera

doi:10.1002/essoar.10511253.1

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Time series refers to arranging a series of data in chronological order. Time series prediction analysis is commonly used to study the trend of events over time, including periodic, seasonal, and random changes [13]. With the advent of the big data era, users constantly engage in interactive behavior with the outside world, generating and accumulating a large amount of event data.…”

Section: B Public Opinion and Time Series Analysismentioning

confidence: 99%

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

Mingying Shi, Mengru Wang, Yongcong Luo

2024

jes

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This article is based on the massive text data in the digital era, using text mining methods to explore the evolution mechanism of online public opinion, in order to achieve the control of public opinion trends in sudden network events and regulate the stability of online and offline public opinion. We introduced time series for decision-making assistance, combined with time series autoregressive integrated moving average model (ARIMA) for prediction, and comprehensively evaluates the evolution of public opinion from two dimensions: sentimental orientation and social network analysis (SNA). Based on this, a new exploration model for public opinion evolution, SNA-ARIMA, is constructed. The research results indicate that the process of public opinion dissemination exhibits distinct phased characteristics, and each stage is comprehensively influenced by different key nodes or factors. This study provides decision support for managing public opinion crises, making the identification of key nodes in online public opinion events, the evolution mechanism of public opinion, and the guidance of public opinion crises more systematic, forward-looking, and scientific.

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Section: B Public Opinion and Time Series Analysismentioning

confidence: 99%

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

Mingying Shi, Mengru Wang, Yongcong Luo

2024

jes

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Gulf of Naples Advanced Model (GNAM): A Multiannual Comparison with Coastal HF Radar Data and Hydrological Measurements in a Coastal Tyrrhenian Basin

Kokoszka

Saviano

Botte

et al. 2022

JMSE

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High-resolution modelling systems have increasingly become an essential requirement to investigate ocean dynamics over a wide range of spatial and temporal scales, and to integrate the punctual ocean observations. When applied in coastal areas, they also have the potential to provide a detailed representation of transport and exchange processes at the sub-basin scale. This paper presents a validation exercise between the surface fields generated by the regional ocean modeling system (ROMS), developed for the Tyrrhenian Sea and downscaled for the Gulf of Naples (GNAM Gulf of Naples advanced model), and a 4 year-long (2009–2012) record of high-frequency radar (HFR) data. The comparison between hourly and seasonal model results and HFR surface fields is focused on the Gulf of Naples (GoN), where an observational network of three HFR sites has been operational since 2004, and on a specific subdomain characterized by the presence of the Sarno river, a long-term ecological research station (LTER-MC) and one important canyon area. An evaluation on a transect delimiting inshore–offshore zones in the GoN is also presented. The GNAM model was also compared with in situ hydrological parameters of temperatures and salinities retrieved at the LTER-MC fixed monitoring station. According to the skill metrics, basic circulation features are accurately reproduced by the circulation model, despite some model drawbacks in terms of increment of energy content in the surface current field occurring during specific seasonal events. The results allow us to identify potential model errors and to suggest useful improvements, the outcome also confirms the unique capability of HF radar systems to provide fine-scale measurements for the validation of numerical models and to counterbalance the lack of high-resolution measurements in coastal areas.

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A Turbulence Survey in the Gulf of Naples, Mediterranean Sea, during the Seasonal Destratification

Kokoszka

Conversano

Iudicone

et al. 2023

JMSE

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The seasonality of the vertical mixing at coastal sites is not well characterized yet. Here, a time series of the dissipation rate of turbulent kinetic energy (ε) was obtained from weekly morning microstructure observations covering the destratification period (July 2015, February 2016) at a coastal site in the western Mediterranean Sea, influenced by freshwater runoffs. Estimated with bulk parameters from the public re-analyzed dataset ERA5, the Ekman layer, and the convective penetration depth scale with the mixed layer depth (MLD) with a good agreement. Below the MLD, peaks of ε are observed in the baroclinic layers that progressively overlap with the bottom layer, where repeated near-bottom turbidity peaks provide evidence of sediment resuspension, suggesting energetic processes within the bottom boundary layer. In the subsurface, moderate values ( to) are observed, following a Burr type XII distribution. Significant correlation with ε at MLD is obtained with a model combining the effects of wind, wind–wave, and convection, highlighting a calm sea bias in our data, plus a sunrise bias when morning buoyancy fluxes are stabilizing. Another correlation, obtained from a pure-wind estimation 18 h before, suggests the role of wind in generating internal waves in the stratified layers, thus, impacting mixing intensity.

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Long-term variability of the coastal ocean stratification in the Gulf of Naples: Two decades of monitoring the marine ecosystem at the LTER-MC site, between land and open Mediterranean sea

Cited by 3 publications

References 43 publications

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

Gulf of Naples Advanced Model (GNAM): A Multiannual Comparison with Coastal HF Radar Data and Hydrological Measurements in a Coastal Tyrrhenian Basin

A Turbulence Survey in the Gulf of Naples, Mediterranean Sea, during the Seasonal Destratification

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