TY - JOUR
T1 - On the selection of time-varying scenarios of wind and ocean waves
T2 - Methodologies and applications in the North Tyrrhenian Sea
AU - Cremonini, Giulia
AU - De Leo, Francesco
AU - Stocchino, Alessandro
AU - Besio, Giovanni
N1 - Funding Information:
This research was developed in the framework of the Interreg Italia-Francia Marittimo projects SPlasH! 1 1 (Stop alle Plastiche in H20!, grant number D31I18000620007), GEREMIA 2 2 (GEstione dei REflui per il MIglioramento delle Acque portuali, grant number D41I18000600005) and SINAPSI 3 3 (asSIstenza alla Navigazione per l’Accesso ai Porti in Sicurezza, grant number D64I18000160007) . Authors would like to deeply acknowledge Prof. Alessandro Verri and Prof. Carlo Ciliberto for their invaluable suggestions during the development of the research. The computational codes have been developed in Matlab® environment, and are available upon request. taking advantage of the Statistics and Machine Learning Toolbox. Note that the maximum number of iterations was bumped up to 1000 to ensure that the code was always able to converge. All the codes developed are available upon request. Please contact Giulia Cremonini at: [email protected]. The results for CE and W 2 are available for download at Genoa Research Data Management Repository, doi: https://doi.org/10.5281/zenodo.4117656
Funding Information:
This research was developed in the framework of the Interreg Italia-Francia Marittimo projects SPlasH! 1 (Stop alle Plastiche in H20!, grant number D31I18000620007), GEREMIA 2 (GEstione dei REflui per il MIglioramento delle Acque portuali, grant number D41I18000600005) and SINAPSI 3 (asSIstenza alla Navigazione per l'Accesso ai Porti in Sicurezza, grant number D64I18000160007). Authors would like to deeply acknowledge Prof. Alessandro Verri and Prof. Carlo Ciliberto for their invaluable suggestions during the development of the research. The computational codes have been developed in Matlab? environment, and are available upon request. taking advantage of the Statistics and Machine Learning Toolbox. Note that the maximum number of iterations was bumped up to 1000 to ensure that the code was always able to converge. All the codes developed are available upon request. Please contact Giulia Cremonini at: [email protected]. The results for CE and W2 are available for download at Genoa Research Data Management Repository, doi: https://doi.org/10.5281/zenodo.4117656
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/7
Y1 - 2021/7
N2 - This paper analyses a methodology to identify sub-series of waves parameters and wind speed able to explain the overall variability of the input dataset. To this end, the K-means clustering technique is applied to a 40-year long time series of hindcast data off the Genoa coastline (NW Italy). K-means aims to group the data in a reduced number of clusters, represented by as many modes of variability (or “model scenarios”). This work reviews and discusses a methodology to select time-varying model scenarios and assess the performances of K-means according to two indexes and increasing number of clusters. These indexes are used to compute the number of clusters best suited for the application at hand, testing different conditions as concerns the variables involved in the analysis and their temporal resolution. Results show that the indexes may not be consistent with each other, and that the number of scenarios to be reasonably employed strongly depends on how data are initially assembled. Finally, some of the model scenarios selected in front of Genoa are analysed and discussed in the framework of the local wind-wave climatology.
AB - This paper analyses a methodology to identify sub-series of waves parameters and wind speed able to explain the overall variability of the input dataset. To this end, the K-means clustering technique is applied to a 40-year long time series of hindcast data off the Genoa coastline (NW Italy). K-means aims to group the data in a reduced number of clusters, represented by as many modes of variability (or “model scenarios”). This work reviews and discusses a methodology to select time-varying model scenarios and assess the performances of K-means according to two indexes and increasing number of clusters. These indexes are used to compute the number of clusters best suited for the application at hand, testing different conditions as concerns the variables involved in the analysis and their temporal resolution. Results show that the indexes may not be consistent with each other, and that the number of scenarios to be reasonably employed strongly depends on how data are initially assembled. Finally, some of the model scenarios selected in front of Genoa are analysed and discussed in the framework of the local wind-wave climatology.
KW - K-means
KW - Ligurian coastline
KW - Met-ocean modelling
UR - http://www.scopus.com/inward/record.url?scp=85110304097&partnerID=8YFLogxK
U2 - 10.1016/j.ocemod.2021.101819
DO - 10.1016/j.ocemod.2021.101819
M3 - Journal article
AN - SCOPUS:85110304097
SN - 1463-5003
VL - 163
JO - Ocean Modelling
JF - Ocean Modelling
M1 - 101819
ER -