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dc.contributor.authorBarjouei, Abolfazl Shojaei
dc.contributor.authorNaseri, Masoud
dc.coverage.spatialBarents Seaen_US
dc.date.accessioned2023-06-12T19:56:25Z
dc.date.available2023-06-12T19:56:25Z
dc.date.issued2021
dc.identifier.citationBarjouei, A. S. and Naseri, M. (2021) A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing. Journal of Marine Science and Engineering, 9:539, 24pp. DOI: https://doi.org/10.3390/jmse9050539en_US
dc.identifier.urihttps://repository.oceanbestpractices.org/handle/11329/2273
dc.description.abstractEnvironmental conditions in Arctic waters pose challenges to various offshore industrial activities. In this regard, better prediction of meteorological and oceanographic conditions contributes to addressing the challenges by developing economic plans and adopting safe strategies. This study revolved around simulation of meteorological and oceanographic conditions. To this aim, the applications of Bayesian inference, as well as Monte Carlo simulation (MCS) methods including sequential importance sampling (SIS) and Markov Chain Monte Carlo (MCMC) were studied. Three-hourly reanalysis data from the NOrwegian ReAnalysis 10 km (NORA10) for 33 years were used to evaluate the performance of the suggested simulation approaches. The data corresponding to the first 32 years were used to predict the meteorological and oceanographic conditions, and the data corresponding to the following year were used to model verification on a daily basis. The predicted meteorological and oceanographic conditions were then considered as inputs for the newly introduced icing model, namely Marine-Icing model for the Norwegian Coast Guard (MINCOG), to estimate sea spray icing in some regions of the Arctic Ocean, particularly in the sea area between Northern Norway and Svalbard archipelago. The results indicate that the monthly average absolute deviation (AAD) from reanalysis values for the MINCOG estimations with Bayesian, SIS, and MCMC inputs is not greater than 0.13, 0.22, and 0.41 cm/h, respectively.en_US
dc.language.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherSea spray icingen_US
dc.subject.otherWave heighten_US
dc.subject.otherWind speeden_US
dc.subject.otherRelative humidityen_US
dc.subject.otherAtmospheric pressureen_US
dc.subject.otherMarine-Icing modelen_US
dc.subject.otherWave perioden_US
dc.titleA Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icingen_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerange24pp.en_US
dc.identifier.doihttps://doi.org/10.3390/jmse9050539
dc.subject.parameterDisciplineOther physical oceanographic measurementsen_US
dc.subject.parameterDisciplineWavesen_US
dc.subject.dmProcessesData analysisen_US
dc.bibliographicCitation.titleJournal of Marine Science and Engineeringen_US
dc.bibliographicCitation.volume9en_US
dc.bibliographicCitation.issue539en_US
dc.description.sdg14.aen_US
dc.description.maturitylevelPilot or Demonstrateden_US
dc.description.adoptionNovel (no adoption outside originators)en_US
dc.description.methodologyTypeMethoden_US
obps.contact.contactnameAbolfazl Shojaei Barjouei
obps.contact.contactemailshojaei.b@gmail.com
obps.resourceurl.publisherhttps://www.mdpi.com/2077-1312/9/5/539


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International