A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing
Environmental 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 revolve...
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ftmdpi:oai:mdpi.com:/2077-1312/9/5/539/ 2023-08-20T04:04:06+02:00 A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing Abolfazl Shojaei Barjouei Masoud Naseri agris 2021-05-17 application/pdf https://doi.org/10.3390/jmse9050539 EN eng Multidisciplinary Digital Publishing Institute Physical Oceanography https://dx.doi.org/10.3390/jmse9050539 https://creativecommons.org/licenses/by/4.0/ Journal of Marine Science and Engineering; Volume 9; Issue 5; Pages: 539 Arctic offshore Barents Sea meteorology oceanography marine icing simulation Bayesian approach sequential importance sampling Markov chain Monte Carlo Text 2021 ftmdpi https://doi.org/10.3390/jmse9050539 2023-08-01T01:43:57Z Environmental 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. Text Arctic Arctic Ocean Barents Sea Northern Norway Svalbard MDPI Open Access Publishing Arctic Arctic Ocean Barents Sea Norway Svalbard Svalbard Archipelago Journal of Marine Science and Engineering 9 5 539 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Arctic offshore Barents Sea meteorology oceanography marine icing simulation Bayesian approach sequential importance sampling Markov chain Monte Carlo |
spellingShingle |
Arctic offshore Barents Sea meteorology oceanography marine icing simulation Bayesian approach sequential importance sampling Markov chain Monte Carlo Abolfazl Shojaei Barjouei Masoud Naseri A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
topic_facet |
Arctic offshore Barents Sea meteorology oceanography marine icing simulation Bayesian approach sequential importance sampling Markov chain Monte Carlo |
description |
Environmental 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. |
format |
Text |
author |
Abolfazl Shojaei Barjouei Masoud Naseri |
author_facet |
Abolfazl Shojaei Barjouei Masoud Naseri |
author_sort |
Abolfazl Shojaei Barjouei |
title |
A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
title_short |
A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
title_full |
A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
title_fullStr |
A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
title_full_unstemmed |
A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
title_sort |
comparative study of statistical techniques for prediction of meteorological and oceanographic conditions: an application in sea spray icing |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/jmse9050539 |
op_coverage |
agris |
geographic |
Arctic Arctic Ocean Barents Sea Norway Svalbard Svalbard Archipelago |
geographic_facet |
Arctic Arctic Ocean Barents Sea Norway Svalbard Svalbard Archipelago |
genre |
Arctic Arctic Ocean Barents Sea Northern Norway Svalbard |
genre_facet |
Arctic Arctic Ocean Barents Sea Northern Norway Svalbard |
op_source |
Journal of Marine Science and Engineering; Volume 9; Issue 5; Pages: 539 |
op_relation |
Physical Oceanography https://dx.doi.org/10.3390/jmse9050539 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/jmse9050539 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
9 |
container_issue |
5 |
container_start_page |
539 |
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1774714520206311424 |