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...
Published in: | Journal of Marine Science and Engineering |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/22794 https://doi.org/10.3390/jmse9050539 |
id |
ftunivtroemsoe:oai:munin.uit.no:10037/22794 |
---|---|
record_format |
openpolar |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/22794 2023-05-15T14:59:17+02:00 A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing Shojaei Barjouei, Abolfazl Naseri, Masoud 2021-05-17 https://hdl.handle.net/10037/22794 https://doi.org/10.3390/jmse9050539 eng eng MDPI Journal of Marine Science and Engineering Shojaei Barjouei A, Naseri N. 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. 2021;9(5):539 FRIDAID 1928808 doi:10.3390/jmse9050539 2077-1312 https://hdl.handle.net/10037/22794 openAccess Copyright 2021 The Author(s) VDP::Technology: 500::Marine technology: 580 VDP::Teknologi: 500::Marin teknologi: 580 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.3390/jmse9050539 2021-10-27T22:54:44Z 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. Article in Journal/Newspaper Arctic Arctic Ocean Northern Norway Svalbard University of Tromsø: Munin Open Research Archive Arctic Arctic Ocean Norway Svalbard Svalbard Archipelago Journal of Marine Science and Engineering 9 5 539 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500::Marine technology: 580 VDP::Teknologi: 500::Marin teknologi: 580 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 |
spellingShingle |
VDP::Technology: 500::Marine technology: 580 VDP::Teknologi: 500::Marin teknologi: 580 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 Shojaei Barjouei, Abolfazl Naseri, Masoud A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing |
topic_facet |
VDP::Technology: 500::Marine technology: 580 VDP::Teknologi: 500::Marin teknologi: 580 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 |
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 |
Article in Journal/Newspaper |
author |
Shojaei Barjouei, Abolfazl Naseri, Masoud |
author_facet |
Shojaei Barjouei, Abolfazl Naseri, Masoud |
author_sort |
Shojaei Barjouei, Abolfazl |
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 |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/22794 https://doi.org/10.3390/jmse9050539 |
geographic |
Arctic Arctic Ocean Norway Svalbard Svalbard Archipelago |
geographic_facet |
Arctic Arctic Ocean Norway Svalbard Svalbard Archipelago |
genre |
Arctic Arctic Ocean Northern Norway Svalbard |
genre_facet |
Arctic Arctic Ocean Northern Norway Svalbard |
op_relation |
Journal of Marine Science and Engineering Shojaei Barjouei A, Naseri N. 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. 2021;9(5):539 FRIDAID 1928808 doi:10.3390/jmse9050539 2077-1312 https://hdl.handle.net/10037/22794 |
op_rights |
openAccess Copyright 2021 The Author(s) |
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 |
_version_ |
1766331394733113344 |