| Summary: | Sea spray icing is considered as a major environmental challenge in the Arctic Ocean, which poses a critical risk not only to the vessels and industrial operations but also to human safety. Although some studies have been carried out to estimate spray icing rate (e.g., RIGICE04 and ICEMOD models), such models suffer from some unrealistic modeling assumptions and limited verification. Moreover, limited researches have been conducted on the prediction of icing rates in the long-term, as well as climatological information on spray icing for long-term risk-based decisions in the Arctic offshore industrial applications. In this study, simulation of meteorological conditions to improve prediction of sea spray icing for offshore industrial applications in the Arctic region is purposed. The applications of Bayesian inference as well as Monte Carlo methods comprised of Sequential Importance Sampling (SIS) and Markov Chain Monte Carlo (MCMC) in the prediction of meteorological and oceanographic parameters to improve the estimation of sea spray icing in the Arctic region is purposed. Reanalysis data from NOrwegian ReAnalysis 10km (NORA10) during 33 years are applied to evaluate the performance of the models. Consequently, using the 32-year data, the parameters are predicted and compared for the last one-year on a daily basis. The predicted parameters are considered as input for the newly introduced icing model namely Marine-Icing Model for the Norwegian COast Guard (MINCOG) and the results are evaluated and discussed. Apart from the prediction of sea spray icing, the applied prediction and simulation techniques can play useful roles in industrial application, especially, when new data and information are collected using which the meteorological and atmospheric conditions are predicted for future junctures. This provides the decision-maker with valuable information for planning offshore activities in the future (e.g., offshore fleet optimization). Accordingly, sea voyages with relatively lower risks can be selected based on the predicted parameters and icing rates.
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