A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing

Abstract

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.