A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing
Abstract
:1. Introduction
2. Methods
2.1. Bayesian Inference
Gaussian DataGenerating Process
 $\mu $: mean of the datagenerating process;
 ${\sigma}_{*}^{2}$: known variance of the datagenerating process;
 $\left({\mu}_{H},{\sigma}_{H}^{2}\right)$: hyperparameters of Gaussian prior distribution;
 $\overline{x}$: sample mean;
 $\left({\mu}_{H}^{\prime},{\sigma}_{H}^{2\prime}\right)$: hyperparameters of Gaussian posterior distribution;
 $\left({\mu}_{+},{\sigma}_{+}^{2}\right)$: parameters of Gaussian predictive distribution.
2.2. Sequential Importance Sampling
2.2.1. Sequential Importance Sampling for Markov Processes
2.3. Markov Chain Monte Carlo
2.3.1. The Metropolis–Hastings Algorithm
2.3.2. Convergence Diagnostic
2.4. Proposed Models
2.4.1. Proposed Bayesian Approach
2.4.2. Proposed Sequential Importance Sampling Algorithm
Algorithm 1 Proposed sequential importance sampling (SIS) for prediction of meteorological and oceanographic conditions. 

2.4.3. Proposed Markov Chain Monte Carlo Algorithm
Algorithm 2 Proposed Markov chain Monte Carlo (MCMC) for prediction of meteorological and oceanographic conditions. 

3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
List of Acronyms.  
Acronym  Meaning 
AAD  Average Absolute Deviation 
ACO  Ant Colony Optimization 
CV  Coefficient of Variation 
DOE  Design of Experiments 
IS  Importance Sampling 
MCDM  MultiCriteria DecisionMaking 
MCMC  Markov Chain Monte Carlo 
MCMC200  Markov Chain Monte Carlo with 200 iterations 
MCMC500  Markov Chain Monte Carlo with 500 iterations 
MCS  Monte Carlo Simulation 
MINCOG  MarineIcing model for the Norwegian COast Guard 
MLE  Maximum Likelihood Estimation 
NORA10  NOrwegian ReAnalysis 10 km 
NSR  Northern Sea Route 
RAMS  Reliability, Availability, Maintainability, and Safety 
Probability Density Function  
RAMS  Reliability, Availability, Maintainability, and Safety 
SIR  Sampling Importance Resampling 
SIS  Sequential Importance Sampling 
SIS200  Sequential Importance Sampling with 200 iterations 
SIS500  Sequential Importance Sampling with 500 iterations 
SMC  Sequential Monte Carlo 
List of Symbols.  
Symbol  Meaning 
$\left(a,b\right)$  The parameters of the Weibull distribution 
$A$  A positive value, which is needed to shift the data in Weibull estimation; since the Weibull distribution does not support nonpositive values. 
$C{V}_{m}$  CV for the $m$ last drawn samples in the MCMC algorithm iterations 
$C{V}^{T}$  A threshold for CV 
$D$  The number of days in a year, which adopts the values 365 and 366 for normal and leap years, respectively. 
$D{M}^{t,y}\left(\theta \right)$  The daily mean of the parameter $\theta $ at time $t$ in year $y$. Here, ‘time’ is referring to ‘day’. 
$D{V}^{t,y}\left(\theta \right)$  Deviation of $D{M}^{t,y}\left(\theta \right)$ from its value at time ‘$t1$’ in year $y$. Here, ‘time’ is referring to ‘day’. 
$E\left(X\right)$  The expected value of $X$ 
${E}_{{f}^{t}}\left[h\left({X}^{1:t}\right)\right]$  The expected value of a quantity of interest, $h\left({X}^{1:t}\right)$, with respect to ${f}^{t}$ 
$f$  A target density 
$f\left(\theta \right)$  Prior distribution of the parameter $\theta $ 
$f\left(\theta x\right)$  Posterior distribution of the parameter $\theta $ given the data $x$ 
$f\left(x\theta \right)$  The likelihood function of the data in hand, given the parameter $\theta $ 
${f}^{t}$  Target density of a discretetime sequential random variable at time $t$ 
$g$  Proposal density or envelope for $f$ 
${g}^{t}$  Proposal density or envelope for ${f}^{t}$ 
$h$  An arbitrary function 
$H$  Indices for hyperparameters 
H0  The null hypothesis in the AndersonDarling test of hypothesis 
H1  The alternative hypothesis in the AndersonDarling test of hypothesis 
$i$  Subscript index for samples; $i=1,2,\dots ,n$ 
$I{S}_{j}\left(\theta \right)$  The weighted average of all drawn samples until iteration $j$ for $\theta $, using IS weights 
$j$  Subscript index as iteration counter of algorithms; $j=1,2,\dots ,M$ 
${k}_{\left(z\right)}$  Center of the ${z}^{th}$ bin in the kernel density estimation 
$M$  Number of iterations of an algorithm 
$MCM{C}^{t}\left(\theta \right)$  MCMC estimation for $\theta $ at time $t$ 
$n$  Sample size 
p  The parameter of Binomial distribution 
${s}^{t,y}\left(\theta \right)$  Possible values (i.e. state space) for the parameter $\theta $ in the SIS algorithm at time $t$ in year $y$. Here, ‘time’ is referring to ‘day’. The values are based on the historical deviations from the daily mean of the parameter in the previous day. 
$S\left(X\right)$  Sample standard deviation of $X$ 
${S}^{t}\left(\theta \right)$  Set of ${s}^{t,y}\left(\theta \right)$ for all years; ${S}^{t}\left(\theta \right)=\left\{{s}^{t,1}\left(\theta \right),\dots ,{s}^{t,Y}\left(\theta \right)\right\}$ 
$SI{S}^{t}\left(\theta \right)$  SIS estimation for $\theta $ at time $t$ 
$t$  Superscript index for the time in a discretetime sequential process. Without loss of generality, ‘time’ is referring to ‘day’ in this study. 
${u}^{t}$  IS weight for $\left({x}^{t}{x}^{t1}\right)$ in a Markov process 
${u}_{j}^{t}$  IS weight for $\left({x}^{t}{x}^{t1}\right)$ in a Markov process for a drawn sample in iteration $j$ 
${w}_{j}$  IS weight for a drawn sample in iteration $j$ 
${w}^{t}$  IS weight for ${x}^{1:t}$ 
${w}_{j}^{t}$  IS weight for ${x}^{1:t}$ in iteration $j$ 
${W}^{t}$  Set of ${w}_{j}^{t}$ from iterations of SIS algorithm; ${W}^{t}=\left\{{w}_{1}^{t},\dots ,{w}_{M}^{t}\right\}$ 
$x$  The available data on the dataset 
${x}_{i}$  The ith sample of $x$ 
${x}^{+}$  Unobserved data of the random variable $X$ in the future 
${x}^{t}$  A sample for ${X}^{t}$ 
$\overline{x}$  Sample mean 
$X$  A random variable 
${X}^{t}$  A discretetime sequential random variable at time $t$ 
${X}^{1:t}=\left({X}^{1},\dots ,{X}^{t}\right)$  A discretetime stochastic process representing the entire history of the sequence of a random variable 
${x}^{1:t}$  A sample for ${X}^{1:t}$ 
${x}_{i}^{1:t}$  The ith sample for ${X}^{1:t}$ 
$y$  Superscript index for years; $y=1,\dots ,Y$ 
$Y$  Number of years from the dataset that are used for estimation 
$z$  Subscript index for bins in the kernel density estimation 
$\alpha $  Acceptance probability in the MetropolisHastings algorithm 
$\theta $  Generic parameter that is supposed to be estimated 
${\theta}^{\prime}$  A drawn sample for parameter $\theta $, which might be accepted or rejected 
${\theta}_{j}$  An accepted sample for parameter $\theta $ in iteration $j$ 
$\lambda $  The parameter of Poisson distribution 
$\mu $  Mean of the datagenerating process 
$\left({\mu}_{H},{\sigma}_{H}^{2}\right)$  Hyperparameters of Gaussian prior distribution 
$\left({\mu}_{H}^{\prime},{\sigma}_{H}^{2\prime}\right)$  Hyperparameters of Gaussian posterior distribution 
$\left({\mu}_{+},{\sigma}_{+}^{2}\right)$  Parameters of Gaussian predictive distribution 
$\left({\mu}_{re},{\sigma}_{re}^{2}\right)$  Parameters of reanalysis values in 2012 
${\sigma}_{*}^{2}$  The known variance of the datagenerating process 
References
 Sevastyanov, D.V. Arctic Tourism in the Barents Sea region: Current Situation and Boundaries of the Possible. Arct. North 2020, 39, 26–36. [Google Scholar] [CrossRef]
 Ryerson, C.C. Ice protection of offshore platforms. Cold Reg. Sci. Technol. 2011, 65, 97–110. [Google Scholar] [CrossRef]
 DehghaniSanij, A.R.; Dehghani, S.R.; Naterer, G.F.; Muzychka, Y.S. Sea spray icing phenomena on marine vessels and offshore structures: Review and formulation. Ocean Eng. 2017, 132, 25–39. [Google Scholar] [CrossRef]
 Heinrich, H. Industrial Accident Prevention: A Scientific Approach, 3rd ed.; McGraw Hill: New York, NY, USA, 1950. [Google Scholar]
 Chatterton, M.; Cook, J.C. The Effects of Icing on Commercial Fishing Vessels; Worcester Polytechnic Institute: Worcester, UK, 2008. [Google Scholar]
 Barabadi, A.; Garmabaki, A.; Zaki, R. Designing for performability: An icing risk index for Arctic offshore. Cold Reg. Sci. Technol. 2016, 124, 77–86. [Google Scholar] [CrossRef]
 Rashid, T.; Khawaja, H.A.; Edvardsen, K. Review of marine icing and anti/deicing systems. J. Mar. Eng. Technol. 2016, 15, 79–87. [Google Scholar] [CrossRef]
 Samuelsen, E.M.; Edvardsen, K.; Graversen, R.G. Modelled and observed seaspray icing in ArcticNorwegian waters. Cold Reg. Sci. Technol. 2017, 134, 54–81. [Google Scholar] [CrossRef] [Green Version]
 Mertins, H.O. Icing on fishing vessels due to spray. Mar. Obs. 1968, 38, 128–130. [Google Scholar]
 Stallabrass, J.R. Trawler Icing: A Compilation of Work Done at N.R.C.; Mechanical Engineering Report MD56; National Research Conseil: Ottawa, ON, Canada, 1980. [Google Scholar]
 Sultana, K.R.; Dehghani, S.R.; Pope, K.; Muzychka, Y.S. A review of numerical modelling techniques for marine icing applications. Cold Reg. Sci. Technol. 2018, 145, 40–51. [Google Scholar] [CrossRef]
 Kulyakhtin, A.; Tsarau, A. A timedependent model of marine icing with application of computational fluid dynamics. Cold Reg. Sci. Technol. 2014, 104–105, 33–44. [Google Scholar] [CrossRef] [Green Version]
 Horjen, I. Numerical Modelling of TimeDependent Marine Icing, AntiIcing and DeIcing; Norges Tekniske Høgskole (NTH): Trondheim, Norway, 1960. [Google Scholar]
 Horjen, I. Numerical modeling of twodimensional sea spray icing on vesselmounted cylinders. Cold Reg. Sci. Technol. 2013, 93, 20–35. [Google Scholar] [CrossRef]
 Forest, T.; Lozowski, E.; Gagnon, R.E. Estimating Marine Icing on Offshore Structures Using RIGICE04. In Proceedings of the 11th International Workshop on Atmospheric Icing of Structures, Montreal, PQ, Canada, 12–16 June 2005; National Research Council Canada: Montreal, QC, Canada, 2005; pp. 12–16. [Google Scholar]
 Samuelsen, E.M. Shipicing prediction methods applied in operational weather forecasting. Q. J. R. Meteorol. Soc. 2017, 144, 13–33. [Google Scholar] [CrossRef]
 Reistad, M.; Breivik, Ø.; Haakenstad, H.; Aarnes, O.J.; Furevik, B.; Bidlot, J.R. A highresolution hindcast of wind and waves for the North Sea, the Norwegian Sea, and the Barents Sea. J. Geophys. Res. Oceans 2011, 116, C05019. [Google Scholar] [CrossRef] [Green Version]
 Little, R.J. Calibrated Bayes: A Bayes/Frequentist Roadmap. Am. Stat. 2006, 60, 213–223. [Google Scholar] [CrossRef]
 Wilks, D. Statistical Methods in the Atmospheric Sciences, 3rd ed.; Academic Press: Cambridge, MA, USA, 2011; Volume 3. [Google Scholar]
 Park, M.H.; Ju, M.; Kim, J.Y. Bayesian approach in estimating flood waste generation: A case study in South Korea. J. Environ. Manag. 2020, 265, 110552. [Google Scholar] [CrossRef]
 Robert, C.; Casella, G. A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data. Stat. Sci. 2012, 26, 102–115. [Google Scholar] [CrossRef]
 Zio, E. The Monte Carlo Simulation Method for System Reliability and Risk Analysis; Springer: London, UK, 2013. [Google Scholar] [CrossRef]
 Epstein, E.S. Statistical Inference and Prediction in Climatology: A Bayesian Approach; Meteorological Monographs, American Meteorological Society: Boston, MA, USA, 1985; Volume 20. [Google Scholar]
 Lee, P.M. Bayesian Statistics, an Introduction, 2nd ed.; Wiley: New York, NY, USA, 1997. [Google Scholar]
 Barjouei, A.S.; Naseri, M.; Ræder, T.B.; Samuelsen, E.M. Simulation of Atmospheric and Oceanographic Parameters for Spray Icing Prediction. In Proceedings of the 30th Conference Anniversary of the International Society of Offshore and Polar Engineers, Shanghai, China, 11–16 October 2020; ISOPE: Mountain View, CA, USA, 2020; ISOPEI201256; pp. 750–1256. Available online: https://onepetro.org/ISOPEIOPEC/proceedingsabstract/ISOPE20/AllISOPE20/ISOPEI201256/446378 (accessed on 20 April 2021).
 Ridgeway, G.; Madigan, D. A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets. Data Min. Knowl. Discov. 2003, 7, 301–319. [Google Scholar] [CrossRef] [PubMed]
 Givens, G.H.; Hoeting, J.A. Computational Statistics, 2nd ed.; John Wiley & Sons Inc.: Hoboken, NJ, USA, 2013. [Google Scholar] [CrossRef]
 Rubin, D.B. Comment. J. Am. Stat. 1987, 82, 543–546. [Google Scholar] [CrossRef]
 Rubin, D.B. Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3; Bernardo, J.M., DeGroot, M.H., Lindley, D.V., Smith, A.F., Eds.; John Wiley & Sons Inc.: Oxford, UK, 1988; pp. 395–402. [Google Scholar]
 Liu, J.S.; Chen, R. Sequential Monte Carlo Methods for Dynamic Systems. J. Am. Stat. Assoc. 1988, 93, 1032–1044. [Google Scholar] [CrossRef]
 Barbu, A.; Zhu, S.C. Sequential Monte Carlo. In Monte Carlo Methods; Springer: Singapore, 2020; pp. 19–48. [Google Scholar] [CrossRef]
 Brooks, S.P.; Roberts, G.O. Convergence assessment techniques for Markov chain Monte Carlo. Stat. Comput. 1998, 8, 319–335. [Google Scholar] [CrossRef]
 Tierney, L. Markov Chains for Exploring Posterior Distributions. Ann. Stat. 1994, 22, 1701–1762. [Google Scholar] [CrossRef]
 Cowles, M.K.; Carlin, B.P. Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. J. Am. Stat. Assoc. 1996, 91, 883–904. [Google Scholar] [CrossRef]
 Norwegian Petroleum Directorate. Available online: https://factpages.npd.no/en (accessed on 4 May 2020).
 Anderson, T.W.; Darling, D.A. Asymptotic theory of certain ‘goodnessoffit’ criteria based on stochastic processes. Ann. Math. Stat. 1952, 23, 193–212. [Google Scholar] [CrossRef]
 MATLAB. MATLAB and Simulink; 9.8.0.1396136 (R2020a); The MathWorks Inc.: Natick, MA, USA, 2020; Available online: https://es.mathworks.com/products/matlab/ (accessed on 14 April 2020).
 Smith, G. Essential Statistics, Regression, and Econometrics, 2nd ed.; Academic Press: Cambridge, MA, USA, 2015. [Google Scholar] [CrossRef]
 Naseri, M.; Samuelsen, E.M. Unprecedented VesselIcing Climatology Based on SprayIcing Modelling and Reanalysis Data: A RiskBased DecisionMaking Input for Arctic Offshore Industries. Atmosphere 2019, 10, 197. [Google Scholar] [CrossRef] [Green Version]
 Wang, S.; Mu, Y.; Zhang, X.; Jia, X. Polar tourism and environment change: Opportunity, impact and adaptation. Polar Sci. 2020, 25, 100544. [Google Scholar] [CrossRef]
Parameter  Number of Days in Year Which H0 Cannot Be Rejected  Percentage of Days in Year Which H0 Cannot Be Rejected 

Wave height  245  67% 
Wind speed  330  90% 
Temperature  257  70% 
Relative humidity  284  78% 
Atmospheric pressure  346  95% 
Wave period  180  49% 
Parameter  Value 

${\sigma}_{*}^{2}$  1.12 
$\left({\mu}_{H},{\sigma}_{H}^{2}\right)$  (−3.49, 10.29) 
$\overline{x}$  (−5.20, 8.52) 
$\left({\mu}_{H}^{\prime},{\sigma}_{H}^{2\prime}\right)$  (−5.16, 0.25) 
$\left({\mu}_{+},{\sigma}_{+}^{2}\right)$  (−5.16, 1.50) 
$\left({\mu}_{re},{\sigma}_{re}^{2}\right)$  (−4.44, 0.95) 
Month  Bayesian  SIS200 ^{2}  SIS500 ^{3}  MCMC200 ^{4}  MCMC500 ^{5} 

Jan  1.00  1.03  0.94  0.99  0.97 
Feb  0.97  1.19  1.25  1.00  0.97 
Mar  0.89  0.96  1.12  0.97  0.84 
Apr  0.65  0.74  0.63  0.67  0.70 
May  0.98  1.10  1.04  0.95  0.95 
Jun  0.54  0.62  0.52  0.66  0.53 
Jul  0.42  0.38  0.46  0.47  0.52 
Aug  0.54  0.56  0.59  0.56  0.74 
Sep  0.82  0.83  1.02  0.89  1.07 
Oct  0.99  0.94  1.17  1.15  1.22 
Nov  0.66  0.84  0.75  0.78  0.83 
Dec  1.03  1.48  1.37  1.15  1.09 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  3.39  3.37  3.31  3.76  3.52 
Feb  2.39  2.23  2.31  2.29  2.38 
Mar  2.69  3.14  2.92  2.77  2.89 
Apr  1.99  2.07  2.65  1.92  2.18 
May  2.77  2.50  2.84  2.60  2.54 
Jun  2.33  2.49  2.49  2.22  2.14 
Jul  1.56  1.86  1.90  1.65  1.53 
Aug  2.48  2.51  2.60  2.35  2.62 
Sep  2.90  3.62  3.23  3.01  3.00 
Oct  2.85  3.18  4.10  3.22  3.07 
Nov  2.63  3.13  3.48  2.43  2.59 
Dec  2.83  3.61  3.64  2.94  2.91 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  3.13  4.36  4.29  5.95  5.99 
Feb  4.25  4.88  5.16  7.25  6.94 
Mar  2.76  3.19  3.16  4.49  5.57 
Apr  1.83  2.58  2.26  2.40  2.18 
May  1.68  1.85  2.17  1.64  1.98 
Jun  0.63  0.64  0.76  0.67  0.57 
Jul  0.75  0.84  0.71  0.80  0.76 
Aug  0.85  0.83  0.97  0.89  0.78 
Sep  1.33  1.83  1.25  1.43  1.42 
Oct  1.41  2.44  2.46  2.07  2.12 
Nov  2.34  2.75  3.02  3.38  3.04 
Dec  2.12  3.31  3.44  3.55  3.49 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  5.31  5.59  5.20  5.82  6.02 
Feb  9.54  9.34  9.33  8.21  8.25 
Mar  5.94  7.53  7.49  6.28  5.16 
Apr  9.72  10.39  9.59  9.94  9.65 
May  8.22  8.82  8.57  8.60  8.54 
Jun  5.54  5.41  5.25  4.96  4.88 
Jul  6.48  5.98  7.34  6.36  6.36 
Aug  6.55  6.14  7.52  5.95  5.99 
Sep  8.81  10.47  9.22  9.63  9.75 
Oct  6.46  8.41  9.76  6.19  6.21 
Nov  9.98  13.56  11.86  11.59  11.29 
Dec  6.97  9.38  8.16  8.97  7.67 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  14.20  14.09  15.64  13.07  14.57 
Feb  17.13  15.96  16.92  16.46  16.86 
Mar  10.58  12.53  12.60  11.55  12.91 
Apr  9.35  12.01  9.41  8.40  10.38 
May  9.80  11.05  9.92  10.46  9.95 
Jun  4.40  5.05  4.20  4.84  4.88 
Jul  7.07  8.74  7.66  7.45  7.76 
Aug  7.62  8.29  6.44  7.46  6.84 
Sep  8.79  9.35  10.88  10.40  9.69 
Oct  8.46  8.41  12.09  8.68  7.70 
Nov  12.85  12.20  14.82  12.36  12.79 
Dec  16.90  17.16  18.81  17.05  16.12 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  1.06  1.14  1.08  2.00  1.82 
Feb  1.14  1.59  1.38  2.33  3.38 
Mar  0.99  1.40  1.20  1.52  2.16 
Apr  0.78  0.81  0.80  1.34  1.61 
May  1.03  1.36  1.29  1.47  1.56 
Jun  0.63  1.03  0.77  0.70  0.67 
Jul  0.70  0.97  0.79  0.84  0.71 
Aug  0.60  0.62  0.70  0.71  0.96 
Sep  0.64  0.63  0.88  0.89  0.61 
Oct  0.98  1.22  1.06  1.13  1.01 
Nov  0.63  0.98  1.13  0.83  0.73 
Dec  0.89  1.01  1.25  1.28  1.47 
Month  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Jan  0.08  0.19  0.20  0.34  0.35 
Feb  0.13  0.19  0.22  0.41  0.39 
Mar  0.08  0.12  0.12  0.22  0.29 
Apr  0.07  0.12  0.10  0.10  0.09 
May  0.00  0.00  0.02  0.01  0.02 
Jun  0.00  0.00  0.00  0.00  0.00 
Jul  0.00  0.00  0.00  0.00  0.00 
Aug  0.00  0.00  0.00  0.00  0.00 
Sep  0.00  0.00  0.00  0.00  0.00 
Oct  0.00  0.01  0.01  0.00  0.00 
Nov  0.03  0.04  0.04  0.07  0.04 
Dec  0.06  0.11  0.16  0.14  0.14 
Location  Bayesian  SIS200  SIS500  MCMC200  MCMC500 

Coordinates (74.07° N, 35.81° E)  00:00:01  00:00:05  00:00:22  00:00:12  00:00:19 
Entire area  00:04:41  00:44:29  02:00:03  01:28:28  02:22:14 
ttest Parameter  30 Years  32 Years 

Mean  2.68  2.61 
Variance  4.32  3.80 
Observations  365  365 
df  725   
t Stat  0.45   
pvalue  0.65   
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Shojaei Barjouei, A.; Naseri, M. A Comparative Study of Statistical Techniques for Prediction of Meteorological and Oceanographic Conditions: An Application in Sea Spray Icing. J. Mar. Sci. Eng. 2021, 9, 539. https://doi.org/10.3390/jmse9050539
Shojaei Barjouei A, Naseri M. 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. https://doi.org/10.3390/jmse9050539
Chicago/Turabian StyleShojaei Barjouei, Abolfazl, and Masoud Naseri. 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, no. 5: 539. https://doi.org/10.3390/jmse9050539