Journal of Ocean University of China

, Volume 18, Issue 6, pp 1265–1272 | Cite as

Statistical Prediction for Annual Start Date and Duration of Sea-Ice Coverage at Qinhuangdao Observation Station

  • Chunshuo Jiao
  • Wanlei Zhang
  • Sheng DongEmail author


Qinhuangdao City is located in the mid-latitude monsoon-affected region, and the timing of sea-ice coverage changes from year to year, making sea-ice forecasting difficult. In this paper, we propose a statistical model using the 1980–2013 data collected at the Qinhuangdao observation station. The start date and the duration of ice coverage are fitted with four marginal distributions, from which the best-fitted, i.e., the Weibull distribution, is selected to form a joint probability density function (PDF), built by the Gaussian copula method, for the two variables. With a given start date forecast by the Gray-Markov model (GMM), the joint PDF becomes a conditional probability model, which predicts that the duration of ice coverage is most likely 33 days at the Qinhuangdao observation station in 2014–2015. The predicted duration value is only two days less than the actual situation. The results prove that the new prediction model is feasible and effective to predict the period of ice coverage. The general sea-ice conditions that the sea ice would most likely form on December 8 and last for 80 days at the Qinhuangdao observation station could also be obtained from the joint PDF. The statistical model provides a useful tool to forecast ice conditions for planning and management of maritime activities.

Key words

start date of sea ice duration of sea ice statistical prediction Weibull distribution Gaussian copula 


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We acknowledge the support of the National Natural Science Foundation of China (No. 51779236) and the NSFC-Shandong Joint Fund Project (No. U1706226). We thank Prof. Kwok Fai Cheung for constructive discussion during his visit at the Ocean University of China under the support of 111 Project (No. B14028). We also appreciate the anonymous reviewers who provided insightful and constructive comments on this study.


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Copyright information

© Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2019

Authors and Affiliations

  1. 1.College of EngineeringOcean University of ChinaQingdaoChina
  2. 2.Qinhuangdao Marine Environmental Monitoring Central Station of SOAQinhuangdaoChina

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