{"title":"Study on the Drill of Maritime Rescue Oriented Modeling Method of Climate Background Environment","authors":"Shan Chang, Xuyun Zhao, Bingnan Song, Yu Wang","doi":"10.1145/3316551.3316577","DOIUrl":null,"url":null,"abstract":"In order to provide an about real-world rescue environment for marine rescue simulation training, meteorological marine environment modeling becomes more and more important. In the modeling of meteorological ocean environment, the modeling method of complex environment is becoming more and more mature and the models are becoming more sophisticated, while the climate background models are often overlooked. How to provide real and accurate data-based climate background modeling for marine rescue simulation training has become an urgent problem to be solved. Based on the seasonal-trend decomposition procedure based on Loess (STL) and statistical feature modeling method, a modeling method for constructing dynamic climate background modeling using climate time series is proposed. For different climate model simulation application and the specific characteristics of climate change, our method builds the dynamic climate background model for different sea areas and different months accordingly, while considering the changing of climatic factors at different time scales. The dynamic climate background model is composed of four climate trend models with different time periods, namely, the annual variation trend of climate elements, the daily mean anomaly variation trend, the daily variation trend and the random perturbation. This modeling method of climate background model is not just a way to reflect the trends of climate change, but also to provide a dynamic and numerical climate background information for simulation training. As a result, providing this information to the commander or the rescue equipment can further improve the effectiveness of maritime rescue drill.","PeriodicalId":300199,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Digital Signal Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 3rd International Conference on Digital Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316551.3316577","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In order to provide an about real-world rescue environment for marine rescue simulation training, meteorological marine environment modeling becomes more and more important. In the modeling of meteorological ocean environment, the modeling method of complex environment is becoming more and more mature and the models are becoming more sophisticated, while the climate background models are often overlooked. How to provide real and accurate data-based climate background modeling for marine rescue simulation training has become an urgent problem to be solved. Based on the seasonal-trend decomposition procedure based on Loess (STL) and statistical feature modeling method, a modeling method for constructing dynamic climate background modeling using climate time series is proposed. For different climate model simulation application and the specific characteristics of climate change, our method builds the dynamic climate background model for different sea areas and different months accordingly, while considering the changing of climatic factors at different time scales. The dynamic climate background model is composed of four climate trend models with different time periods, namely, the annual variation trend of climate elements, the daily mean anomaly variation trend, the daily variation trend and the random perturbation. This modeling method of climate background model is not just a way to reflect the trends of climate change, but also to provide a dynamic and numerical climate background information for simulation training. As a result, providing this information to the commander or the rescue equipment can further improve the effectiveness of maritime rescue drill.