The Method for Extraction of Marine Underwater Electromagnetic Anomalous Field

2013 ◽  
Vol 475-476 ◽  
pp. 32-37
Author(s):  
Rui Yong Yue ◽  
Yue Lei Liu ◽  
Ji Tian ◽  
Jun Jun Lu ◽  
Li Jun Jiang
Keyword(s):  
Electromagnetic Field ◽  
Prediction Model ◽  
Environmental Data ◽  
Ocean Current ◽  
Filter Method ◽  
Anomalous Field ◽  
Electromagnetic Data ◽  
Anomalous Signal ◽  
Marine Disasters ◽  
Marine Environmental

Marine underwater electromagnetic field plays an important role in the prediction of the marine disasters, such as earthquake and tsunami. A detection method for the weak marine electromagnetic anomalous signatures generated by the earthquake and tsunami is presented. In order to implement the anomalous signal detection, the ocean current meter and tidal wave meter are integrated into the marine environmental underwater electromagnetic field measurement system, and the electromagnetic field data and environmental data such as sea current and wave height can be measured synchronously. The measured environmental data should be used to construct the prediction model of marine underwater electromagnetic field induced by the sea motion. Based on the prediction model, the theoretical values of electromagnetic field induced by the sea motion are estimated. This real signature induced by the sea motion can be extracted from the measured underwater electromagnetic data by use of the coherent filter method, and the remaining anomalous signature can be used to analyze the earthquake and tsunami phenomenon.

Design of Marine Data Warehouse ETL System

2014 ◽  
Vol 668-669 ◽  
pp. 1374-1377 ◽  
Author(s):  
Wei Jun Wen
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Data Warehouse ◽  
Environmental Data ◽  
Data Sources ◽  
Quality Data ◽  
Critical Step ◽  
Marine Data ◽  
Data Extracting ◽  
Marine Environmental

ETL refers to the process of data extracting, transformation and loading and is deemed as a critical step in ensuring the quality, data specification and standardization of marine environmental data. Marine data, due to their complication, field diversity and huge volume, still remain decentralized, polyphyletic and isomerous with different semantics and hence far from being able to provide effective data sources for decision making. ETL enables the construction of marine environmental data warehouse in the form of cleaning, transformation, integration, loading and periodic updating of basic marine data warehouse. The paper presents a research on rules for cleaning, transformation and integration of marine data, based on which original ETL system of marine environmental data warehouse is so designed and developed. The system further guarantees data quality and correctness in analysis and decision-making based on marine environmental data in the future.

Research on the Incremental Updating Mechanism of Marine Environmental Data Warehouse

2014 ◽  
Vol 668-669 ◽  
pp. 1378-1381 ◽  
Author(s):  
Wei Jun Wen
Keyword(s):  
Data Warehouse ◽  
Marine Environment ◽  
Historical Data ◽  
Environmental Data ◽  
Operating Efficiency ◽  
Massive Data ◽  
Complete Set ◽  
Incremental Update ◽  
Update Frequency ◽  
Marine Environmental

Marine environment data warehouse can store massive data. After the full amount of historical data has been initially loaded, the incremental update mode must be applied to ensure timely updates of data. In this paper, in view of the marine environment data warehouse’s characteristics, such as massive data amount, large number of historical data and low update frequency, a complete set of mechanisms for incremental update of marine environment data warehouse was proposed to greatly improve the operating efficiency of marine environment data warehouse.

scholarly journals Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1083-1104
Author(s):  
Young Jun Kim ◽  
Hyun-Cheol Kim ◽  
Daehyeon Han ◽  
Sanggyun Lee ◽  
Jungho Im
Keyword(s):  
Neural Networks ◽  
Sea Ice ◽  
Prediction Model ◽  
Convolutional Neural Networks ◽  
Route Planning ◽  
Arctic Sea Ice ◽  
Ocean Current ◽  
Sea Ice Concentration ◽  
Model Based ◽  
Arctic Sea

Abstract. Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error – MAE – of 2.28 %, anomaly correlation coefficient – ACC – of 0.98, root-mean-square error – RMSE – of 5.76 %, normalized RMSE – nRMSE – of 16.15 %, and NSE – Nash–Sutcliffe efficiency – of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics.

Design and Development of a Drifting Buoy for Gathering Environmental Data

2014 ◽  
Vol 651-653 ◽  
pp. 417-421
Author(s):  
Xin Jun Gan ◽  
Yong Hua Chen ◽  
Yong Ping Xu ◽  
Tao Zuo Ni ◽  
Jing Bo Jiang ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Water Mass ◽  
Environmental Data ◽  
Ocean Current ◽  
Station Network ◽  
Ground Station ◽  
Drifting Buoy ◽  
Main Components ◽  
Current Drift

Operational meteorologists and Oceanographers rely on real-time environmental data to run their numerical prediction models, even carry on the research. The ground station network is dense and the data of good quality, but there is not enough environmental data from the oceans, particularly in data-sparse areas not covered by commercial ships reporting environmental data. A drifting ocean buoy is described. The drifter consists of three main components: a surface float, a tether assembly and a dimensionally-stable drogue. It utilizes a drag structure which follows the water mass of the ocean as it flows in the form of the ocean current, and which also has an aerodynamically shaped low wind drag mast to minimize wind induced errors in ocean current drift measurements; the drag structure also being stable and resistant to heaving (pitch and roll) so as to maintain a mast carried antenna above the water even at high sea states.

scholarly journals Prediction of monthly Arctic sea ice concentration using satellite and reanalysis data based on convolutional neural networks

2019 ◽  
Author(s):  
Young Jun Kim ◽  
Hyun-Cheol Kim ◽  
Daehyeon Han ◽  
Sanggyun Lee ◽  
Jungho Im
Keyword(s):  
Neural Networks ◽  
Sea Ice ◽  
Prediction Model ◽  
Convolutional Neural Networks ◽  
Arctic Sea Ice ◽  
Ocean Current ◽  
Sea Ice Concentration ◽  
Model Based ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice, due to the global warming and its various adjoint cases. In this study, a novel one-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep learning approach, Convolutional Neural Networks (CNN). This monthly SIC prediction model based CNN is shown to perform better predictions (mean absolute error (MAE) of 2.28 %, root mean square error (RMSE) of 5.76 %, normalized RMSE (nRMSE) of 16.15 %, and NSE of 0.97) than a random forest (RF)-based model (MAE of 2.45 %, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and a simple prediction model based on the yearly trend (MAE of 9.36 %, RMSE of 21.93 %, nRMSE of 61.94 %, and NSE of 0.83) through hindcast validations. Spatiotemporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with less than 5.0 % RMSEs. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SIC. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed one-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping route planning, management of fishery industry, and long-term sea ice forecasting and dynamics.

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