Study on the transport of COD in the sea area around Maidao off Qingdao coast using data assimilation

2007 ◽  
Vol 6 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Qiang Zhao ◽  
Xiaomin Hu ◽  
Xianqing Lü ◽  
Xuejun Xiong ◽  
Bo Yang
Keyword(s):  
Data Assimilation ◽  
Qingdao Coast ◽  
Using Data ◽  
Sea Area

scholarly journals Enhancing CFD-LES air pollution prediction accuracy using data assimilation

2019 ◽  
Vol 165 ◽  
pp. 106383 ◽  
Author(s):  
Elsa Aristodemou ◽  
Rossella Arcucci ◽  
Laetitia Mottet ◽  
Alan Robins ◽  
Christopher Pain ◽  
...  
Keyword(s):  
Air Pollution ◽  
Data Assimilation ◽  
Prediction Accuracy ◽  
Using Data ◽  
Air Pollution Prediction ◽  
Pollution Prediction

Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011

2018 ◽  
Vol 36 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoli Xia ◽  
Jinzhong Min ◽  
Feifei Shen ◽  
Yuanbing Wang ◽  
Chun Yang
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Dust Storm ◽  
Using Data

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

Development of the Forecasting System of the Current from Sacrificial Anodes of Marine Structures using Data Assimilation

2014 ◽  
Vol 63 (2) ◽  
pp. 43-49
Author(s):  
Naoki Yoneya ◽  
Yoshikazu Akira ◽  
Kenkichi Tashiro ◽  
Tomohiro Iida ◽  
Toru Yamaji ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Marine Structures ◽  
Sacrificial Anodes ◽  
Forecasting System ◽  
Using Data

Prediction of nontidal sea level variations in the Persian Gulf using data assimilation techniques

2018 ◽  
Vol 60 (3) ◽  
pp. 340-355 ◽  
Author(s):  
Naghmeh Afshar-Kaveh ◽  
Abbas Ghaheri ◽  
Vahid Chegini ◽  
Mostafa Nazarali
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Persian Gulf ◽  
Sea Level Variations ◽  
Using Data ◽  
The Persian Gulf

scholarly journals DESIGN AND IMPLEMENTATION OF INTELLIGENT SEAWATER AUTOMATIC ON-LINE MONITORING SYSTEM BASED ON BIG DATA

2018 ◽  
Vol 48 (3) ◽  
pp. 157-162
Author(s):  
L. Y. LI ◽  
J. YANG ◽  
Y. LEI ◽  
K. H. XIONG ◽  
W. H. CHEN ◽  
...  
Keyword(s):  
Monitoring System ◽  
Online Monitoring ◽  
Large Data ◽  
Test System ◽  
Data Conversion ◽  
Detection Technology ◽  
Large Data Analysis ◽  
Using Data ◽  
Integration Data ◽  
Sea Area

Based on large data analysis method and automatic detection technology, this paper designs a test system, which can realize intelligent online monitoring of seawater. Based on the theory of large data, the data preprocessing method of large data is applied by relying on the information transmitted by integrated sensors. Using data cleaning, data integration, data conversion and data reduction technology, a large number of data collected by marine monitoring devices are processed accurately. An automatic seawater monitoring system is designed on a software platform. Finally, combined with the experimental data of a certain sea area, the test results are analyzed, which proves the feasibility and effectiveness of the designed seawater online monitoring system. It has achieved the effect of seawater environmental analysis and early warning.

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