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

scholarly journals REMOTE SENSING AND DATA ASSIMILATION FOR SURF ZONE BATHYMETRIC INVERSION

2012 ◽  
Vol 1 (33) ◽  
pp. 44 ◽  
Author(s):  
Greg Wilson ◽  
Tuba Özkan-Haller ◽  
Robert Holman ◽  
Alexander Kurapov
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Field Experiment ◽  
Surf Zone ◽  
Sequential Data ◽  
Forecasting System ◽  
Using Data ◽  
Sequential Data Assimilation

We demonstrate the implementation and validation of a surf zone forecasting system, which uses remote sensing observations to control errors in surf zone bathymetry. This system uses ensemble-based sequential data assimilation techniques, which are adaptable to arbitrary geophysical observations, and/or arbitrary improvements to model physics. The system is validated using data from a 2010 field experiment at Duck, NC (U.S.A.), and is shown to produce accurate corrections to bathymetry, leading to improvements in prediction of currents.

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

Implementation of altimeter data assimilation on a regional wave forecasting system and its impact on wave and swell surge forecast in the Indian Ocean

2021 ◽  
Vol 237 ◽  
pp. 109585
Author(s):  
M. Seemanth ◽  
P.G. Remya ◽  
Suchandra Aich Bhowmick ◽  
Rashmi Sharma ◽  
T.M. Balakrishnan Nair ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Data Assimilation ◽  
Altimeter Data ◽  
Forecasting System ◽  
The Indian Ocean ◽  
Wave Forecasting

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

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.

scholarly journals A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

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
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