Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.7
Journals
JMSE
Special Issues
Ocean Modeling and Data Assimilation
share announcement

Ocean Modeling and Data Assimilation

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 5121

Special Issue Editors

Prof. Dr. Changsheng Chen

E-Mail Website
Guest Editor
School for Marine Science and Technology, University of Massachusetts-Dartmouth, New Bedford, MA 02744-1221, USA
Interests: modeling and observational exploration of coastal ocean circulation; oceanic frontal processes; biological and physical interactions; arctic ocean and climate change
Prof. Dr. Chunyan Li

E-Mail Website
Guest Editor
Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: physical oceanography; transport processes; sediment transport; flushing of bays; coastal and estuarine circulations; innovative observations; modeling of coastal ocean processes; weather induced oceanographic and estuarine response and impact to the coast; storm surges; cold front induced oceanic and coastal processes; arctic estuarine dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is a call for original papers for a Special Issue on “Ocean Modeling and Data Assimilation”. The subjects cover ocean modeling, data assimilation, and associated research. This represents a wide range of original contributions involving methodological or computational algorithm development, new models with validations, model‒model comparisons, model assessments, numerical model experiments, process-oriented studies, optimal data usage in numerical modeling, forecasting, and integration among circulation, wave, and atmospheric models, just to name a few. We encourage authors to submit research papers with a clear description of goals, methodology, and major findings. We also encourage authors to share important new data and model output. All papers should explicitly state the novelty of the work. We welcome novelty and major findings and all types of scientific research with quality writing.

Prof. Dr. Changsheng Chen
Prof. Dr. Chunyan Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 4745 KiB  
Article
Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels
by Xin Bi, Wenqi Shi, Junli Xu and Xianqing Lv
J. Mar. Sci. Eng. 2024, 12(7), 1233; https://doi.org/10.3390/jmse12071233 - 22 Jul 2024
Viewed by 683
Abstract
Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In [...] Read more.
Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression Cd = (a + b × U10) × 10−3. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution. Full article
(This article belongs to the Special Issue Ocean Modeling and Data Assimilation)
►▼ Show Figures

Figure 1

24 pages, 12578 KiB  
Article
The Response of Mixed Layer Depth Due to Hurricane Katrina (2005)
by Wonhyun Lee and Jayaram Veeramony
J. Mar. Sci. Eng. 2024, 12(4), 678; https://doi.org/10.3390/jmse12040678 - 19 Apr 2024
Viewed by 1095
Abstract
The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. [...] Read more.
The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~±4 °C) and salinity (~±1.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on 29–30 August during Hurricane Katrina in the middle of the northern Gulf of Mexico, compared to an average MLD of ~20–40 m during pre-storm conditions. It took about 18 days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events. Full article
(This article belongs to the Special Issue Ocean Modeling and Data Assimilation)
►▼ Show Figures

Figure 1

12 pages, 3922 KiB  
Article
A Global-Ocean-Data Assimilation for Operational Oceanography
by Yinghao Qin, Qinglong Yu, Liying Wan, Yang Liu, Huier Mo, Yi Wang, Sujing Meng, Xiangyu Wu, Dandan Sui and Jiping Xie
J. Mar. Sci. Eng. 2023, 11(12), 2255; https://doi.org/10.3390/jmse11122255 - 29 Nov 2023
Cited by 1 | Viewed by 1427
Abstract
In this study, a global-ocean-data-assimilation system based on the three-dimensional variational (3DVAR) scheme is built for operational oceanography. The available observations include satellite altimetry; the satellite-measured sea-surface temperature (SST); and T/S profiles from Argo floats, which are assimilated to provide the initial condition [...] Read more.
In this study, a global-ocean-data-assimilation system based on the three-dimensional variational (3DVAR) scheme is built for operational oceanography. The available observations include satellite altimetry; the satellite-measured sea-surface temperature (SST); and T/S profiles from Argo floats, which are assimilated to provide the initial condition of the global-ocean forecasting. The statistical analysis methods are designed to assess the performance of the data-assimilation scheme, and the results show that the analysis SST fields agree well with OSTIA and MGDSST, and the corresponding root-mean-square errors are, respectively, 0.523 and 0.548 °C. Moreover, the analysis sea-surface-height fields are well represented at the middle and low latitudes and have a slightly greater difference in the regions with strong mesoscale eddies. The variations in the vertical distribution of the forecasting temperature profiles resemble those of the GTS buoy observation. The forecasting salinity profiles correspond well to GTS observations, except with a weaker cold bias between the depths 100 and 200 m (about 0.2 PSU) at buoy station 2901494. Overall, our 3DVAR assimilation system plays a significant role in improving the accuracy of analysis and forecasting fields for operational oceanography. Full article
(This article belongs to the Special Issue Ocean Modeling and Data Assimilation)
►▼ Show Figures

Figure 1

17 pages, 4949 KiB  
Article
Adjoint Data Assimilation of the Flow on the Southern Flank of Georges Bank: March–June 1999
by Changsheng Chen and Qichun Xu
J. Mar. Sci. Eng. 2023, 11(12), 2247; https://doi.org/10.3390/jmse11122247 - 28 Nov 2023
Viewed by 838
Abstract
An adjoint data assimilation method was incorporated into the ECOM-si coastal ocean circulation model and applied to assimilate the flow field on the southern flank of Georges Bank from March to June 1999. The model was driven by tidal forcing consisting of ten [...] Read more.
An adjoint data assimilation method was incorporated into the ECOM-si coastal ocean circulation model and applied to assimilate the flow field on the southern flank of Georges Bank from March to June 1999. The model was driven by tidal forcing consisting of ten tidal constituents at the open boundary, observed winds, and surface heat fluxes. Numerical experiments were conducted following a strategy to understand the critical issues affecting the efficiency and accuracy of the assimilated flow field. The adjoint data assimilation method significantly improved the computational accuracy of subtidal currents, especially for the along-isobath velocity. The integration window length and iteration number were two parameters affecting the assimilation convergence rate toward the observations. In such a nonlinear dynamical system, using a window length close to the M2 tidal period could make the adjoint model difficult to converge, no matter how many iterations were made. Reducing the time interval for the window length speeded up the convergence rate, but it was paid out with the sacrifice of statistical confidence. This assimilation experiment used a 6-h window length, which led to a faster convergence rate in the first ten iterations. The best-assimilated fields that satisfied the error criteria were obtained as the iteration number increased. Full article
(This article belongs to the Special Issue Ocean Modeling and Data Assimilation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop