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Advances in Physical, Biological, and Coupled Ocean Models
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Advances in Physical, Biological, and Coupled Ocean Models

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 (30 September 2023) | Viewed by 3244

Special Issue Editors

Prof. Dr. Zhenya Song

E-Mail Website
Guest Editor
First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
Interests: ocean and climate simulation; high-performance computing; machine learning applications; short-term climate prediction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chan Joo Jang

E-Mail Website
Guest Editor
Ocean Circulation Research Center, Korea Institute of Ocean Science & Technology, Busan, Republic of Korea
Interests: ocean climate change; extreme climate events; regional ocean climate modeling; physical-biogeochemical coupled modeling
Dr. Haiyan Zhang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin, China
Interests: marine ecosystem; physical-biogeochemical coupled model; biogeochemical cycle of elements; physical and biogeochemical processes; hypoxia

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to contribute research articles or review articles to this Special Issue on “Advances in Physical, Biological, and Coupled Ocean Models” in the Journal of Marine Science and Engineering.

Under the double pressure of human activities and climate change, the marine ecological environments are being seriously damaged, leading to extreme marine events and natural ecological disasters. The ocean model has become the key tool for better understanding, forecasting, predicting, and projecting the marine environment and ecosystem. However, accurate simulation and forecast remain a challenge. The main goal of this Special Issue is to build synergies between fundamental and applied approaches of ocean general circulation models, and marine biological models, with special emphasis on the physical-biological coupled ocean models to bring together different experts and models, including marine, ecological, and climatological sciences. We welcome papers dealing with theoretical, modelling, applied, and observational approaches to ocean models that allow a better understanding of their advancement and applications. New technologies, such as Artificial Intelligence, for ocean models are also welcome.

Prof. Dr. Zhenya Song
Prof. Dr. Chan Joo Jang
Dr. Haiyan Zhang
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.

Keywords

  • ocean general circulation model
  • marine biological model
  • atmosphere-ocean coupled model
  • physical-biological coupled model
  • ocean-sediment-ecosystem coupled model
  • marine ecosystem
  • physical and biogeochemical processes
  • climate change
  • artificial intelligence

Published Papers (2 papers)

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Research

25 pages, 4554 KiB  
Article
Assessment of Inflation Schemes on Parameter Estimation and Their Application in ENSO Prediction in an OSSE Framework
by Yanqiu Gao
J. Mar. Sci. Eng. 2023, 11(10), 2003; https://doi.org/10.3390/jmse11102003 - 18 Oct 2023
Viewed by 1098
Abstract
The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper value and finally in parameter [...] Read more.
The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper value and finally in parameter estimation failure. To alleviate this degeneration, various covariance inflation schemes have been proposed. In this study, I examined six currently used inflation schemes: fixed inflation, conditional covariance inflation, modified estimated parameter ensemble spread, relaxation-to-prior perturbations, relaxation-to-prior spread, and new conditional covariance inflation. The six schemes were thoroughly explored using the Zebiak–Cane model and the local ensemble transform Kalman filter in the observing system simulation experiment framework. Emphasis was placed on the comparison of these schemes when it came to estimating single and multiple parameters in terms of oceanic analyses and resultant El Niño–Southern Oscillation (ENSO) predictions. The results showed that the new conditional covariance inflation scheme had the best results in terms of the estimated parameters, resultant state analyses, and ENSO predictions. In addition, the results suggested that better parameter estimation yields better state simulations, resulting in improved predictions. Overall, this study provides viable information for selecting inflation schemes for parameter estimation, offering theoretical guidance for constructing operational assimilation systems. Full article
(This article belongs to the Special Issue Advances in Physical, Biological, and Coupled Ocean Models)
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14 pages, 2414 KiB  
Article
Predicting the Tropical Sea Surface Temperature Diurnal Cycle Amplitude Using an Improved XGBoost Algorithm
by Yueling Feng, Zhen Gao, Heng Xiao, Xiaodan Yang and Zhenya Song
J. Mar. Sci. Eng. 2022, 10(11), 1686; https://doi.org/10.3390/jmse10111686 - 07 Nov 2022
Cited by 5 | Viewed by 1594
Abstract
As a critical physical parameter in the sea–air interface, sea surface temperature (SST) plays a crucial role in the sea–air interaction process. The SST diurnal cycle is one of the most critical changes that occur in the various time scales of [...] Read more.
As a critical physical parameter in the sea–air interface, sea surface temperature (SST) plays a crucial role in the sea–air interaction process. The SST diurnal cycle is one of the most critical changes that occur in the various time scales of SST. Currently, accurate simulation and prediction of SST diurnal cycle amplitude remain challenging. The application of machine learning in marine environment research, simulation, and prediction has received increasing attention. In this study, a regression prediction model for SST diurnal cycle amplitude was constructed based on TOGA/COARE buoy-observed data and an extreme gradient boosting algorithm (XGBoost). The XGBoost algorithm was also optimized using label distribution smoothing (LDS) to respond to the problem of uneven cycle amplitude size distribution. The results showed that the LDS-XGB model outperformed various empirical models and other machine learning models in terms of prediction error and prediction accuracy while effectively improving the data imbalance problem without losing model accuracy and achieving accurate and efficient predictions of the SST diurnal cycle amplitude. This work is a good demonstration of the integration of marine science and machine learning, which indicates that machine learning plays an important role in the model parametrizations and understanding the mechanisms. Full article
(This article belongs to the Special Issue Advances in Physical, Biological, and Coupled Ocean Models)
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