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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 (1 October 2022) | Viewed by 25012

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Special Issue Editors

Prof. Dr. Shaoqing Zhang

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Guest Editor

Key Lab of Physical Oceanography, Ministry of Education (POL), Institute for Advanced Ocean Studies, Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China(OUC), Qingdao, China
Interests: coupled modeling; coupled model data assimilation; weather-climate predictability; parameter estimation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuxin Zhao

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The College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
Interests: intelligent systems; information fusion; ocean information engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Zuo

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Guest Editor

European Centre for Medium-Range Weather Forecasts, Reading, UK
Interests: ocean data assimilation; ocean analysis; climate reanalysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junyu Dong

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Guest Editor

Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: ocean data science; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Numerous questions are emerging in this time of data bloom: Should the development of Artificial Intelligence and Deep Learning (AIDL) be driven by data or scientific models and information estimation theory? How can AIDL benefit more from as well as advance science and technology? A science-driven AIDL is evidently a heathy track. As a matter of fact, AIDL originated from our understanding of the natural world—mathematical modeling with dynamics and physics, data assimilation—with Bayes’ Theorem guiding combinations of models and data, as well as advanced deep neural network algorithms.

In this Special Issue, we call for papers that deal with recent advances in modeling, data assimilation, and parameter estimation, as well as AIDL-associated research and development in geoscience research and applications, including advanced modeling, data assimilation, and deep neural network algorithms, data mining in the Earth system, as well as advanced topics such as data-based parameter optimization, AIDL-induced parameterization, etc. We address the concept that science-driven AIDL development can help to improve our understanding of dynamics and physics, thus furthering the advances of science and technology. Potential topics include but are not limited to:

Modeling, data assimilation, and parameter estimation;
Bayes’ theorem-based AIDL algorithms;
Data-assimilation-induced AIDL algorithms;
Model parameter estimation and AIDL;
Advanced deep neural network algorithms;
Climate downscaling and evaluation with neural networks;
AIDL-induced climate and chemistry modeling and parameterization;
Advanced AIDL algorithms induced from modeling mesoscale to sub-mesoscale physical processes;
AIDL-driven cloud and microphysics expressions;
Data-based parameter optimization applied to AIDL algorithms;
Data mining in the Earth system (e.g., optimal translation of native Earth system observations into user-specific information).

Prof. Dr. Shaoqing Zhang
Prof. Dr. Yuxin Zhao
Dr. Hao Zuo
Prof. Dr. Junyu Dong
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

Earth system modeling
Data assimilation
Artificial Intelligence
Deep learning
Ocean information engineering

Related Special Issue

Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering II in Journal of Marine Science and Engineering (4 articles)

Published Papers (12 papers)

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Research

18 pages, 5335 KiB

Open AccessArticle

Optimization of a Regional Marine Environment Mobile Observation Network Based on Deep Reinforcement Learning

by Yuxin Zhao, Yanlong Liu and Xiong Deng

J. Mar. Sci. Eng. 2023, 11(1), 208; https://doi.org/10.3390/jmse11010208 - 12 Jan 2023

Cited by 1 | Viewed by 1297

Abstract

The observation path planning of an ocean mobile observation network is an important part of the ocean mobile observation system. With the aim of developing a traditional algorithm to solve the observation path of the mobile observation network, a complex objective function needs to be constructed, and an improved deep reinforcement learning algorithm is proposed. The improved deep reinforcement learning algorithm does not need to establish the objective function. The agent samples the marine environment information by exploring and receiving feedback from the environment. Focusing on the real-time dynamic variability of the marine environment, our experiment shows that adding bidirectional recurrency to the Deep Q-network allows the Q-network to better estimate the underlying system state. Compared with the results of existing algorithms, the improved deep reinforcement learning algorithm can effectively improve the sampling efficiency of the observation platform. To improve the prediction accuracy of the marine environment numerical prediction system, we conduct sampling path experiments on a single platform, double platform, and five platforms. The experimental results show that increasing the number of observation platforms can effectively improve the prediction accuracy of the numerical prediction system, but when the number of observation platforms exceeds 2, increasing the number of observation platforms will not improve the prediction accuracy, and there is a certain degree of decline. In addition, in the multi-platform experiment, the improved deep reinforcement learning algorithm is compared with the unimproved algorithm, and the results show that the proposed algorithm is better than the existing algorithm. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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17 pages, 6125 KiB

Open AccessArticle

A Simple Bias Correction Scheme in Ocean Data Assimilation

by Changxiang Yan and Jiang Zhu

J. Mar. Sci. Eng. 2023, 11(1), 205; https://doi.org/10.3390/jmse11010205 - 12 Jan 2023

Cited by 2 | Viewed by 1610

Abstract

The mode bias is present and time-dependent due to imperfect configurations. Data assimilation is the process by which observations are used to correct the model forecast, and is affected by the bias. How to reduce the bias is an important issue. This paper investigates the roles of a simple bias correction scheme in ocean data assimilation. In this scheme, the misfits between modeled and monthly temperature and salinity with interannual variability from the Met Office Hadley Centre subsurface temperature and salinity data set (EN4.2.2) are used for the innovations in assimilation via the Ensemble Optimal Interpolation method. Two assimilation experiments are implemented to evaluate the impacts of bias correction. The first experiment is a data assimilation system without bias correction. In the second experiment, the bias correction is applied in assimilation. For comparison, the nature run with no assimilation and no bias correction is also conducted. When the bias correction is not applied, the assimilation alone leads to a rising trend in the heat and salt content that is not found in the observations. It is a spurious temporal variability due to the effect of the bias on the data assimilation. Meanwhile, the assimilation experiment without bias correction also produces significant negative impacts on the subsurface salinity. The experiment with bias correction performs best with notable improvements over the results of the other two experiments. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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20 pages, 18605 KiB

Open AccessArticle

Significant Wave Height Prediction in the South China Sea Based on the ConvLSTM Algorithm

by Lei Han, Qiyan Ji, Xiaoyan Jia, Yu Liu, Guoqing Han and Xiayan Lin

J. Mar. Sci. Eng. 2022, 10(11), 1683; https://doi.org/10.3390/jmse10111683 - 7 Nov 2022

Cited by 16 | Viewed by 2391

Abstract

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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20 pages, 2631 KiB

Open AccessArticle

A Variational Bayesian-Based Simultaneous Localization and Mapping Method for Autonomous Underwater Vehicle Navigation

by Pengcheng Mu, Xin Zhang, Ping Qin and Bo He

J. Mar. Sci. Eng. 2022, 10(10), 1563; https://doi.org/10.3390/jmse10101563 - 21 Oct 2022

Cited by 2 | Viewed by 1771

Abstract

Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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16 pages, 8437 KiB

Open AccessFeature PaperArticle

PWPNet: A Deep Learning Framework for Real-Time Prediction of Significant Wave Height Distribution in a Port

by Cui Xie, Xiudong Liu, Tenghao Man, Tianbao Xie, Junyu Dong, Xiaozhou Ma, Yang Zhao and Guohai Dong

J. Mar. Sci. Eng. 2022, 10(10), 1375; https://doi.org/10.3390/jmse10101375 - 26 Sep 2022

Cited by 1 | Viewed by 1672

Abstract

In this paper, a 2-stage cascaded deep learning framework, Port Wave Prediction Network (PWPNet), is proposed for real-time prediction of significant wave height (SWH) distribution in a port. The PWP-out model of the first stage, predicting port-entrance wave parameters, utilizes three branches, the first branch using a Long Short Term Memory (LSTM) module to learn the temporal dependencies of time sequences of port-entrance wave parameters, the second branch using Wave and Wind field Feature Extraction (WWFE) modules, composed of a residual network with spatial and channel attention, to capture spatiotemporal characteristics of outside-port 2D wave and wind field data, the third branch using multi-scale time encoding to capture the periodic characteristics of waves and wind. The PWP-in model of the second stage, estimating the in-port SWH distribution, uses port-entrance wave parameters based on a customized Artificial Neural Network (ANN) and takes PWP-out’s output as its input. A comparison of the performance of PWP-out and mainstream machine learning models including LSTM, GRU, BPNN, SVR, ELM, and RF at Hambantota Port shows that PWP-out outperforms all other models regarding medium-term (25–48 h), med–long-term (49–72 h), and long-term (73–96 h) predictions, and ablation experiments proved the effectiveness of the three branches. Furthermore, the performance comparison of our PWPNet and other 2-stage models of LSTM, GRU, BPNN, SVR, ELM, and RF cascaded with PWP-in shows that PWPNet outperforms those cascaded models for medium-term to long-term predictions of SWH distribution in a port. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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14 pages, 481 KiB

Open AccessArticle

An Improved 1D-VAR Retrieval Algorithm of Temperature Profiles from an Ocean-Based Microwave Radiometer

by Hualong Yan, Yuxin Zhao and Songbo Chen

J. Mar. Sci. Eng. 2022, 10(5), 641; https://doi.org/10.3390/jmse10050641 - 8 May 2022

Cited by 2 | Viewed by 1353

Abstract

In this study, a one-dimensional variational algorithm that combines brightness temperatures (BTs), measured by ocean-based microwave radiometers (MWR), with reanalysis data was developed to generate high accuracy temperature profiles. A forward radiative transfer model was used to simulate the BTs. For the V band (50–70 GHz), there is a good agreement between observations and simulations, but for K band (20–30 GHz), which is more affected by water vapor, large errors are observed. To reduce the errors, a combined temperature and water vapor background error covariance matrix is applied to the 1D-Var algorithm. In addition, a correction factor is added to the 1D-Var iterative equation to improve retrieval accuracy. The results of the improved 1D-Var method have been compared with the MWR built-in neural network (NN) method, original 1D-Var method, and radiosonde data, which shows that the retrievals of the combined 1D-Var method showed significant improvements between 0 to 10 km. The statistical results show that the maximum mean absolute error of the combined 1D-Var method is less than 2 K in clear sky and cloudy conditions. This paper demonstrates that the proposed combined 1D-Var method has better performance than many known retrieval methods. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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14 pages, 27159 KiB

Open AccessArticle

Improving Ocean Forecasting Using Deep Learning and Numerical Model Integration

by Youngjin Choi, Youngmin Park, Jaedong Hwang, Kijune Jeong and Euihyun Kim

J. Mar. Sci. Eng. 2022, 10(4), 450; https://doi.org/10.3390/jmse10040450 - 23 Mar 2022

Cited by 7 | Viewed by 2903

Abstract

In this paper, we propose a novel method to enhance the accuracy of a real-time ocean forecasting system. The proposed system consists of a real-time restoration system of satellite ocean temperature based on a deep generative inpainting network (GIN) and assimilation of satellite data with the initial fields of the numerical ocean model. The deep learning real-time ocean forecasting system is as fast as conventional forecasting systems, while also showing enhanced performance. Our results showed that the difference in temperature between in situ observation and actual forecasting results was improved by about 0.5 °C in daily average values in the open sea, which suggests that cutting back the temporal gaps between data assimilation and forecasting enhances the accuracy of the forecasting system in the open ocean. The proposed approach can provide more accurate forecasts with an efficient operation time. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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16 pages, 4248 KiB

Open AccessArticle

An Initial Field Intelligent Correcting Algorithm for Numerical Forecasting Based on Artificial Neural Networks under the Conditions of Limited Observations: Part I—Focusing on Ocean Temperature

by Kai Mao, Feng Gao, Shaoqing Zhang and Chang Liu

J. Mar. Sci. Eng. 2022, 10(3), 311; https://doi.org/10.3390/jmse10030311 - 23 Feb 2022

Cited by 1 | Viewed by 1746

Abstract

For the numerical forecasting of ocean temperature, the effective fusion of observations and the initial field under the conditions of limited observations has always been a significant problem. Traditional data assimilation methods cannot make full use of limited observations to correct the initial field. In order to obtain an optimal initial field with limited observations, this study proposed an intelligent correcting (IC) algorithm based on artificial neural networks (ANNs). The IC algorithm can fully mine the correlation laws between the grid points using historical data, and this process essentially replaces the estimation of background error covariance in traditional data assimilation methods. Experimental results show that the IC algorithm can lead to superior forecasting accuracy, with a lower root mean square error (around 0.7 °C) and higher coefficient of determination (0.9934) relative to the optimal interpolation method. Through the IC algorithm, the largest reduction in mean forecasting error can reach around −0.5 °C and the maximum percentage decline in mean forecasting error can reach 30% compared with the original numerical forecasting results. Therefore, the experiments validate that the IC algorithm can effectively correct the initial field under the conditions of limited observations. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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17 pages, 5429 KiB

Open AccessArticle

Variance of the Equatorial Atmospheric Circulations in the Reanalysis

by Emmanuel OlaOluwa Eresanya and Yuping Guan

J. Mar. Sci. Eng. 2021, 9(12), 1386; https://doi.org/10.3390/jmse9121386 - 6 Dec 2021

Viewed by 2855

Abstract

The structure of the equatorial atmospheric circulation, as defined by the zonal mass streamfunction (ZMS), computed using the new fifth-generation ECMWF reanalysis for the global climate and weather (ERA-5) and the National Centers for Environmental Prediction NCEP–US Department of Energy reanalysis (NCEP-2) reanalysis products, is investigated and compared with Coupled Model Intercomparison Project Phase 6 (CMIP 6) ensemble mean. The equatorial atmospheric circulations majorly involve three components: the Indian Ocean cell (IOC), the Pacific Walker cell (POC) and the Atlantic Ocean cell (AOC). The IOC, POC and AOC average monthly or seasonal cycle peaks around March, June and February, respectively. ERA-5 has a higher IOC intensity from February to August, whereas NCEP-2 has a greater IOC intensity from September to December; NCEP-2 indicates greater POC intensity from January to May, whereas ERA-5 shows higher POC intensity from June to October. For the AOC, ERA-5 specifies greater intensity from March to August and NCEP-2 has a higher intensity from September to December. The equatorial atmospheric circulations cells vary in the reanalysis products, the IOC is weak and wider (weaker and smaller) in the ERA-5 (NCEP-2), the POC is more robust and wider (feebler and teensier) in NCEP-2 (ERA-5) and the AOC is weaker and wider (stronger and smaller) in ERA-5 (NCEP-2). ERA-5 revealed a farther westward POC and AOC compared to NCEP-2. In the CMIP 6 model ensemble mean (MME), the equatorial atmospheric circulations mean state indicated generally weaker cells, with the IOC smaller and the POC greater swinging eastward and westward, respectively, while the AOC is more westward. These changes in equatorial circulation correspond to changes in dynamically related heating in the tropics. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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17 pages, 5800 KiB

Open AccessArticle

How Does El Niño Affect Predictability Barrier of Its Accompanied Positive Indian Ocean Dipole Event?

by Da Liu, Wansuo Duan and Rong Feng

J. Mar. Sci. Eng. 2021, 9(11), 1169; https://doi.org/10.3390/jmse9111169 - 24 Oct 2021

Viewed by 1514

Abstract

The effects of El Niño on the predictability of positive Indian Ocean dipole (pIOD) events are investigated by using the GFDL CM2p1 coupled model from the perspective of error growth. The results show that, under the influence of El Niño, the summer predictability barrier (SPB) for pIOD tends to intensify and the winter predictability barrier (WPB) is weakened. Since the reason for the weakening of WPB has been explained in a previous study, the present study attempts to explore why the SPB is enhanced. The results demonstrate that the initial sea temperature errors, which are most likely to induce SPB for pIOD with El Niño, possess patterns similar to those for pIOD without El Niño, whose dominant errors concentrate in the tropical Pacific Ocean (PO), with a pattern of negative SST errors occurring in the eastern and central PO and subsurface sea temperature errors being negative in the eastern PO and positive in the western PO. By tracking the development of such initial errors, it is found that the initial errors over PO lead to anomalous westerlies in the southeastern Indian Ocean (IO) through the effect of double-cell Walker circulation. Such westerly anomalies are inhibited by the strongest climatological easterly wind and the southeasterlies related to the pIOD event itself in summer, while they are enhanced by El Niño. This competing effect causes the intensified seasonal variation in latent heat flux, with much less loss in summer under the effect of El Niño. The greater suppression of the loss of latent heat flux favors the positive sea surface temperature (SST) errors developing much faster in the eastern Indian Ocean in summer, and eventually induces an enhanced SPB for pIOD due to El Niño. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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23 pages, 5089 KiB

Open AccessArticle

A New Scheme of Adaptive Covariance Inflation for Ensemble Filtering Data Assimilation

by Ang Su, Liang Zhang, Xuefeng Zhang, Shaoqing Zhang, Zhao Liu, Caili Liu and Anmin Zhang

J. Mar. Sci. Eng. 2021, 9(10), 1054; https://doi.org/10.3390/jmse9101054 - 24 Sep 2021

Viewed by 1864

Abstract

Due to the model and sampling errors of the finite ensemble, the background ensemble spread becomes small and the error covariance is underestimated during filtering for data assimilation. Because of the constraint of computational resources, it is difficult to use a large ensemble size to reduce sampling errors in high-dimensional real atmospheric and ocean models. Here, based on Bayesian theory, we explore a new spatially and temporally varying adaptive covariance inflation algorithm. To increase the statistical presentation of a finite background ensemble, the prior probability of inflation obeys the inverse chi-square distribution, and the likelihood function obeys the t distribution, which are used to obtain prior or posterior covariance inflation schemes. Different ensemble sizes are used to compare the assimilation quality with other inflation schemes within both the perfect and biased model frameworks. With two simple coupled models, we examined the performance of the new scheme. The results show that the new inflation scheme performed better than existing schemes in some cases, with more stability and fewer assimilation errors, especially when a small ensemble size was used in the biased model. Due to better computing performance and relaxed demand for computational resources, the new scheme has more potential applications in more comprehensive models for prediction initialization and reanalysis. In a word, the new inflation scheme performs well for a small ensemble size, and it may be more suitable for large-scale models. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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16 pages, 4613 KiB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Two Approaches for Correcting the Systematic Ocean Temperature Bias of CAS-ESM-C

by Mengjiao Du, Fei Zheng, Jiang Zhu, Renping Lin and Kan Yi

J. Mar. Sci. Eng. 2021, 9(9), 925; https://doi.org/10.3390/jmse9090925 - 26 Aug 2021

Cited by 2 | Viewed by 1862

Abstract

Currently, several ocean data assimilation methods have been adopted to increase the performance of air–sea coupled models, but inconsistent adjustments between the sea temperature with other oceanic fields can be introduced. In the coupled model CAS-ESM-C, inconsistent adjustments for ocean currents commonly occur in the tropical western Pacific and the eastern Indian Ocean. To overcome this problem, a new ensemble-based bias correction approach—a simple modification of the Ensemble Optimal Interpolation (EnOI) approach for multi-variable into a direct approach for a single variable—is proposed to minimize the model biases. Compared with the EnOI approach, this new approach can effectively avoid inconsistent adjustments. Meanwhile, the comparisons suggest that inconsistent adjustment mainly results from the unreasonable correlations between temperature and ocean current in the background matrix. In addition, the ocean current can be directly corrected in the EnOI approach, which can additionally generate biases for the upper ocean. These induced ocean biases can produce unreasonable ocean heat sinking and heat storage in the tropical western Pacific. It will generate incorrect ocean heat transmission toward the east, further amplifying the inconsistency introduced through the tropical air–sea interaction process. Full article

(This article belongs to the Special Issue Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering)

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