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Prediction of Ocean Circulation and Its Variability – Expanding the Horizons

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 11015

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Dr. Sergey M. Varlamov

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Senior Scientist, Environmental Variability Prediction and Application Research Group, Application Laboratory (APL), Research Institute for Value-Added-Information Generation (VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku, Yokohama, 236-0001, Japan
Interests: ocean modeling; data assimilation; real time ocean prediction; ocean downscaled modeling; internal tides

Special Issue Information

Dear Colleagues,

Progress in ocean modeling, remote sensing, and development of new computers has made it possible to develop real-time simulation systems for the prediction of both large-scale general ocean circulation and regional mesoscale and submesoscale ocean phenomena or “ocean weather”. The reliability of such systems strongly depends on model validation, calibration of model parameters, and on the utilization of all kinds of available observations data. Assimilation of such data substantially helps to improve prediction skills.

In turn, different types of remote sensing data could require special processing to be turned to unbiased data easily utilized by ocean modelers.

These and other issues related to ocean modeling with remotely sensed data assimilation, data and modeling results processing and analysis methods, etc. could be a topics of invited papers. Development and applications of “ocean weather” prediction systems are in scope of interest as well."

We welcome all researchers working on related topics to discuss problems you face and share results you have achieved in Remote Sensing, an Open Access Journal.

Dr. Sergey M. Varlamov
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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 circulation modeling
Ocean data assimilation
Ocean weather prediction
Ocean model validation
Ocean model parameters calibration
Ocean remote sensing data processing

Published Papers (4 papers)

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Research

19 pages, 6151 KiB

Open AccessArticle

Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction

by Yanqiu Gao, Youmin Tang, Xunshu Song and Zheqi Shen

Remote Sens. 2021, 13(19), 3923; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193923 - 30 Sep 2021

Cited by 7 | Viewed by 1402

Abstract

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world. Full article

(This article belongs to the Special Issue Prediction of Ocean Circulation and Its Variability – Expanding the Horizons)

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26 pages, 8371 KiB

Open AccessArticle

A Nowcast/Forecast System for Japan’s Coasts Using Daily Assimilation of Remote Sensing and In Situ Data

by Yasumasa Miyazawa, Sergey M. Varlamov, Toru Miyama, Yukio Kurihara, Hiroshi Murakami and Misako Kachi

Remote Sens. 2021, 13(13), 2431; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132431 - 22 Jun 2021

Cited by 10 | Viewed by 3444

Abstract

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model’s efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model’s ability to represent the ocean currents near the coast accurately. Full article

(This article belongs to the Special Issue Prediction of Ocean Circulation and Its Variability – Expanding the Horizons)

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20 pages, 11430 KiB

Open AccessArticle

The Intra-Tidal Characteristics of Tidal Front and Their Spring–Neap Tidal and Seasonal Variations in Bungo Channel, Japan

by Menghong Dong and Xinyu Guo

Remote Sens. 2021, 13(9), 1840; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091840 - 09 May 2021

Viewed by 2347

Abstract

The intra-tidal variations of a tidal front in Bungo Channel, Japan and their dependence on the spring–neap tidal cycle and month were analyzed utilizing high-resolution (~2 km) hourly sea surface temperature (SST) data obtained from a Himawari-8 geostationary satellite from April 2016 to August 2020. A gradient-based front detection method was utilized to define the position and intensity of the front. Similar to previous ship-based studies, SST data were utilized to identify tidal fronts between a well-mixed strait and its surrounding stratified area. The hourly SST data confirmed the theoretical intra-tidal movement of the tidal front, which is mainly controlled by tidal current advection. Notably, the intensity of the front increases during the ebb current phase, which carries the front toward the stratified area, but decreases during the flood current phase that drives the front in the opposite direction. Due to a strong dependence on tidal currents, the intra-tidal variations appear in a fortnight cycle, and the fortnightly variations of the front are dependent on the month in which the background stratification and residual current changes occur. Additionally, tidal current convergence and divergence are posited to cause tidal front intensification and weakening. Full article

(This article belongs to the Special Issue Prediction of Ocean Circulation and Its Variability – Expanding the Horizons)

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19 pages, 4691 KiB

Open AccessArticle

Variability in the Sea Surface Temperature Gradient and Its Impacts on Chlorophyll-a Concentration in the Kuroshio Extension

by Yuntao Wang, Rui Tang, Yi Yu and Fei Ji

Remote Sens. 2021, 13(5), 888; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050888 - 26 Feb 2021

Cited by 8 | Viewed by 3042

Abstract

Sixteen years of satellite observational data in the Northwestern Pacific Ocean are used to describe the variability in the sea surface temperature (SST) gradient and its impact on chlorophyll-a concentrations (Chl-a). Spatially, a meridional dependence is identified in which the SST gradient increases to the north in association with elevated Chl-a. Temporally, the seasonal variability shows a large SST gradient and high Chl-a in winter and spring, while the SST gradient and Chl-a are much lower in summer. The seasonal variability in Chl-a leads the variability in the SST gradient by one month. A significant correlation between the SST gradient and Chl-a in the anomalous field is obtained only in the western section of the Kuroshio extension (KE) and the highest correlation is identified without any lags. An index for the section is defined as the proportion of the number of times that the SST gradient magnitude is anomalously large in each year, and the index is highly related to the stability of the KE and has a prominent influence on Chl-a in the region. An anomalously large positive (negative) SST gradient magnitude occurs when the KE is unstable (stable) and the corresponding Chl-a is high (low). Full article

(This article belongs to the Special Issue Prediction of Ocean Circulation and Its Variability – Expanding the Horizons)

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