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Models of Ocean-Wave-Atmosphere Interaction Processes
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Models of Ocean-Wave-Atmosphere Interaction Processes

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 (31 December 2021) | Viewed by 9949

Special Issue Editor

Dr. Lichuan Wu

E-Mail Website
Guest Editor
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Interests: air-wave-sea interactions; boundary layer processes; coupled model development; extreme weathers

Special Issue Information

Dear Colleagues,

Ocean surface covers 70% of the global earth surface. In the air–sea interface, the ocean–wave–atmosphere interactions control the momentum of heat and mass transfer between the atmosphere and the ocean, which plays a critical role in the weather and climate systems on different scales. Accurate representation of ocean–wave–atmosphere interaction processes in models is centrally important for climate and weather predictions, as well as mechanism studies of extreme events. Meanwhile, ocean–wave–atmosphere interactions are important processes for us to understand the evolution of marine extreme events and climate change. Various methods, including in situ measurements, large-eddy simulations, coupled model simulations, machine learning, etc., have been used to explore the ocean–wave–atmosphere interaction processes and their impacts on weather and climate. We seek to gather a series of publications that highlight recent new findings on various aspects of air–sea turbulent fluxes, gas transfer, boundary layer processes, parametrization development, coupled model development, simulations of marine extremes and climate, and beyond.

Dr. Lichuan Wu
Guest Editor

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

  • Air–wave–sea interactions
  • Coupled model
  • Marine extreme weathers
  • Turbulent fluxes
  • Gas transfer
  • Parameterizations
  • Model simulations
  • Boundary layer processes
  • Climate

Published Papers (3 papers)

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Research

15 pages, 21578 KiB  
Article
CLTS-Net: A More Accurate and Universal Method for the Long-Term Prediction of Significant Wave Height
by Shuang Li, Peng Hao, Chengcheng Yu and Gengkun Wu
J. Mar. Sci. Eng. 2021, 9(12), 1464; https://doi.org/10.3390/jmse9121464 - 20 Dec 2021
Cited by 9 | Viewed by 2745
Abstract
Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur [...] Read more.
Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas. Full article
(This article belongs to the Special Issue Models of Ocean-Wave-Atmosphere Interaction Processes)
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16 pages, 4745 KiB  
Article
Variability of Heat and Water Fluxes in the Red Sea Using ERA5 Data (1981–2020)
by Hazem Nagy, Bayoumy Mohamed and Omneya Ibrahim
J. Mar. Sci. Eng. 2021, 9(11), 1276; https://doi.org/10.3390/jmse9111276 - 16 Nov 2021
Cited by 4 | Viewed by 2777
Abstract
The study of heat and water fluxes is one of the most essential components for understanding the interactions and exchanges between the ocean and atmosphere. Heat transfer across the air–sea interface is an important process in ocean–atmosphere dynamics. In this study, a 40-year [...] Read more.
The study of heat and water fluxes is one of the most essential components for understanding the interactions and exchanges between the ocean and atmosphere. Heat transfer across the air–sea interface is an important process in ocean–atmosphere dynamics. In this study, a 40-year (1981–2020) high-resolution (0.25° × 0.25°) ERA-5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to estimate the variability and trends of heat and water flux components in the Red Sea. The results show that the surface net heat flux is negative (loss) in the Northern Red Sea (NRS) and positive (gain) in the Southern Red Sea (SRS). The highest seasonal surface net heat flux is observed in the spring and early summer, while the lowest is reported in the winter. A significant linear trend is found in the surface net heat flux over the NRS and SRS, with values of about −0.12 ± 0.052 (W/m2)/yr and +0.20 ± 0.021 (W/m2)/yr, respectively. The annual mean surface net water flux loss to the atmosphere over the entire Red Sea is +1.46 ± 0.23 m/yr. The seasonal surface net water flux peak occurs in winter as a result of the northeast monsoon wind, which increases evaporation rate over the whole length of the Red Sea. The highest surface net water flux (+2.1 m/yr) is detected during 2020, while the lowest value (+1.3 m/yr) is observed during 1985. Full article
(This article belongs to the Special Issue Models of Ocean-Wave-Atmosphere Interaction Processes)
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16 pages, 4252 KiB  
Article
A Wind–Wave-Dependent Sea Spray Volume Flux Model Based on Field Experiments
by Xingkun Xu, Joey J. Voermans, Hongyu Ma, Changlong Guan and Alexander V. Babanin
J. Mar. Sci. Eng. 2021, 9(11), 1168; https://doi.org/10.3390/jmse9111168 - 24 Oct 2021
Cited by 18 | Viewed by 2248
Abstract
Sea spray can contribute significantly to the exchanges of heat and momentum across the air–sea interface. However, while critical, sea spray physics are typically not included in operational atmospheric and oceanic models due to large uncertainties in their parameterizations. In large part, this [...] Read more.
Sea spray can contribute significantly to the exchanges of heat and momentum across the air–sea interface. However, while critical, sea spray physics are typically not included in operational atmospheric and oceanic models due to large uncertainties in their parameterizations. In large part, this is because of the scarcity of in-situ sea spray observations which prevent rigorous validation of existing sea spray models. Moreover, while sea spray is critically produced through the fundamental interactions between wind and waves, traditionally, sea spray models are parameterized in terms of wind properties only. In this study, we present novel in-situ observations of sea spray derived from a laser altimeter through the adoption of the Beer–Lambert law. Observations of sea spray cover a broad range of wind and wave properties and are used to develop a wind–wave-dependent sea spray volume flux model. Improved performance of the model is observed when wave properties are included, in contrast to a parameterization based on wind properties alone. The novel in-situ sea spray observations and the predictive model derived here are consistent with the classic spray model in both trend and magnitude. Our model and novel observations provide opportunities to improve the prediction of air–sea fluxes in operational weather forecasting models. Full article
(This article belongs to the Special Issue Models of Ocean-Wave-Atmosphere Interaction Processes)
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