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Remote Sensing and Parameterization of Air-Sea Interaction

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 10196

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Prof. Dr. Zhiqiu Gao

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Guest Editor

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Interests: surface processes: modeling and observation; near surface layer parameterization for models; air-sea or land-sea interaction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuanjian Yang

E-Mail Website
Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: multispectral remote sensing; cloud precipitation; land surface temperature; Doppler lidar; urban boundary layer; data fusion; artificial intelligence and weather prediction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kai Qin

E-Mail Website
Guest Editor

School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: aerosol; trace gases; remote sensing; lidar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to invite you to submit a paper to a Special Issue of the journal Remote Sensing entitled “Remote Sensing and Parameterization of Air-Sea Interaction”. The aim of this Special Issue is to publish original research manuscripts focused on the application of remote sensing science and technology in estimating the air-sea interactions on various scales. We aim to publish papers related to (1) novel/improved methods and/or retrieval algorithms of satellite remote sensing and radar detection, and (2) satellite/radar data assimilation in weather forecasting and ocean current prediction, to benefit the community, open to everyone in need of them. We encourage submissions from researchers all around the world.

The scope of this Special Issue includes, but is not limited to, the following:

Sea surface wind and wave status remote sensing;
Lidar retrieval algorithms of atmospheric boundary layer height, sea surface wind, and waves;
Satellite and lidar data fusion and assimilation for coastal weather forecast and ocean current prediction;
Validation of global database of air-sea interaction forecasting;
Remote sensing, lidar detection, and numerical simulation of air-sea interactions under hurricane/typhoon environments.

Prof. Dr. Zhiqiu Gao
Prof. Dr. Yuanjian Yang
Prof. Dr. Kai Qin
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 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

air-sea interaction
data fusion/assimilation
sea surface wind and wave
coastal weather forecast
ocean current prediction

Published Papers (9 papers)

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11 pages, 8119 KiB

Open AccessCommunication

A Transfer Learning-Enhanced Generative Adversarial Network for Downscaling Sea Surface Height through Heterogeneous Data Fusion

by Qi Zhang, Wenjin Sun, Huaihai Guo, Changming Dong and Hong Zheng

Remote Sens. 2024, 16(5), 763; https://doi.org/10.3390/rs16050763 - 22 Feb 2024

Viewed by 493

Abstract

In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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16 pages, 25877 KiB

Open AccessArticle

Improving Radar Reflectivity Reconstruction with Himawari-9 and UNet++ for Off-Shore Weather Monitoring

by Bingcheng Wan and Chloe Yuchao Gao

Remote Sens. 2024, 16(1), 56; https://doi.org/10.3390/rs16010056 - 22 Dec 2023

Viewed by 657

Abstract

Weather radars play a crucial role in the monitoring of severe convective weather. However, due to their limited detection range, they cannot conduct an effective monitoring in remote offshore areas. Therefore, this paper utilized UNet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibited significant diurnal cycles, but there were notable differences in their variation characteristics. To address this, we established four comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results showed that adding the distribution map of the minimum brightness temperature at the corresponding time in the model could effectively improve the model’s performance in both spatial and temporal dimensions, reduce the root-mean-square error (RMSE) of the model, and enhance the accuracy of severe convective weather monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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21 pages, 8238 KiB

Open AccessArticle

Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations

by Zheng Li, Bingcheng Wan, Zexia Duan, Yuanhong He, Yingxin Yu and Huansang Chen

Remote Sens. 2023, 15(17), 4172; https://doi.org/10.3390/rs15174172 - 25 Aug 2023

Cited by 1 | Viewed by 1046

Abstract

This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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23 pages, 7078 KiB

Open AccessArticle

SeaMAE: Masked Pre-Training with Meteorological Satellite Imagery for Sea Fog Detection

by Haotian Yan, Sundingkai Su, Ming Wu, Mengqiu Xu, Yihao Zuo, Chuang Zhang and Bin Huang

Remote Sens. 2023, 15(16), 4102; https://doi.org/10.3390/rs15164102 - 21 Aug 2023

Viewed by 1056

Abstract

Sea fog detection (SFD) presents a significant challenge in the field of intelligent Earth observation, particularly in analyzing meteorological satellite imagery. Akin to various vision tasks, ImageNet pre-training is commonly used for pre-training SFD. However, in the context of multi-spectral meteorological satellite imagery, the initial step of deep learning has received limited attention. Recently, pre-training with Very High-Resolution (VHR) satellite imagery has gained increased popularity in remote-sensing vision tasks, showing the potential to replace ImageNet pre-training. However, it is worth noting that the meteorological satellite imagery applied in SFD, despite being an application of computer vision in remote sensing, differs greatly from VHR satellite imagery. To address the limitation of pre-training for SFD, this paper introduces a novel deep-learning paradigm to the meteorological domain driven by Masked Image Modeling (MIM). Our research reveals two key insights: (1) Pre-training with meteorological satellite imagery yields superior SFD performance compared to pre-training with nature imagery and VHR satellite imagery. (2) Incorporating the architectural characteristics of SFD models into a vanilla masked autoencoder (MAE) can augment the effectiveness of meteorological pre-training. To facilitate this research, we curate a pre-training dataset comprising 514,655 temporal multi-spectral meteorological satellite images, covering the Bohai Sea and Yellow Sea regions, which have the most sea fog occurrence. The longitude ranges from 115.00E to 128.75E, and the latitude ranges from 27.60N to 41.35N. Moreover, we introduce SeaMAE, a novel MAE that utilizes a Vision Transformer as the encoder and a convolutional hierarchical decoder, to learn meteorological representations. SeaMAE is pre-trained on this dataset and fine-tuned for SFD, resulting in state-of-the-art performance. For instance, using the ViT-Base as the backbone, SeaMAE pre-training which achieves

64.18 %

surpasses from-scratch learning, natural imagery pre-training, and VRH satellite imagery pre-training by

5.53 %

2.49 %

, and

2.21 %

, respectively, in terms of Intersection over Union of SFD. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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17 pages, 11862 KiB

Open AccessArticle

Comparisons of the Urbanization Effect on Heat Stress Changes in Guangdong during Different Periods

by Wen Li, Liya Chao, Peng Si, Huixian Zhang and Qingxiang Li

Remote Sens. 2023, 15(11), 2750; https://doi.org/10.3390/rs15112750 - 25 May 2023

Cited by 2 | Viewed by 1178

Abstract

While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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16 pages, 7324 KiB

Open AccessArticle

Towards a Consistent Wind Data Record for the CFOSAT Scatterometer

by Xiaoheng Mou, Wenming Lin and Yijun He

Remote Sens. 2023, 15(8), 2081; https://doi.org/10.3390/rs15082081 - 14 Apr 2023

Cited by 2 | Viewed by 1153

Abstract

Since launch, the Ku-band rotating fan-beam scatterometer onboard the China–France Oceanography Satellite (CFOSAT) has provided valuable sea surface wind measurements for more than four years. The performance of CFOSAT scatterometer (CSCAT)-derived wind vectors is generally good in terms of root-mean-square error, while the absolute calibration error remains an issue in the current CSCAT product. In this paper, the temporal variation in CSCAT winds is overviewed by analyzing the collocated CSCAT and numerical weather prediction (NWP) model winds. Then, the reasons for the inconsistency of CSCAT-retrieved winds are discussed. The results show that the imperfect calibration of radar backscatter coefficients is likely the main problem of CSCAT wind processing. Consequently, a running-window-based (i.e., weekly) ocean calibration is proposed to evaluate the consistency of CSCAT radar backscatters, and in turn, to recalibrate CSCAT backscattering measurements before the reprocessing of CSCAT wind data. Although the proposed method is not feasible for the near-real-time processing of CSCAT data, it significantly mitigates the temporal variations in CSCAT wind speed bias, resulting in a more consistent CSCAT wind data record that may be beneficial to meteorological quantitative applications. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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21 pages, 18882 KiB

Open AccessArticle

SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements

by Changjing Xu, Zhixiong Wang, Xiaochun Zhai, Wenming Lin and Yijun He

Remote Sens. 2023, 15(6), 1630; https://doi.org/10.3390/rs15061630 - 17 Mar 2023

Cited by 1 | Viewed by 1094

Abstract

This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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16 pages, 4433 KiB

Open AccessTechnical Note

The Sensitivity of Large Eddy Simulations to Grid Resolution in Tropical Cyclone High Wind Area Applications

by Yi Jing, Hong Wang, Ping Zhu, Yubin Li, Lei Ye, Lifeng Jiang and Anting Wang

Remote Sens. 2023, 15(15), 3785; https://doi.org/10.3390/rs15153785 - 30 Jul 2023

Viewed by 929

Abstract

The question of at what resolution the large eddy simulations (LESs) of a tropical cyclone (TC) high wind area may converge largely remains unanswered. To address this issue, LESs with five resolutions of 300 m, 100 m, 60 m, 33 m, and 20 m are performed in this study to simulate a high wind area near the radius of maximum wind of Typhoon Chanthu (2021) using the Weather Research and Forecasting (WRF) model. The results show that, for a limited area LES, model grid resolution may alter the local turbulence structure to generate significantly different extreme values of temperature, moisture, and winds, but it only has a marginal impact on the median values of these variables throughout the vertical column. All simulations are able to capture the turbulent roll vortices in the TC boundary layer, but the structure and intensity of the rolls vary substantially in different resolution simulations. Local hectometer-scale eddies with vertical velocities exceeding 10 m s⁻¹ are only observed in the 20 m resolution simulation but not in the coarser resolution simulations. The ratio of the resolved turbulent momentum fluxes and turbulent kinetic energies (TKEs) to the total momentum fluxes and TKEs appears to show some convergence of LESs when the grid resolution reaches 100 m or finer, suggesting that it is an acceptable grid resolution for LES applications in TC simulations. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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12 pages, 7782 KiB

Open AccessTechnical Note

Observed Surface Wind Field Structure of Severe Tropical Cyclones and Associated Precipitation

by Rong Du, Guosheng Zhang and Bin Huang

Remote Sens. 2023, 15(11), 2808; https://doi.org/10.3390/rs15112808 - 29 May 2023

Cited by 1 | Viewed by 1446

Abstract

Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, this study assessed the surface wind fields from high spatial resolution Synthetic Aperture Radar (SAR) observations, the fifth generation ECMWF reanalysis for the global climate and weather (ERA5) data and the Tropical Cyclone Winds and Inflow Angle Asymmetry (TCIAA) wind model. The results showed that SAR data are sufficient to reveal the surface wind field near a TC center and can accurately describe TC intensity and size under severe TC conditions. Then, a new, improved statistical wind structure model was set up using ERA5 data alone based on the assessment. In addition, the warm sea surface (SST > 26.5 °C) produced stronger TC wind fields and heavier precipitation. When the SST was higher (lower), the heavy rainfall was located on the left (right) side of the TC track and the strong positive correlation between wind speed and precipitation increased as the SST decreased. Full article

(This article belongs to the Special Issue Remote Sensing and Parameterization of Air-Sea Interaction)

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