Sea Surface Temperature Images Research Articles

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.

Observational Evidence of Cold Filamentary Intensification in an Energetic Meander of the Antarctic Circumpolar Current

Abstract Eddy stirring at mesoscale oceanic fronts generates finescale filaments, visible in submesoscale-resolving model simulations and high-resolution satellite images of sea surface temperature, ocean color, and sea ice. Submesoscale filaments have widths of O(1–10) km and evolve on time scales of hours to days, making them extremely challenging to observe. Despite their relatively small scale, submesoscale processes play a key role in the climate system by providing a route to dissipation; altering the stratification of the ocean interior; and generating strong vertical velocities that exchange heat, carbon, nutrients, and oxygen between the mixed layer and the ocean interior. We present a unique set of in situ and satellite observations in a standing meander region of the Antarctic Circumpolar Current (ACC) that supports the theory of cold filamentary intensification—revealing enhanced vertical velocities and evidence of subduction and ventilation associated with finescale cold filaments. We show that these processes are not confined to the mixed layer; EM-APEX floats reveal enhanced downward velocities (>100 m day−1) and evidence of ageostrophic motion extending as deep as 1600 dbar, associated with a ∼20-km-wide cold filament. A finer-scale (∼5 km wide) cold filament crossed by a towed Triaxus is associated with anomalous chlorophyll and oxygen values extending at least 100–200 dbar below the base of the mixed layer, implying recent subduction and ventilation. Energetic standing meanders within the weakly stratified ACC provide an environment conductive to the generation of finescale filaments that can transport water mass properties across mesoscale fronts and deep into the ocean interior.

Automated ocean front feature mapping using Sentinel-1 with examples from the Gulf Stream

ABSTRACT This study assessed the ability of Sentinel-1 radial velocity (RVL) products to mark the position of ocean current front features, using the Gulf Stream (GS) as a case study. RVL-derived front features were compared to fronts derived from Multi-scale Ultra-high Resolution Sea Surface Temperature Analysis (MURSST) data. A ridge filter was used to find fronts in both the Sea Surface Temperature (SST) and RVL data, and the similarity between each pair of fronts was measured using the discrete Hausdorff Distance (HD) and Mean Hausdorff Distance (MHD). Front features were correctly identified in concurrent SST and RVL data pairs in 65% of cases. The automatic front extraction sometimes failed by misclassifying an eddy or similar ocean feature as the ocean current in either the RVL or SST image, and sometimes failed to extract the entire length of the front visible within the image. SST and RVL fronts were classified manually to determine the success rate of the automatic front extraction, and to exclude front extraction errors from further analysis. The results demonstrated that RVL products were effective at determining the location of ocean fronts where the angle between the front’s normal vector and the sensor’s azimuthal heading is less than ~ 40°. A mean HD of 25.5 km and a mean MHD of 10.8 km was calculated for all front pairs in the study area.

An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields

Abstract. We have assembled 2 851 702 nearly cloud-free cutout images (sized 144 km × 144 km) of sea surface temperature (SST) data from the entire 2012–2020 Level-2 Visible Infrared Imaging Radiometer Suite (VIIRS) dataset to perform a quantitative comparison to the ocean model output from the MIT General Circulation Model (MITgcm). Specifically, we evaluate outputs from the LLC4320 (LLC, latitude–longitude–polar cap) 148∘ global-ocean simulation for a 1-year period starting on 17 November 2011 but otherwise matched in geography and the day of the year to the VIIRS observations. In lieu of simple (e.g., mean, standard deviation) or complex (e.g., power spectrum) statistics, we analyze the cutouts of SST anomalies with an unsupervised probabilistic autoencoder (PAE) trained to learn the distribution of structures in SST anomaly (SSTa) on ∼ 10–80 km scales (i.e., submesoscale to mesoscale). A principal finding is that the LLC4320 simulation reproduces, over a large fraction of the ocean, the observed distribution of SSTa patterns well, both globally and regionally. Globally, the medians of the structure distributions match to within 2σ for 65 % of the ocean, despite a modest, latitude-dependent offset. Regionally, the model outputs reproduce mesoscale variations in SSTa patterns revealed by the PAE in the VIIRS data, including subtle features imprinted by variations in bathymetry. We also identify significant differences in the distribution of SSTa patterns in several regions: (1) in an equatorial band equatorward of 15∘; (2) in the Antarctic Circumpolar Current (ACC), especially in the eastern half of the Indian Ocean; and (3) in the vicinity of the point at which western boundary currents separate from the continental margin. It is clear that region 3 is a result of premature separation in the simulated western boundary currents. The model output in region 2, the southern Indian Ocean, tends to predict more structure than observed, perhaps arising from a misrepresentation of the mixed layer or of energy dissipation and stirring in the simulation. The differences in region 1, the equatorial band, are also likely due to model errors, perhaps arising from the shortness of the simulation or from the lack of high-frequency and/or wavenumber atmospheric forcing. Although we do not yet know the exact causes for these model–data SSTa differences, we expect that this type of comparison will help guide future developments of high-resolution global-ocean simulations.

Super Resolution of Satellite-Derived Sea Surface Temperature Using a Transformer-Based Model

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.

Piece‐wise constant cluster modelling of dynamics of upwelling patterns

AbstractA comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three‐stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least‐squares clustering criterion. The approach has been successfully applied to real‐world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.

Pemetaan Zona Potensial Penangkapan Ikan (ZPPI) Di Perairan Selat Bali Menggunakan Citra Satelit Aqua MODIS


 
 
 
 The abundance and distribution of fish are influenced by environmental productivity, such as chlorophyll-a distribution and variations in sea surface temperature (SST). The lack of technology fishermen use in the waters of the Bali Strait has resulted in the high potential of fish resources not being optimally utilized. This study aimed to determine potential fishing zones by utilizing Aqua MODIS satellite data which can detect SST and chlorophyll-a. The image data observed was from October to December 2018 to 2020. The method used is the Single Image Edge Detection Algorithm, where this algorithm is used to determine potential fishing areas designed to detect fronts in SST images. The results showed that in December 2018, 7 potential fishing points were produced, marked with a chlorophyll-a value of 0.25 mg/m3 and an SST of 29.25°C. In December 2020, 82 potential fishing points were produced, with a chlorophyll-a value of 0.31 mg/m3 and an SPL of 29.79°C. Validation was carried out by analyzing the result of image processing with fish catch data obtained from Pengambengan PPN, Muncar PPP, and Kedonganan TPI. SST, with a value of 27°C, is the ideal temperature for fish where when the temperature increases, the number of potential fish catch points decreases and vice versa. The distribution of chlorophyll-a content with a value of > 0.2 mg/m3 affects the number of potential fishing points, where the higher the chlorophyll-a content, the more fishing ground points and vice versa.
 
 
 

Core–shell clustering approach for detection and analysis of coastal upwelling

A comprehensive approach is presented to analyze season’s coastal upwelling represented by weekly sea surface temperature (SST) image grids. The proposed model, core–shell clustering, assumes that the season’s upwelling can be divided into shorter periods of stability, time ranges, consisting of constant core and variable shell parts. A one-by-one core–shell clustering algorithm is provided. The algorithm parameters are automatically derived from the least-squares clustering criterion.The approach applies to SST gridded data for sixteen successive years (2004–2019) of coastal upwelling in the western Iberian coast, the northernmost branch of the Canary Current Upwelling System. Our results show that at each season, there are 3 to 5 time intervals, the ranges, at which the upwelling presents stable core patterns of relatively cold water surrounded by somewhat larger shell areas of warmer waters. Based on other experimental computations performed by our team, we conclude that this pattern is not just a purely local phenomenon but has a more global meaning. Inter-annual time series analysis are consistent among themselves and with existing expert domain knowledge.

A New Upwelling Index for the Moroccan Atlantic Coast for the Period between 1982–2021

Being a component of the Eastern Boundary Upwelling (EBU) ecosystem, Morocco’s Atlantic coast presents high biological production throughout the year, with seasonal variations in upwelling dynamics. This characterization reflects the inherent nature of EBU’s ecosystems. In this work, we develop a novel methodology to compute a new upwelling index based on the analysis of sea surface temperature (SST) images. Our new upwelling index is not only simple to calculate but also efficient. Indeed, it is limited only to the upwelling region, which has allowed the improvement of the quantification and analysis of the seasonal and interannual variability of the upwelling dynamics. The new proposed upwelling index is based on the application of a recent segmentation method that allows for the monitoring of upwelling dynamics using satellite observations. The proposed upwelling index is applied to a 40-year database of weekly SST images covering the period from 1982 to 2021, and the results are used to analyze seasonal and interannual variations of the upwelling in the region.

Exploring biophysical links of catch rate from mid-water trawl fishery in the north-west coast of India, Eastern Arabian Sea: A remote sensing approach

Fish frequently shift their distribution ranges as a result of changes in preferred environmental factors. Knowledge on distribution of fish in relation to their environmental optima is crucial for improving the understanding of fishing grounds and planning sustainable exploitation. This study investigated the monthly variability in environmental factors impacting the catch rate and the spatio-temporal distribution patterns of fish along northwest coast of India (NWCI) from 2017 to 2019. The time series images of sea surface temperature (SST), sea surface height anomaly (SSHa), chlorophyll-a concentration (CHL), and euphotic depth (Zeu) indicate close coupling between bio-physical variables in the region. Generalized Additive Model (GAM) applied to the catch rate of mid-water trawlers found that CHL and Zeu were the most influential variables for ribbonfish distribution, while CHL and SST influence squid distribution and SST and SSHa influence cuttlefish distribution. The total deviation explained 37.70 %, 10.70 %, and 22.28 % in the final model for simulating the spatio-temporal distribution of ribbonfish, squid, and cuttlefish, respectively. Environmental factors were significant in the final GAM model (p < 0.05). Spatio-temporal distribution patterns of fishery resources from mid-water trawlers showed that they were related to biophysical changes in the northwest coast of India. The high catch rate was observed in the northern latitudes during the post-monsoon (October), the major fishing season in the region. However, it shrank and shifted to southern latitudes during the winter (January). This study could be adapted to nearby countries in the Arabian Sea region for an effective and useful tool to monitor and manage the fishery resources. Furthermore, it could aid in decision-making for policymakers and resource managers to formulate strategies for holistic marine fishery management and sustainable resource exploitation.

Capture Area Compatibility with Map of Fishery Potential Area Based on Chlorophyl-a and Sea Surface Temperature in Aru Sea Waters-Indonesia

The Republic of Indonesia's Fisheries Management Area (Wilayah Pengelolaan Perikanan Negara Republik Indonesia) marine waters is an area with high capture fisheries potential, reaching 20% of the national fish potential. Fishing is often hampered by the lack of information related to Fishing Potential Zones, resulting in reduced efficiency. The solution offered is to utilize geographic information systems (GIS) or remote sensing with aqua MODIS satellite images of chlorophyll-a and sea surface temperature as data for mapping potential fishing zones. The objectives of this research are to understand the operation of purse seines, analyze the influence of sea surface temperature and chlorophyll-a on purse seine catches, and determine the level of suitability of fishing areas with maps of potential fishing zones. To answer the objective, non-experimental based applied research was conducted with a case study approach, survey method and correlation approach to determine the effect of chlorophyll-a and sea surface temperature variables on catches. The tools used hardware, namely laptops and computers with the software needed research are SEADAS, SAGA, QGIS, Ms. Excel and SPSS. Data on the distribution of and chlorophyll-a used in the study are data from Aqua MODIS level 1x1 kilometer resolution downloaded on the page https://oceancolor.gsfc.nasa.gov/.&#x0D; As for the results of statistical testing, there is an influence of chlorophyll-a and sea surface temperature simultaneously and partially on the catch by giving an influence of 73.6%. With the distribution of chlorophyll-a concentration and sea surface temperature in the KM fishing area. The level of conformity of fishing areas with maps of potential fishing zones on fishing trips from December 2020 to April 2021 reached a percentage of conformity of 55.93% with a total catch of 209,360 Kg, indicating the selection of fishing areas by conventional skippers on KM XXX compared to the forecast of fishing areas using aqua MODIS satellite imagery remote sensing technology can be categorized as good even though it only relies on experience and information about fishing areas from other vessels.

MEC: A Mesoscale Events Classifier for Oceanographic Imagery

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.

Seasonal variability in spatial patterns of sea surface cold- and warm fronts over the continental shelf of the northern South China Sea

Ubiquitous fronts are a key part of energy transfer from large scales to small scales and exert a great impact on material exchange and biogeochemical processes. The spatial pattern and seasonal variability of cold- and warm fronts over the wide shelf of the northern South China Sea (SCS) are investigated using a 20-year time series (2002−2021) of 1-km spatial resolution Group for High Resolution Sea Surface Temperature (GHRSST) images. Our analysis shows distinct spatial and temporal variability in the occurrence of the cold- and warm fronts. Over the inner shelf (depth &amp;lt;50 m), the band-shaped cold fronts are predominately observed during spring through autumn from east of Hainan Island to Taiwan Shoal, with the presence of the maximum intensity and probability in winter. The frontal formations are possibly associated with the joint effect of the Guangdong Coastal Current (GCC) and the South China Sea Warm Current. During summer, the inshore fronts have relatively low probability and gradient magnitude. The warm fronts mainly occur off the western Guangdong coast possibly due to the southwestward-flowing GCC, whereas the cold fronts dominate off the eastern Guangdong coast and the eastern Hainan Island largely because of the coastal upwelling. Over the outer shelf (depth &amp;gt;50 m), the finer-scale cold- and warm fronts are discretely observed, with relatively weaker intensity and lower probability. The frontal activities are very vigorous in winter but slightly quiescent in summer, apparently resulting from the influence of the rich submesoscale processes in the SCS. This study could help improve our understanding of the SCS oceanic multiscale dynamics.

Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images

Optical remotely sensed data often have data gaps due to cloud coverage, which hinders their full potential in many environmental applications. The question of how to accurately and effectively reconstruct the measurements in the data gaps remains a challenge. In this study, we developed a bidirectional long short-term memory (BiLSTM) model with a novel and adaptable custom temporal penalty layer for spatiotemporal gap-filling. The model was trained and tested in time-series daily night-time sea surface temperature (SST) images acquired by the Himawari-8 satellite. The modelling results showed strong performance, accurately reconstructing spatial and temporal features of cloud affected SST data. Our neural network achieved a per-image MAE of 0.1193°C and per-image RMSE of 0.1293°C. The model was also able to produce realistic SST time-series predictions that were consistent with the expected seasonal variables. Importantly, the BiLSTM model outperformed the previous state-of-the-art Simple Spatial Gapfilling Processor (SSGP) algorithm in terms of error metrics, structural similarity, and computational efficiency. Overall, the results of this study indicates that the BiLSTM neural network model we developed is highly suitable for spatiotemporal gap-filling of SST and other remotely sensed data.

Abrupt change of a thermal front in a high-biomass coastal zone during early spring

Coastal fronts play vital roles in local biogeochemical environment. An abrupt change of Zhejiang-Fujian coastal front (ZFCF) during early spring was well captured by multi-source satellite-retrieved sea surface temperature images. Here in this study, we investigated the mechanism of the abrupt decay with a combination of satellite observation and numerical simulation. Correlation analysis of long-term reanalysis data indicates that the variability of wind, heat flux and the Zhejiang-Fujian coastal current (ZFCC) have significant relationships with the variation of ZFCF in winter. Following heat budget analysis points out that net heat flux and horizontal advection are important in determining the net temperature tendency difference between two water masses of ZFCF during this process. To further explore the intrinsic physical roles of different dynamic factors, a comprehensive numerical investigation was conducted. Compared with the observations, the model reproduced the abrupt change process of the ZFCF satisfyingly. Sensitive experiments reveal that the weakening of the ZFCC, caused by the relaxation of the monsoon, contributes to the abrupt decay of ZFCF in the first half period, and heating effect of the Kuroshio Intrusion (KI) water gives rise to the following half period of the decay under the recovered monsoon. Further, with the impact of the KI water after the change, the ZFCF can be maintained even if the ZFCC is weak, whereas in January, the contribution of the KI water to the formation of the ZFCF seems to be limited under the prevailing monsoon. Besides, the riverine discharge and the tidal forcing can also modulate the spatiotemporal variation of ZFCF, the decrease of the river input also intensifies that decay, while tides fix the front at a specific depth.

Variability of the thermal front and its relationship with Chlorophyll-a in the north Bay of Bengal

The spatial and temporal variability of the thermal fronts and their relationship with chlorophyll-a in the north Bay of Bengal (BOB) was studied using time series of MODIS daily Sea Surface Temperature (SST) and weekly chlorophyll-a concentration to investigate the locations of intense fish catch activities in the continental shelf and slope as inferred from NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime boat detection products. An edge-detection algorithm operated on each SST image to identify thermal fronts in which a gradient of 0.3 °C/pixel (∼0.075 °C/km) was chosen following the sensitivity analysis. These thermal fronts at the daily scale were aggregated weekly to estimate the thermal front density. A least square-based harmonic analysis procedure was adopted to estimate mean climatology and seasonal harmonics of thermal front density and chlorophyll-a data sets. It shows persistent and coherent spatial features between the datasets in their climatology and seasonal cycles. They exhibit distinct locations of maxima co-occurring between the 10 and 50 m isobaths in the continental shelf and slope region coincided with the areas of the VIIRS mapped fishing vessels cluster sites. The annual frequency dominates the seasonal cycles of TFD with a positive phase during November–March (winter and spring seasons) and peaks in January. Mean (residual) circulation, as simulated from a regional ocean model, shows coherent spatial features congruent to the persistent thermal fronts and steered by the bathymetry. These locations with a high probability of thermal fronts and high concentrations of Chlorophyll-a can be considered as the perennial potential fishing zone, with the winter season being a more favorable period for fishing.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

Thermal Structure of Water Exchange at the Entrance of a Tide-Dominated Strait

In summer, westward residual flow in the Qiongzhou Strait (QS) drives a water transport through the strait into the Beibu Gulf, playing a vital role in modifying the gulf-scale circulation. By utilizing multi-source observations, realistic simulations, as well as idealized modeling, the water exchange process at the western entrance of the QS was explored from the perspective of thermal structure. High-resolution satellite sea-surface temperature images captured an interesting thermal phenomenon in the studied region, i.e., a trident-shaped warm water tongue. Validations of numerical results suggested satisfactory model performance in reproducing the trident-shaped warm water tongue. Modeling results together with cruise observations indicates extremely weak stratification in the studied region, which is a result of local intense tidal mixing. The cold upwelling water developed at the eastern side of the strait arrived at the western entrance by the westward residual flow, and then encountered and communicated with the well-mixed warm gulf water there. The alternatively deep/shallow topography (sea trough/ridge) triggers spatial variability in tidal current intensity: stronger currents over the deep area with weaker flow above the shallow region leads to alternatively warm/cold thermal distribution in the cross-isobath direction. Further investigation demonstrates that, through modifying the circulation and regulating the intensity of upwelling on the eastern side of the QS, wind also makes a significant contribution to the thermal distribution at the western entrance of the QS.

An Observation-Based Study of Gulf Stream Meander Kinematics Offshore of Cape Hatteras

Gulf Stream (GS) meander structure and propagation offshore of Cape Hatteras are investigated by integrating current measurements from a bottom-moored Acoustic Doppler Current Profiler (ADCP) with high-frequency radar (HFR) surface current measurements and satellite Sea Surface Temperature (SST) images during November 2014. The ADCP measurements provide well-resolved current observations throughout most of the water column, while hourly surface current measurements from HF radars and available satellite SST images provide spatial context to the GS orientation, meander propagation, circulation, and shear structure in the region of the ADCP mooring. The observations provide new insights about meander propagation and evolution in this important transition region. ADCP measurements observed that the increase and deepening intervals of the downstream current with approaching meander crests were typically longer than those for the decrease and shoaling of downstream current, consistent with prominent skewed crests near the surface. The transition time from trough to crest is much greater than that from crest to trough, reflecting the asymmetry in the downstream velocity structure. Vertical shears in the downstream and cross-stream velocity components are indicative of a cold dome centered downslope and offshore of the ADCP. Local maxima in downstream current and bottom temperature at the ADCP occur simultaneously, are accompanied by large vertical velocities, and are led by offshore currents in the upper water column. The mean meander phase speed estimated with HFRs is 48 km/day. Meander periods during the month are about 5-6 days. Where the maxima are seen in the water column, downstream currents reach 2.5 m/s, with current reversals sometimes in excess of 0.5 m/s. Downstream currents occupy an increasing portion of the water column as a crest approaches, and a decreasing fraction as a trough approaches. The deepening increase in downstream velocities with approaching crests is often accompanied by an increase in upstream velocities near the bottom.