Learning multi-tracer convolutional models for the reconstruction of high-resolution SSH fields

A primitive equation ocean model has been applied to the study of mesoscale ocean dynamics and prediction using sea surface height information derived from a satellite‐borne altimeter. Results from a model of the Gulf of Mexico were directly compared with the altimeter data from GEOS 3 and Seasat reported by Marsh et al. (1984) and in situ hydrographic data analyzed by Maul and Herman (1985). In the eastern Gulf the amplitude and position of relative maxima of sea surface height variability were found to be similar for model, altimeter, and in situ data. In the central Gulf the model and the altimeter‐derived sea surface height variability maps were similar but differed considerably from the in situ results. The mean sea surface in the model and from the in situ data also agreed well in the eastern Gulf, but there was little agreement in the central Gulf. While there is no geoid adequate for determining an independent altimetric estimate of the mean dynamic height of the sea surface in the Gulf by differencing the geoid with the altimeter‐derived mean sea surface, the model dynamic mean sea surface can be subtracted from the mean surface of Marsh et al. (1984) to obtain a new estimate of the Gulf geoid, which may be the best geoid available in that region for several years. A benchmark experiment (“truth”) was integrated to statistical equilibrium and compared with results from four experiments in which the model was initialized with fields modified from archived benchmark data. The experiments differed only in the initialization fields. Each experiment was integrated for 100 days with inflow transport remaining constant throughout the integration. The idealized experiments were initialized geostrophically (1) with the exact sea surface and pycnocline height fields, (2) with only the exact sea surface heights and a pycnocline assumed to compensate it such that the deep pressure perturbations vanished, (3) just as in case 2 but with a geoid error component added on small spatial scales, and (4) just as in case 3 except the geoid error model included an additional contribution in strong geoid gradient regions. The sequence of four numerical experiments showed that (1) geostrophic initialization with exact sea surface and pycnocline height information provided accurate forecasts to 100 days, (2) even when only the sea surface height information was provided to the numerical model for geostrophic initialization and deep pressure perturbations assumed to vanish at the initial time, the forecasts were superior to persistence or climatology over the 100‐day forecast period, and (3) errors in the geoid on spatial scales comparable to the grid resolution of the model did not seriously degrade the forecast, even in dynamically active regions with large gradients in the geoid height where instability processes were observed to occur.

An investigation of the Helmholtz and wave-vortex decompositions on surface currents in a coastal region

A 'regime shift' is characterized by an abrupt transition from one quasi-steady climatic state to another, and its transition period is much shorter than the lengths of the individual epochs of each climatic state. In the present study, we investigate when regime shifts occurred and what was the difference in climatic states before and after the shifts, using the wintertime sea surface temperature (SST) field in the Northern Hemisphere. The relationship between changes in the SST field, and those in the atmospheric circulation, is also investigated. In order to detect organized patterns of the SST variations, we apply an empirical orthogonal function (EOF) analysis. As the results, the first mode is identical to El Nino/Southern Oscillation (ENSO) and so-called Pacific Decadal Oscillation (PDO), and corresponds to the Pacific/North American (PNA) pattern. The second mode, which relates to the Arctic Oscillation (AO), has a zonally elongated signal in both the North Atlantic, and North Pacific. EOF analyses to each oceanic basin are made separately, and the robustness of these modes is confirmed. In the present study, we define the regime shifts as the 'significant' and 'systematic' changes between the two quasi-steady states, continuing more than 5 years. Then, in order to identify the years when regime shifts occurred in the SST field, we carefully inspect the time series of original gridded SST data and those of the EOF modes. As a result, six regime shifts are detected in the study period from the 1910s to the 1990s: 1925/26, 1945/46, 1957/58, 1970/71, 1976/77 and 1988/89. It is ascertained that the shifts at almost all grids are completed within one year. All regime shifts having similar SST and atmospheric circulation pattern, including the changes in an intensity of the Aleutian Low (AL), and the corresponding SST changes in the central North Pacific. All regime shifts can be well described by the combination of the first and the second EOF modes. The duration between each regime shift is about 10 years, which are identical to the PDO. The simultaneous shifts in the first, and the second EOF modes, imply that the change in the AL activity associated with the PNA pattern, might have some connection with that of the AO.

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.

The altimetric data from the first 2.5‐year Geosat Exact Repeat Mission were used to estimate the mean sea surface height (SSH) field in the region of the Gulf Stream and its recirculation gyres. Assuming the instantaneous surface velocity field is composed of an eastward flowing jet and two westward recirculating flows, we used the time‐varying surface data from the altimeter to determine the shape of the along‐track mean SSH profiles and the historical hydrographic data to constrain the net SSH difference across the Gulf Stream system. The two‐dimensional mean SSH field was determined by objectively mapping the mean height profiles along the ascending and descending tracks. The SSH jump across the mean Gulf Stream has a maximum of 1.15m around 65°W and drops to an almost constant 0.9m downstream of the New England Seamount Chain (NESC). While the SSH jump associated with the mean northern recirculating flow is mostly uniform, we found that the Gulf Stream's southern recirculation has two local gyres that are separated by the NESC. An attempt was then made to estimate the mean deep circulation in this region by comparing the mean SSH field derived from the altimetry data and the surface dynamic height field based on the historical hydrographic data. Despite the large uncertainties, the mean deep flow pattern thus estimated agrees favorably with the overall circulation pattern from the long‐term current meter observations. Like the well‐defined northern recirculation gyre, we found that a continuous southward flow exists along 57.5°W, which follows closely along the deep layer potential vorticity contours. To the south of the Gulf Stream, the deep circulation consists of two separated recirculation gyres; the recirculation gyre to the east of the NESC appears to be trapped around the Corner Rise.

This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach’s performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties.

Assimilating remote sensing and in situ observations into a coastal model of northern South China Sea using ensemble Kalman filter

In this study we use the monthly mean sea surface temperature (SST) fields (1985-1997), calculated by the National Oceanic and Atmospheric Administration (NOAA) Pathfinder project analysis of advanced very high resolution radiometer (AVHRR) data, to investigate the temporal and spatial variability of warm/cool anomalies in the Levantine Basin (LB). Wind data from European Remote Sensing Satellite-1/2 (ERS-1/2) over the LB are analysed and surface wind stress and wind stress curls are calculated. First, we employed an optimal interpolation scheme to build up a dataset for 1985-1997 on an 18 km 2 18 km grid. An ensemble mean SST field ( T ) has been established with a rather weak horizontal gradient (19.4° C south-east of Rhodes to 22.4° C southeast of the LB). Second, we performed a composite analysis to obtain the monthly averaged SST anomaly field ( T ) deviation from the ensemble mean. Three areas of evident SST anomalies were found: south of Rhodes, south-east of Crete and north-east of Cyprus. During the spring-to-summer transition (March to May), a small low anomaly is formed south-east of Crete ( T <-1.4° C in May), and a small high anomaly is formed south of Rhodes ( T >-0.4° C in May). During the autumn-to-winter transition (October to November), a small high anomaly forms south-east of Crete ( T >1.2° C in November), and a small low anomaly forms between Rhodes and Antalia Bay ( T <-0.2° C in November). Third, we performed an empirical orthogonal function (EOF) analysis on the residual T relative to T + T in order to obtain transient and interannual variations of the SST fields. EOF1 explains 73% of the total variance and represents the warm/cool anomaly pattern, with a maximum value in the region between the south of Rhodes and Cyprus and a minimum value off the western part of the Egyptian coast. EOF2 explains 6% of the total variance. A strong LB warm anomaly (1° C warmer) appears during August 1988, May 1994 and September-October-November 1994. A strong cool anomaly (1° C cooler) occurs during May 1987, November 1988, August 1989 and May 1992. The cross-correlation coefficient between wind stress curl from ERS-1/2 over the LB in the period from January 1992 to December 1997 and the SST anomaly were calculated, and it reveals the air-sea interaction in the LB.

Different estimates of the monthly mean sea surface temperature (SST) fields are intercompared. Fields derived from ship data alone differ substantially in high SST gradient regions and in areas of low data density. These differences exhibit seasonal and interannual variations which are spatially correlated. The large bias and variability preclude interchangeable use of different SST products derived from ships alone.

The fusion of real-time remote sensing imagery with numerical ocean circulation models provides a unique capability for monitoring the coastal ocean. A real-time ocean monitoring system is currently operating in the Gulf of Mexico. Dynamic changes in the ocean environment occur on scales of hours, days, weeks and years, which influence biological, ecological and physical processes. By monitoring these processes at different spatial resolutions, new enhanced capability is available to coastal managers and researchers for making decisions. Monitoring of physical processes and bio-optical responses are currently being done for the Gulf of Mexico. Open ocean eddies and loop current interact with coastal processes such as river plumes and tides and have significant impact on the biological and ecological processes along our shores. Until now we had no capability to routinely monitor these ocean conditions without insitu sensors and observations. Now the “weather” in the Gulf of Mexico is being monitored daily and provides coastal managers and researchers an edge in tracking or pin-pointing events. This information assists the decision maker in identifying how physical events are influencing the coastal ecosystem. How is this being done? Improved bio-optical algorithms have been developed and applied to ocean color satellite imagery from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) providing estimates of water constituents such as absorption properties from phytoplankton, color dissolved organic matter (CDOM), and detritus and backscattering properties related to sediment concentration. In addition, advances in numerical circulation modeling (Navy Coastal Ocean Model — NCOM) provide nowcasts and forecasts of coastal processes (currents, sea surface height, salinity and temperature). Physical ocean properties of temperature, salinity, and currents from NCOM are combined with daily latest pixel composite products of phytoplankton, CDOM, and detritus absorption, backscattering and sea surface temperature (SST). We demonstrate an advanced environmental monitoring capability available operationally that highlights the fusion of physical (model) and bio-optical properties (satellite). The entire Gulf conditions are demonstrated daily in addition to higher resolution monitoring of coastal conditions. We will focus on Mississippi and Louisiana Coast using high resolution (250 meter resolution) MODIS imagery. We will demonstrate a capability of supporting coastal manager's and researchers integrating satellite bio-optical products, model circulation (current vectors), sea surface height (contours), salinity (contours) and temperature (contours) layers into Google Earth. In addition, we will demonstrate promising new research using satellite optics and numerical models to create surface optical forecast (hourly increments up to 48 hours) and a 3-D optical volume of the ocean derived from a physical (model) / optical (satellite) Gaussian relationship optimized using insitu profiles. By using these methods of fusing the satellite and ocean circulation model it will provide coastal zone managers with a new capability to assess, predict and track Harmful Algal Blooms, Hypoxia, and sediment discharge.

Remotely sensed features in the US JGOFS Arabian Sea Process Study

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

In the near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e., to compute spatial derivatives of the Sea Surface Height (SSH) measurements, the uncorrelated, small-scale noise and errors expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first-, second-, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the western Mediterranean Sea from a 1/60 ∘ simulation and the SWOT simulator. Based on root mean square error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths, the noise level is reduced by factors of 10 4 and 10 3 for summer and winter, respectively. This is encouraging for the processing of the future SWOT data.

ABSTRACTIn this article, daily fields of sea surface temperature (SST) and sea level anomaly (SLA) of the Western Mediterranean Sea obtained in 2011–2013 are being co-analysed. The study is aimed at assessing potentials of the fields of SLA for manifestation of mesoscale eddies in the region of interest. First, the SST imagery was inspected visually and locations of mesoscale eddies noticed in the images were detected. After that, closed contours were extracted in the fields of SLA and their locations were compared with locations of eddies directly seen in the SST images. Such a comparison showed that among anticyclonic eddies found in SST, about 56.3% had corresponding closed contours of SLA; the percentage was increasing with increasing eddy diameter. For cyclonic eddies, supposedly due to their smaller spatial and time scales, such percentage was only about 20.2%. During the warm season, when the mixed layer is particularly shallow and the turbulence is closer to the two-dimensional model, the correspondence between eddies seen in SST and closed contours of SLA was higher than during the cold period.

The observed signature of mesoscale eddies in sea surface temperature and the associated heat transport