scholarly journals Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

2021 ◽  
Vol 13 (4) ◽  
pp. 744
Author(s):  
J. Xavier Prochaska ◽  
Peter C. Cornillon ◽  
David M. Reiman
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Space Distribution ◽  
Western Boundary ◽  
Learning Modules ◽  
Ocean Science ◽  
The Many ◽  
Future Work

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color).

Deep Learning for Sea Temperature Eddy signature Classification

2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Imagery ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Temperature ◽  
Cloud Coverage

<p>Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years <strong>[1]</strong>. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.</p><p>Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.</p><p>We introduce a novel Deep Learning approach to classify sea temperature eddy signatures <strong>[2]</strong>. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas <strong>[3]</strong>. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.</p><p>We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.</p><p><strong>[1]</strong> <em>Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.</em></p><p><strong>[2]</strong> <em>E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.</em></p><p><strong>[3]</strong> <em>https://www.lmd.polytechnique.fr/dyned/</em></p>

scholarly journals Sea Surface Temperature and Wind Stress Curl Variability near a Cape

2012 ◽  
Vol 42 (11) ◽  
pp. 2073-2087 ◽  
Author(s):  
Renato M. Castelao
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wind Stress ◽  
Current System ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Western Boundary ◽  
Surface Cooling ◽  
Wind Stress Curl ◽  
California Current System

Abstract The coupling between sea surface temperature (SST), SST gradients, and wind stress curl variability near a cape off Brazil is investigated using satellite observations and several different SST high-resolution analyses. The cape is characterized by strong SST fronts year-round, associated with upwelling and advection of warm water offshore in a western boundary current. Observations reveal a strong coupling between crosswind SST gradients and wind stress curl variability, with the predominantly negative crosswind gradients leading to negative, upwelling favorable wind stress curl anomalies. The spatial correlation between empirical orthogonal functions (EOF) of those variables is ~0.6, while the correlation between the EOF amplitude time series of the wind stress curl and crosswind SST gradients is larger than 0.7. The coupling occurs during summer and winter and is strongly modulated by variations in the wind stress directional steadiness. The intensity of the coupling is weaker than around capes on the California Current system, presumably because of higher variability in wind direction off Brazil. During periods of high wind stress directional steadiness off Cape Frio, the coupling is intensified by up to 40%–75%. Wind stress curl is also correlated with SST itself, especially in the vicinity of the cape, although not as strongly as with crosswind SST gradients. The analyses suggest that the observed wind stress curl anomalies can lead to surface cooling of as much as 1°C. If the enhanced upwelling leads to further strengthening of the upwelling front, negative wind stress curl anomalies may be intensified in a positive feedback mechanism.

scholarly journals Observed Transport Variability of the Atlantic Subtropical Cells and Their Connection to Tropical Sea Surface Temperature Variability

2021 ◽  
Author(s):  
Franz Philip Tuchen ◽  
Joke F. Lübbecke ◽  
Peter Brandt ◽  
Yao Fu
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Wind Stress ◽  
Large Scale ◽  
Sea Surface ◽  
Ekman Transport ◽  
Western Boundary ◽  
Eastern Boundary ◽  
Stress Changes

<p>The shallow meridional overturning cells of the Atlantic Ocean, the subtropical cells (STCs), consist of poleward Ekman transport at the surface, subduction in the subtropics, equatorward flow at thermocline level and upwelling along the equator and at the eastern boundary. In this study, we provide the first observational estimate of transport variability associated with the horizontal branches of the Atlantic STCs in both hemispheres based on Argo float data and supplemented by reanalysis products.</p><p>Thermocline layer transport convergence and surface layer transport divergence between 10°N and 10°S are dominated by seasonal variability. Meridional thermocline layer transport anomalies at the western boundary and in the interior basin are anti-correlated and partially compensate each other at all resolved time scales. It is suggested that the seesaw-like relation is forced by the large-scale off-equatorial wind stress changes through low-baroclinic-mode Rossby wave adjustment. We further show that anomalies of the thermocline layer interior transport convergence modulate sea surface temperature (SST) variability in the upwelling regions along the equator and at the eastern boundary at time scales longer than 5 years. Phases of weaker (stronger) interior transport are associated with phases of higher (lower) equatorial SST. At these time scales, STC transport variability is forced by off-equatorial wind stress changes, especially by those in the southern hemisphere. At shorter time scales, equatorial SST anomalies are, instead, mainly forced by local changes of zonal wind stress.</p>

scholarly journals Malvinas-slope water intrusions on the northern Patagonia continental shelf

Ocean Science ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 345-359 ◽  
Author(s):  
A. R. Piola ◽  
N. Martínez Avellaneda ◽  
R. A. Guerrero ◽  
F. P. Jardón ◽  
E. D. Palma ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Continental Shelf ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Local Development ◽  
Drake Passage ◽  
Negative Temperature ◽  
Sea Surface ◽  
Western Boundary ◽  
Boundary Current

Abstract. The Patagonia continental shelf located off southeastern South America is bounded offshore by the Malvinas Current, which extends northward from northern Drake Passage (~55° S) to nearly 38° S. The transition between relatively warm-fresh shelf waters and Subantarctic Waters from the western boundary current is characterized by a thermohaline front extending nearly 2500 km. We use satellite derived sea surface temperature, and chlorophyll-a data combined with hydrographic and surface drifter data to document the intrusions of slope waters onto the continental shelf near 41° S. These intrusions create vertically coherent localized negative temperature and positive salinity anomalies extending onshore about 150 km from the shelf break. The region is associated with a center of action of the first mode of non-seasonal sea surface temperature variability and also relatively high chlorophyll-a variability, suggesting that the intrusions are important in promoting the local development of phytoplankton. The generation of slope water penetrations at this location may be triggered by the inshore excursion of the 100 m isobath, which appears to steer the Malvinas Current waters over the outer shelf.

Assimilation of Low-Peaking Satellite Observations Using the Coupled Interface Framework

2020 ◽  
Vol 148 (2) ◽  
pp. 637-654
Author(s):  
Sergey Frolov ◽  
William Campbell ◽  
Benjamin Ruston ◽  
Craig H. Bishop ◽  
David Kuhl ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Circulation Model ◽  
Satellite Observations ◽  
Sea Surface ◽  
Transfer Model ◽  
Western Boundary ◽  
Diurnal Variability ◽  
Near Surface ◽  
The Impact

Abstract Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge.

Optically Enhanced Super-Resolution of Sea Surface Temperature Using Deep Learning

2021 ◽  
pp. 1-14
Author(s):  
David T. Lloyd ◽  
Aaron Abela ◽  
Reuben A. Farrugia ◽  
Anthony Galea ◽  
Gianluca Valentino
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Super Resolution ◽  
Sea Surface

A hybrid CNN-LSTM based model for the prediction of sea surface temperature using time-series satellite data.

2020 ◽  
Author(s):  
Pavan Kumar Jonnakuti ◽  
Udaya Bhaskar Tata Venkata Sai
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Time Series Data ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Global Ocean ◽  
Series Data

<p>Sea surface temperature (SST) is a key variable of the global ocean, which affects air-sea interaction processes. Forecasts based on statistics and machine learning techniques did not succeed in considering the spatial and temporal relationships of the time series data. Therefore, to achieve precision in SST prediction we propose a deep learning-based model, by which we can produce a more realistic and accurate account of SST ‘behavior’ as it focuses both on space and time. Our hybrid CNN-LSTM model uses multiple processing layers to learn hierarchical representations by implementing 3D and 2D convolution neural networks as a method to better understand the spatial features and additionally we use LSTM to examine the temporal sequence of relations in SST time-series satellite data. Widespread studies, based on the historical satellite datasets spanning from 1980 - present time, in Indian Ocean region shows that our proposed deep learning-based CNN-LSTM model is extremely capable for short and mid-term daily SST prediction accurately exclusive based on the error estimates (obtained from LSTM) of the forecasted data sets.</p><p><strong>Keywords: Deep Learning, Sea Surface Temperature, CNN, LSTM, Prediction.</strong></p><p> </p>

scholarly journals Malvinas-slope water intrusions on the northern Patagonia continental shelf

2009 ◽  
Vol 6 (3) ◽  
pp. 2939-2974 ◽  
Author(s):  
A. R. Piola ◽  
N. M. Avellaneda ◽  
R. A. Guerrero ◽  
F. P. Jardón ◽  
E. D. Palma ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Continental Shelf ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Local Development ◽  
Drake Passage ◽  
Negative Temperature ◽  
Sea Surface ◽  
Western Boundary ◽  
Boundary Current

Abstract. The Patagonia continental shelf located off southeastern South America is bounded offshore by the Malvinas Current, which extends northward from northern Drake Passage (~55° S) to nearly 38° S. The transition between relatively warm-fresh shelf waters and Subantarctic Waters from the western boundary current is characterized by a thermohaline front extending nearly 2500 km. We use satellite derived sea surface temperature, and chlorophyll-a data combined with hydrographic and surface drifter data to document the intrusions of slope waters onto the continental shelf near 41° S. These intrusions create vertically coherent localized negative temperature and positive salinity anomalies extending onshore about 150 km from the shelf break. The region is associated with a center of action of the first mode of non-seasonal sea surface temperature variability and also relatively high chlorophyll-a variability, suggesting that the intrusions are important in promoting the local development of phytoplankton. The generation of slope water penetrations at this location may be triggered by the inshore excursion of the 100 m isobath, which appears to steer the Malvinas Current waters over the outer shelf.

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