scholarly journals Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction

2021 ◽  
Vol 14 ◽  
pp. 887-896
Author(s):  
Bo Ping ◽  
Fenzhen Su ◽  
Xingxing Han ◽  
Yunshan Meng
Keyword(s):  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Super Resolution ◽  
Temperature Reconstruction ◽  
Sea Surface

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

scholarly journals Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?

2021 ◽  
Vol 13 (18) ◽  
pp. 3568
Author(s):  
Bo Ping ◽  
Yunshan Meng ◽  
Cunjin Xue ◽  
Fenzhen Su
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Image Quality ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Super Resolution ◽  
Sea Surface ◽  
Training Dataset ◽  
Low Resolution ◽  
Structure Similarity

Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.

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 Tropical sea-surface temperature reconstruction for the early Paleogene using Mg/Ca ratios of planktonic foraminifera

2003 ◽  
Vol 18 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Aradhna K. Tripati ◽  
Margaret L. Delaney ◽  
James C. Zachos ◽  
Linda D. Anderson ◽  
Daniel C. Kelly ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Planktonic Foraminifera ◽  
Temperature Reconstruction ◽  
Sea Surface ◽  
Tropical Sea ◽  
Early Paleogene

scholarly journals Mid-Holocene sea-surface temperature reconstruction using fossil corals from Kume Island, Ryukyu, Japan

2012 ◽  
Vol 46 (3) ◽  
pp. e27-e32 ◽  
Author(s):  
ARISA SEKI ◽  
YUSUKE YOKOYAMA ◽  
ATSUSHI SUZUKI ◽  
YUTA KAWAKUBO ◽  
TAKASHI OKAI ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Reconstruction ◽  
Sea Surface ◽  
Fossil Corals

The Ocean Temperature Reconstruction of Center Tropical Cyclone Based on Argo

2013 ◽  
Vol 864-867 ◽  
pp. 2171-2174
Author(s):  
Jiong Zhu ◽  
Jian Cheng Kang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Interpolation Method ◽  
Temperature Reconstruction ◽  
Sea Surface ◽  
Weighted Interpolation ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Inverse Distance ◽  
Upper Boundary

There is a correlation between sea surface temperature of the upper boundary waters and the intensity of typhoon. This paper analyzes the use of Argo float data and using inverse distance weighted interpolation method to calculate its internal regional sea surface temperature, when typhoon and other data were compared and error analysis. The results showed that: the method is reliable. When you select a point closer distance calculation and spatial distribution of Argo floats as closely as possible, the weight coefficients taken 2, known buoy number is 4-6, the relative error of calculated is less than 0.4%, RMSE error is less than 1.2 in the 0-600m depth layer

Mid-Pliocene equatorial Pacific sea surface temperature reconstruction: a multi-proxy perspective

2008 ◽  
Vol 367 (1886) ◽  
pp. 109-125 ◽  
Author(s):  
Harry J Dowsett ◽  
Marci M Robinson
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Reconstruction ◽  
Warm Pool ◽  
Equatorial Pacific ◽  
Sea Surface ◽  
Mean Annual Temperature ◽  
Meridional Heat Transport ◽  
Eastern Equatorial Pacific ◽  
Past Climate Change

The Mid-Pliocene is the most recent interval of sustained global warmth, which can be used to examine conditions predicted for the near future. An accurate spatial representation of the low-latitude Mid-Pliocene Pacific surface ocean is necessary to understand past climate change in the light of forecasts of future change. Mid-Pliocene sea surface temperature (SST) anomalies show a strong contrast between the western equatorial Pacific (WEP) and eastern equatorial Pacific (EEP) regardless of proxy (faunal, alkenone and Mg/Ca). All WEP sites show small differences from modern mean annual temperature, but all EEP sites show significant positive deviation from present-day temperatures by as much as 4.4°C. Our reconstruction reflects SSTs similar to modern in the WEP, warmer than modern in the EEP and eastward extension of the WEP warm pool. The east–west equatorial Pacific SST gradient is decreased, but the pole to equator gradient does not change appreciably. We find it improbable that increased greenhouse gases (GHG) alone would cause such a heterogeneous warming and more likely that the cause of Mid-Pliocene warmth is a combination of several forcings including both increased meridional heat transport and increased GHG.

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