Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes

A compilation of digitized satellite imagery of the Gulf Stream (1982, 1983, and 1985)

Ninety plots of digitized temperature boundaries from infared satellite images of the Gulf Stream along with corresponding image snapshots were compiled to determine stream width propagation speed. The satellite images are from the years 1982, 1983, and 1985 and are often of consecutive days. In this report, these images and digitized plots are presented.Funding was provided by the Office of Naval Research through contract Number N00014-87-K-0007, and by the National Science Foundation under grant Numbers OCE 87-00601 and OCE 85-10828

Deep Learning of Sea Surface Temperature Patterns to Identify Ocean Extremes

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)

Mitigating masked pixels in climate-critical datasets

Remote sensing observations of the Earth's surface are frequently stymied by clouds, water vapour, and aerosols in our atmosphere. These degrade or preclude the measurementof quantities critical to scientific and, hence, societal applications. In this study, we train a natural language processing (NLP) algorithm with high-fidelity ocean simulations in order to accurately reconstruct masked or missing data in sea surface temperature (SST)--i.e. one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the Enki model repeatedly outperforms previously adopted inpainting techniques by up to an order-of-magnitude in reconstruction error, while displaying high performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masking fractions of at least 40% show reconstruction errors of less than the known sensor uncertainty (RMSE < ~0.1K). We attribute Enki's success to the attentive nature of NLP combined with realistic SST model outputs, an approach that may be extended to other remote sensing variables. This study demonstrates that systems built upon Enki--or other advanced systems like it--may therefore yield the optimal solution to accurate estimates of otherwise missing or masked parameters in climate-critical datasets sampling a rapidly changing Earth.Comment: 21 pages, 6 main figure, 3 in Appendix; submitte

Some observations of the Azores Current and the North Equatorial Current

The regions containing the two zonal currents of the subtropical gyre in the eastern North Atlantic, the Azores Current and the North Equatorial Current (NEC), have quite different physical characteristics. Associated with the Azores Current are strong horizontal thermohaline gradients that can be located easily both at the surface and at depth with temperature data alone, thus making satellite IR imagery and expendable bathythermograph profiles suitable for observing it. During winter, the surface expression of the Azores Current is often found to the north of the strongest subsurface gradients. In contrast to the Azores Current and to the central water mass boundary just to the south, the NEC has relatively weak horizontal temperature and salinity gradients, requiring density information in order to identify it. There is no clear surface manifestation found with the NEC. Common to both currents, though, is that each transports O(8 Sv) in the upper 800 m of the ocean near 27°W, with the largest velocities being in the upper 400 m

Mitigating masked pixels in a climate-critical ocean dataset

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth

Chlorophyll enchancement and mixing associated with meanders of the shelf break front in the Mid-Atlantic Bight

Meanders of the shelf break front in the Mid-Atlantic Bight (MAB) during April and May of 1997 were associated with chlorophyll enhancement along a hydrographic and a topographic feature. The hydrographic feature was the surface outcrop of the front, which ranged from ~10 to >100 km seaward of the shelf break owing to the meanders. The topographic feature was the shelf break (100-m isobath). Chlorophyll enhancement was observed by a satellite instrument, the ocean color and temperature sensor, and by a fluorometer in situ. It developed in near-surface waters typically nutrient depleted during late spring, thus local nutrient enrichment of near-surface waters was probable. Observations of sufficient resolution to define processes were available only for the region of shelf break chlorophyll enhancement. Along two meander troughs( shoreward extremities near the shelf break), we observed shoaling of cold shelf water. Shelf water shoaled >20 m along frontal-isopycnals, and phytoplankton absorption maxima coincided directly with the shoaled water. Thus local nutrient enrichment by along-isopycnal upwelling was the supported mechanism of chlorophyll enhancement at the shelf break. The basis for along-isopycnal upwelling was seaward flow of shelf water forced by meander circulation near the shelf break. Strong cross-isobath flow and mixing developed as these meanders propagated along the shelf break front of the MAB at a relatively constant rate of ~9 km day¯¹

Allogenic hematopoietic stem cell transplantation after reduced intensity conditioning regimen for elderly patients (60 years and older) with hematologic malignancies using unrelated donors: a retrospective study for the French society for stem cell transplantation (SFGM-TC)

Peer reviewe

Properties of small molecular drug loading and diffusion in a fluorinated PEG hydrogel studied by ^1H molecular diffusion NMR and ^(19)F spin diffusion NMR

R_f-PEG (fluoroalkyl double-ended poly(ethylene glycol)) hydrogel is potentially useful as a drug delivery depot due to its advanced properties of sol–gel two-phase coexistence and low surface erosion. In this study, ^1H molecular diffusion nuclear magnetic resonance (NMR) and ^(19)F spin diffusion NMR were used to probe the drug loading and diffusion properties of the R_f-PEG hydrogel for small anticancer drugs, 5-fluorouracil (FU) and its hydrophobic analog, 1,3-dimethyl-5-fluorouracil (DMFU). It was found that FU has a larger apparent diffusion coefficient than that of DMFU, and the diffusion of the latter was more hindered. The result of ^(19)F spin diffusion NMR for the corresponding freeze-dried samples indicates that a larger portion of DMFU resided in the R_f core/IPDU intermediate-layer region (where IPDU refers to isophorone diurethane, as a linker to interconnect the R_f group and the PEG chain) than that of FU while the opposite is true in the PEG–water phase. To understand the experimental data, a diffusion model was proposed to include: (1) hindered diffusion of the drug molecules in the R_f core/IPDU-intermediate-layer region; (2) relatively free diffusion of the drug molecules in the PEG-water phase (or region); and (3) diffusive exchange of the probe molecules between the above two regions. This study also shows that molecular diffusion NMR combined with spin diffusion NMR is useful in studying the drug loading and diffusion properties in hydrogels for the purpose of drug delivery applications