Previous Seminars

Chasing Water: How ocean currents transport plastic and plankton around the globe

Speaker: 
Erik van Sebille
Affiliation: 
Institute for Marine and Atmospheric Research, Utrecht University, Netherlands
Seminar Date: 
21. October 2019 - 11:15

The ocean is in constant motion, with water circulating within and flowing between basins. As the water moves around, it caries heat and nutrients, as well as planktonic organisms and plastic litter around the globe.

The most natural way to study the pathways of water and the connections between ocean basins is using particle trajectories. The trajectories can come from computing of virtual floats in high-resolution ocean models.

Sparse Representation based on Dictionary Learning

Speaker: 
Ricardo Soares
Affiliation: 
NORCE Energy
Seminar Date: 
16. October 2019 - 11:15 - 11:45

This work presents the use of the Dictionary Learning method for a sparse representation of 4D seismic data. We consider a trade-off between the number of nonzero coefficients retained in the sparse data representation, the computational cost, and how well we can capture the main features of the original 4D seismic signal. K-SVD is an iterative algorithm used in Dictionary Learning that alternates between the calculation of the sparse representation vector and dictionary update. The algorithm starts with the definition of an initial dictionary (Discrete Cosine Transform, for instance).

Fresh updates from the Polar cruise to the North Pole

Speaker: 
Espen Storheim (Polar Acoustics and Oceanography)
Seminar Date: 
27. September 2019 - 10:15 - 10:45

Uncertainty of the Arctic Sea Surface Salinity: from evaluation to assimilation

Speaker: 
Jiping Xie (Ocean Modelling)
Seminar Date: 
10. September 2019 - 11:15 - 11:45

Monitoring the Arctic Sea Surface Salinity (SSS) from space is crucial to understand the global water cycle and the ocean dynamics is limited due to nonhomogeneous and sparse in situ data. Two gridded satellite SSS products have been derived from European Space Agency’s Soil Moisture and Ocean Salinity mission (SMOS). The uncertainties of these two SSS products in the Arctic are quantified against other two SSS products in the Copernicus Marine Environment Monitoring Services and other in situ datasets.

Physical and biogeochemical variability off Baja California (Mexico): insights from numerical NPZD ocean models

Speaker: 
David Rivas
Affiliation: 
CICESE
Seminar Date: 
30. August 2019 - 13:15 - 14:00

Physical-biogeochemical Nitrate-Phytoplankton-Zooplankton-Detritus (NPZD)
numerical models are used to study the variability of nutrients and
phytoplankton biomass in coastal waters off Baja California Peninsula, a
region of high socioeconomic importance located in the southern California
Current System. The focus of these analyses has been the effects of
interannual climatic anomalies. For example, the year 2006 was anomalously
warm and with low chlorophyll (Chl) levels, associated with warm phases of
El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation

Classification of sea ice types on Sentinel-1 SAR data using convolutional neural networks

Speaker: 
Hugo Bolze
Affiliation: 
ENSG, France
Seminar Date: 
23. August 2019 - 13:00 - 13:45

In order to assure the security of navigation and offshore activities scientists have to predict with precision the type and the location of sea ices. Getting information about cold intensity and ocean-atmosphere dynamics of these polar regions are also motivations. The ways to describe sea ices are numerous but this article focuses only on type.

Lagrangian ocean analysis to study the physical mechanisms driving the processes occurring in the Greater Agulhas Current System

Speaker: 
Michael Hart-Davis
Seminar Date: 
13. August 2019 - 11:15 - 11:45

Lagrangian ocean analysis is a powerful way to study ocean processes from in-situ observations and numerical model simulations. As numerical modelling capabilities develop and physical mechanisms of the ocean are better understood, the importance of particle trajectory modelling continues to increase. Therefore, developing cross-disciplinary particle trajectory model applications for the Greater Agulhas System is highly relevant due to its potential contribution to scientific studies and operational applications.

Cloud Computing Needs for Earth Observation Data Analysis: EGI and the European Open Science Cloud

Speaker: 
Bjørn Backeberg
Affiliation: 
Björn Backeberg (1), Yin Chen (1), *Tiziana Ferrari (1), Pedro Gonçalves (4), Paolo Mazzetti (3), Anabela Oliveira (2), Diego Scardaci (1), Gergely Sipos (1) 1. EGI Foundation, 2. LNEC, 3. CNR, 4. Terradue
Seminar Date: 
27. June 2019 - 11:15 - 12:00

Over recent years, the vision of Open Science has emerged as a new paradigm of transparent, data-driven science capable of accelerating competitiveness and innovation. The embodiment of this vision in Europe is the European Open Science Cloud (EOSC), first proposed by the European Commission in April 2016 as part of the Communication on the ‘European Cloud Initiative’, one of the pillars of the Digital Single Market Strategy.

Data assimilation using adaptive, non-conservative, moving mesh models

Speaker: 
Ali Aydoğdu (Data Assimilation)
Seminar Date: 
25. June 2019 - 11:15 - 11:45

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver.

Explore dynamical information with Pseudo-orbit Data Assimilation

Speaker: 
HAILIANG DU
Affiliation: 
Durham University
Seminar Date: 
13. June 2019 - 11:00 - 12:00

Physical processes such as the weather are usually modeled using nonlinear dynamical systems. Traditional statistical approaches are found to be difficult to draw dynamical information from the nonlinear dynamics. This talk is focusing on exploring dynamical information with Pseduo-orbit data assimilation to address various problems encountered in analyzing and modeling nonlinear dynamical systems. The talk will start with solving an “impossible” challenge pointed out by Berliner (1991) when applying the Bayesian paradigm to state estimation in chaotic systems.