Ensemble filtering with displacement errors

Speaker: 
Michael Ying

Affiliation: 
NCAR, Boulder, Colorado

Seminar Date: 
12. February 2020 - 13:00 - 14:00

An outstanding issue for multiscale weather prediction is the choice of data assimilation methods. Since small scale features rapid error growth that gives rise to nonlinearity, data assimilation methods based on linearization, such as the ensemble Kalman filter (EnKF), performs suboptimally. Position error of convective clouds among ensemble members is one of the common causes of nonlinearity and has been a major challenge for data assimilation. Previous studies have made some progress in developing nonlinear optimization methods to reduce position errors.

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The Angola-Benguela Upwelling system: interannual and decadal variability (two talks)

Speaker: 
Marie-Lou Bachelery and Folly Serge Tomety

Affiliation: 
University of Cape Town

Seminar Date: 
26. November 2019 - 13:00 - 14:00

Title 1: Impacts and characteristics of the interannual Coastal Trapped Waves in the Angola-Benguela Upwelling System

Marie-Lou Bachelery, Serena Illig, and Mathieu Rouault

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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.

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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).

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