# Seminar

## Ensemble filtering with displacement errors

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.

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

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

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

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

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).

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

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

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

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.

## Explore dynamical information with Pseudo-orbit Data Assimilation

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.

## Korea Satellite Remote Sensing of the Arctic

Dr. Hyun-Cheol Kim is the director of Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute (KOPRI).

As a remote sensing scientist, he will give us a talk regarding the research activities of KOPRI.

After the seminar, NERSC and KOPRI will sign a MoU.

## The challenge of bounded, non-Gaussian, non-linear and multi-scale variables

Current state estimation or data assimilation techniques assume Gaussian uncertainties for both forecasts and observations. However, unbiased observations of bounded variables can be shown to have highly non-Gaussian uncertainties and observation error standard deviations that depend on the value of the unknown true state. In particular, the observation error variance of such observations must tend to zero as the unknown true state tends to zero.

## Impact of sea ice sources on calibrating a wave-ice interaction model

Because of the interaction between ocean wave and sea ice, reliable models for wave propagation in the ice-covered region is critical to sea ice morphology. We present calibration of a viscoelastic type wave-in-ice model with wave, wind, and ice data collected from the Beaufort and Chukchi seas in the autumn of 2015. The data were from multiple sources of in-situ and remote sensing measurements in the marginal ice zone during the ice advance season.