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  1. Complex Energy Systems

    07 May 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    This is the opening guest lecture for the Electric Grid Focus Group.  Dr. Michael Chertkov of Los Alamos National Lab describes the statistical and mathematical problems arising in complex energy systems, including the electric grid and gas networks.  Systems with high penetration of...

  2. A Survey of Grid Control and Optimization

    07 May 2015 | Presentations | Contributor(s): Laura Slivinski

    This is an introductory survey of how control problems arise on different time scales in electric grid transmission problems.  The set up and general form of such problems are considered, along with operational considerations.

  3. A Survey of Grid Control and Optimization

    07 May 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    This is an introductory survey of how control problems arise on different time scales in electric grid transmission problems.  The set up and general form of such problems are considered, along with operational considerations.

  4. Low-Order Climate Models

    04 May 2015 | Educational Materials | Contributor(s): Laura Slivinski

    This is a list of 'simple' climate models by Daniel Koll. The papers are meant to be comprehensible & interesting to a general audience as well as relevant to climate researchers.

  5. Complex Phytoplankton Dynamics: The Mathematical Perspective

    04 May 2015 | Educational Materials

    Bibliography by Arjen Doelman and Antonios Zagaris to accompany tutorial lecture on Phytoplankton-Nutrient Modeling at the 2011 MBI Workshop on Ocean Ecologies and their Physical Habitats in a Changing Climate, and the follow-on lectures on Phytoplankton growth in oligotrophic oceans: Linear...

  6. Complex Phytoplankton Dynamics: The Mathematical Perspective

    04 May 2015 | Educational Materials | Contributor(s): Laura Slivinski

    Bibliography by Arjen Doelman and Antonios Zagaris to accompany tutorial lecture on Phytoplankton-Nutrient Modeling at the 2011 MBI Workshop on Ocean Ecologies and their Physical Habitats in a Changing Climate, and the follow-on lectures on Phytoplankton growth in oligotrophic oceans: Linear...

  7. Particle filters for geophysical applications

    30 Apr 2015 | Presentations

    Avoiding degeneracy is a crucial challenge for particle filters. Results have shown that the number of particles scales exponentially with respect to the number of independent observations. In this talk I will review attempts to counteract this phenomenon by exploiting proposal densities. I will...

  8. Particle filters for geophysical applications

    30 Apr 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Avoiding degeneracy is a crucial challenge for particle filters. Results have shown that the number of particles scales exponentially with respect to the number of independent observations. In this talk I will review attempts to counteract this phenomenon by exploiting proposal densities. I will...

  9. Toward a hybrid particle-ensemble Kalman filter for assimilating data from Lagrangian instruments into high dimensional models

    30 Apr 2015 | Presentations

    Presented by Elaine Spiller on 3-26-15Abstract:We discuss a recently proposed hybrid particle-ensemble Kalman filter for assimilating Lagrangian data, and apply it to a high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear,...

  10. Toward a hybrid particle-ensemble Kalman filter for assimilating data from Lagrangian instruments into high dimensional models

    30 Apr 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Presented by Elaine Spiller on 3-26-15Abstract:We discuss a recently proposed hybrid particle-ensemble Kalman filter for assimilating Lagrangian data, and apply it to a high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear,...

  11. Emulators in climate science. Uncertainty, sensitivity, calibration and more

    30 Apr 2015 | Presentations

    An emulator or a surrogate is a statistical approximation of a complex numerical model. Emulators are fast to run and include a measure of their own uncertainty. This makes them suitable for a number of applications in climate science. Emulators were originally devised for uncertainty...

  12. Emulators in climate science. Uncertainty, sensitivity, calibration and more

    30 Apr 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    An emulator or a surrogate is a statistical approximation of a complex numerical model. Emulators are fast to run and include a measure of their own uncertainty. This makes them suitable for a number of applications in climate science. Emulators were originally devised for uncertainty...

  13. Statistical Data Assimilation For Parameter Estimation In Costal Ocean Hydrodynamics Modeling

    30 Apr 2015 | Presentations

    Coastal ocean models are used for a variety of applications, including modeling tides and hurricane storm surge. These models numerically solve the shallow water equations, which are derived by depth integrating the Navier-Stokes equations. The inherent uncertainties in coastal ocean models are a...

  14. Statistical Data Assimilation For Parameter Estimation In Costal Ocean Hydrodynamics Modeling

    30 Apr 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Coastal ocean models are used for a variety of applications, including modeling tides and hurricane storm surge. These models numerically solve the shallow water equations, which are derived by depth integrating the Navier-Stokes equations. The inherent uncertainties in coastal ocean models are a...

  15. Correlated Observation Errors in Data Assimilation

    30 Apr 2015 | Presentations

    Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis...

  16. Correlated Observation Errors in Data Assimilation

    30 Apr 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis...

  17. An Introduction to Lagrangian Data Assimilation - Part II

    23 Mar 2015 | Presentations

    Lauara Slivinski at Woods Hole Oceanographic Institution gives an introduction to Lagrangian Data Assimilation on 11-13-2014.

  18. An Introduction to Lagrangian Data Assimilation - Part II

    23 Mar 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Lauara Slivinski at Woods Hole Oceanographic Institution gives an introduction to Lagrangian Data Assimilation on 11-13-2014.

  19. An Introduction to Lagrangian Data Assimilation - Part I

    23 Mar 2015 | Presentations

    Laura Slivinski at Woods Hole Oceanographic Institute gives an introduction to Lagrangian Data Assimlation on 10-30-2014.

  20. An Introduction to Lagrangian Data Assimilation - Part I

    23 Mar 2015 | Presentations | Contributor(s): Mary Lou Zeeman

    Laura Slivinski at Woods Hole Oceanographic Institute gives an introduction to Lagrangian Data Assimlation on 10-30-2014.