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Events List

  1. JDAS Seminar

    Title: Indistinguishable states I: perfect model scenario Abstract: In this presentation I will discuss the paper by Kevin Judd and Leonard Smith on Indistinguishable states for perfect models (Physica D 151, 125 (2001)). The aim of this paper is to make an accurate forecast of states for nonlinear systems. It has been shown that even in the presence of infinite past observations, uncertainty in the observations make (true) state estimation impossible even for deterministic model. There...

  2. JDAS Seminar

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  3. Test Event

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  4. MCRN Hub Demonstration

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  5. MS63 Applications of Ensemble Data Assimilation Methods to Climate Processes

    1:30 PM - 3:30 PM Room: Wasatch A Data assimilation methods seek to improve estimates from a predictive model by combining them with observed data. Most realistic applications, such as those used in climate modeling, involve an underlying dynamical system which is nonlinear. Many ensemble methods have been designed to handle this nonlinearity, and additionally, often provide an estimate of the uncertainty in the prediction via the ensemble spread. This session will include applications of...

  6. SIAM Dynamical Systems DS15

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  7. Takashi Nishikawa - Realistic modeling and analysis of synchronization dynamics in power-grid networks

    An imperative condition for the functioning of a power-grid network is that its power generators remain synchronized. Disturbances can prompt desynchronization, which is a process that has been involved in large power outages. In this talk I will first give a comparative review of three leading models of synchronization in power-grid networks. Each of these models can be derived from first principles under a common framework and represents a power grid as a complex network of coupled...

  8. Talea Mayo - STATISTICAL DATA ASSIMILATION FOR PARAMETER ESTIMATION IN COASTAL OCEAN HYDRODYNAMICS MODELING

    Abstract: 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 result of many factors, including unknown model parameters. Parameters of particular importance are those used to define the bottom friction of the physical domain. In this work we will...

  9. Talea Mayo - STATISTICAL DATA ASSIMILATION FOR PARAMETER ESTIMATION IN COASTAL OCEAN HYDRODYNAMICS MODELING

    Abstract: 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 result of many factors, including unknown model parameters. Parameters of particular importance are those used to define the bottom friction of the physical domain. In this work we will...

  10. Javier Amezcua - Particle filters for geophysical applications.

    Abstract: 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 put special emphasis to the Equivalent Weight Particle Filter (van Leeuwen, 2010), the Implicit Particle Filter (Chorin and Tu,2010), and culminate with the merging of ideas from these two...

  11. Javier Amezcua - Particle filters for geophysical applications

    Abstract: 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 put special emphasis to the Equivalent Weight Particle Filter (van Leeuwen, 2010), the Implicit Particle Filter (Chorin and Tu,2010), and culminate with the merging of ideas from these two...

  12. Suman Archayya - Synchronization in power grid networks

    This survey discusses Master Stability Function (MSF) analysis for determining stability of synchronization of coupled oscillators on network. In addition, we briefly discuss the derivation of the Swing equation that determine the dynamics of a node in power grid network, and different models of the power grid networks. References: 1. Master Stability Functions for Synchronized Coupled Systems, L. M. Pecora and T. L. Carroll, Phys. Rev. Lett. 80, 2109...

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

    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, low-dimensional Lagrangian instrument variables while applying an ensemble Kalman type update to the high-dimensional Eulerian flow field. We present some initial results from this hybrid filter and compare those to results from a standard...

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

    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, low-dimensional Lagrangian instrument variables while applying an ensemble Kalman type update to the high-dimensional Eulerian flow field. We present some initial results from this hybrid filter and compare those to results from a standard...

  15. Peter Challenor - Emulators in climate science. Uncertainty, sensitivity, calibration and more.

    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 quantification but their application goes far beyond simply estimated the errors on model outputs. I will explain what an emulator is, how it can be calculated and some applications in model prediction,...

  16. Peter Challenor - Emulators in climate science. Uncertainty, sensitivity, calibration and more.

    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 quantification but their application goes far beyond simply estimated the errors on model outputs. I will explain what an emulator is, how it can be calculated and some applications in model prediction,...

  17. Colin Grudzien - A Survey of Grid Control and Optimization

    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.

  18. Sarah Dance: Correlated Observation Errors in Data Assimilation

    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 accuracy. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with...

  19. Sarah Dance - Correlated Observation Errors in Data Assimilation

    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 accuracy. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with...

  20. Juan Durazo - Ionospheric weather forecasting using the LETKF

    We track the three-dimensional global distribution of electron density in the ionosphere using the Local Ensemble Kalman Filter (LETKF) and the Thermosphere-Ionosphere-Electrodynamics Global Circulation Model (TIEGCM) by assimilating globally distributed electron density profiles. The TIEGCM is a three-dimensional non-linear representation of the coupled ionosphere-thermosphere system on a global grid with resolution of 5◦ × 5 from 97 km to 600 km in elevation depending on solar...