Tags: data assimilation

All Categories (21-40 of 54)

  1. Alberto Carrassi


  2. Trevor Joseph Gionet


  3. Michail Vrettas


  4. Jul 20 2015

    4th Summer School on Data Assimilation and its applications: Oceanography, Hydrology, Risk & Safety and Reservoir Engineering

    The goal is to get together experts in the field of data assimilation from different schools (statistics, system and control, pure mathematics, engineering, ... ) and to make use of their knowledge...


  5. Jul 06 2015

    CliMathNet Conference 2015

    The conference is based on the theme of numerical weather prediction and data assimilation, and the sessions of the conference will follow the life cycle of a weather forecast.Website:...


  6. Jun 08 2015

    10th International EnKF Workshop

    The ensemble Kalman filter (EnKF) and its many variants have been proven effective for data assimilation in large models, including those in atmospheric, oceanic, hydrologic, and petroleum...


  7. Juan Durazo

    I'm a fourth yeah Phd student studying  data assimilation. My most recent work involve applying an ensemble Kalman filter method(LETKF) to global high order models. Current work involves...


  8. Andy Reagan

    PhD Student at the Unversity of Vermont, studying dynamical systems and happiness.


  9. Midwest Mathematics and Climate Conference - Day 2 Morning Session

    12 May 2015 |

    Juan Restrepo, Oregon State UniversityData AssimilationAccounting for uncertainties has led us to alter our expectations of what is predictable and how such predictions compare to nature. A significant effort, in recent years, has been placed on creating new uncertainty quantification techniques,...

  10. Midwest Mathematics and Climate Conference

    12 May 2015 |

    The conference is sponsored by the National Science Foundation, Institute of Mathematics and Its Applications, the Office of Research,College of Liberal Arts and Sciences, and the Department of Mathematics, Department of Geography/Atmospheric Science Program, and The...

  11. Midwest Mathematics and Climate Conference - Day 1 Afternoon Session

    12 May 2015 |

    Graham Feingold, National Oceanic & Atmospheric AdministratonDynamical System Analogues to Cloud SystemsShallow convection exhibits fascinating cellular structures at scales of a few to several hundred kilometers. Configurations of relatively cloud free open cell states, or much cloudier...

  12. IMA Hot Topics Workshop: Predictability in Earth System Processes

    09 May 2015 |

    A major question facing climate modeling is how best to incorporate data into models. As climate models increase in complexity, their results become correspondingly intricate. Such models represent climate processes spanning multiple spatial and temporal scales and must relate disparate physical...

  13. Realistic modeling and analysis of synchronization dynamics in power-grid networks

    08 May 2015 | | Contributor(s):: Takashi Nishikawa

    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...

  14. Complex Energy Systems

    07 May 2015 | | Contributor(s):: Michael Chertkov

    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...

  15. Complex Phytoplankton Dynamics: The Mathematical Perspective

    04 May 2015 | | Contributor(s):: Arjen Doelman, Antonios Zagaris

    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...

  16. Particle filters for geophysical applications

    30 Apr 2015 | | Contributor(s):: Javier Amezcua

    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...

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

    30 Apr 2015 | | Contributor(s):: Elaine Spiller

    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,...

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

    30 Apr 2015 | | Contributor(s):: Peter Challenor

    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...

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

    30 Apr 2015 | | Contributor(s):: Talea Mayo

    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...

  20. Correlated Observation Errors in Data Assimilation

    30 Apr 2015 | | Contributor(s):: Sarah Dance

    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...