2019 - T3 - WS2 - Big data, data assimilation, and uncertainty quantification

Collection 2019 - T3 - WS2 - Big data, data assimilation, and uncertainty quantification

Organizer(s) Carrassi, Alberto ; Bocquet, Marc ; Crisan, Dan ; Ghattas, Omar ; Ghil, Michael ; Gotwald, Georg ; Vannitsem, Stephane

Date(s) 12/11/2019 - 15/11/2019

linked URL https://climath19ws2.sciencesconf.org

00:00:00 / 00:00:00

23 39

Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

By Marc Bocquet

Information about the video

Date of recording 14/11/2019
Date of publication 19/12/2019
Institution IHP
Licence CC BY-NC-ND
Language English
Format MP4
Venue Institut Henri Poincaré

Domain(s)

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All the collection videos

35:27

published on December 18, 2019

Data Assimilation: Interesting Past, Bright Future

By Michael Ghil

32:39

published on December 18, 2019

Scalable optimal experimental design for Bayesian inverse problems

By Omar Ghattas

33:27

published on December 18, 2019

Shadowing Approach for Data Assimilation

By Svetlana Dubinkina

27:39

published on December 18, 2019

Enhancing the skill of dynamical climate prediction

By François Counillon

22:48

published on December 18, 2019

Account for Model Discrepancy via Multi-model Cross Pollination in Time

By Hailiang Du

33:08

published on December 18, 2019

Data Assimilation in Global NWP: A case study in Big Data and Uncertainty Quantification

By Massimo Bonavita

32:56

published on December 18, 2019

Big Data Assimilation: 30-second-update Weather Forecasting and Perspectives toward DAAI Integration

By Takemasa Miyoshi

28:55

published on December 18, 2019

Is Weather Chaotic? Coexistence of Chaos and Order within a Generalized Lorenz Model

By Bo-Wen Shen

31:12

published on December 18, 2019

Inferring physical parameters in turbulence: from nudging to machine learning

By Luca Biferale

24:57

published on December 18, 2019

Big Data simulation and analysis of numerical solutions of the Elder problem

By Roman Khotyachuk

32:25

published on December 18, 2019

Data assimilation using particle filters for class of partially observed stochastic geophysical fluid dynamics models. Part I

By Dan Crisan

33:13

published on December 18, 2019

Data assimilation using particle filters for class of partially observed stochastic geophysical fluid dynamics models. Part II

By Colin Cotter

30:32

published on December 18, 2019

Stochastic modelling of geophysical flows

By Étienne Mémin

32:47

published on December 18, 2019

Frequency-ranked data-driven stochastic modelling, and applications

By Mickaël Chekroun

30:41

published on December 18, 2019

On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

By Colin Grudzien

33:49

published on December 18, 2019

Neural networks and dynamical systems: dealing with partially observed

By Ronan Fablet

31:26

published on December 18, 2019

Gaussian approximations in smoothers and filters for data assimilation

By Matthias Morzfeld

25:25

published on December 18, 2019

Joint Online Parameter Estimation and Optimal Sensor Placement in Continuous Time with Applications to a Stochastic Advection Diffusion Equation

By Louis Sharrock

27:55

published on December 18, 2019

On the efficiency and Consistency of covariance localisation in the ensemble Kalman filter

By Alban Farchi

26:25

published on December 18, 2019

Efficient algorythms to train supermodels

By Francine Schevenhoven

31:37

published on December 18, 2019

Design of the parametric Kalman filter dynamics: from the symbolic computation to the numerical integration

By Olivier Pannekoucke

33:23

published on December 19, 2019

Targeting simulation with Bayesian design in a subgrid closure model

By Oliver Dunbar

31:04

published on December 19, 2019

Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

By Marc Bocquet

36:44

published on December 19, 2019

Questions on the Table: Stating Plainly Our Aims in Light of Our Acknowledged Ignorance and Training

By Leonard Smith

34:50

published on December 19, 2019

Model error covariance estimation using the batch and online Expectation-Maximization algorithms in ensemble Kalman and particle filters

By Manuel Pulido

32:06

published on December 19, 2019

Machine Learning for Forecasting and Data Assimilation

By Brian Hunt

27:29

published on December 19, 2019

Data Assimilation on Adaptive Meshes

By Christian Sampson

26:58

published on December 19, 2019

Sensitivity with respect to observations in Variational Data Assimilation

By François-Xavier Le Dimet

29:52

published on December 19, 2019

Rigorous analysis and numerical implementation of a data assimilation algorithm

By Edris Titi

30:28

published on December 19, 2019

Nonlinear data assimilation for high-dimensional systems using machine learning methods

By Peter - Jan van Leeuwen

26:30

published on December 19, 2019

Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations

By Jana de Wiljes

25:24

published on December 19, 2019

Strongly coupled data assimilation with a toy model

By Avneet Singh

33:43

published on December 19, 2019

What is the right functional for weakly constrained variational assimilation?

By Jochen Broecker

33:19

published on December 19, 2019

On temporal scale separation in coupled data assimilation with the ensemble Kalman filter

By Alberto Carrassi

30:45

published on December 19, 2019

Routes to long-term predictability in a reduced-order coupled ocean-atmosphere model

By Stéphane Vannitsem

29:55

published on December 19, 2019

Assimilation of observations of the solar magnetic cycle

By Olivier Talagrand

25:36

published on December 19, 2019

Stability of the optimal nonlinear filter

By Lea Oljaca

34:48

published on December 19, 2019

Coupled data assimilation-challenges and practicalities

By Phil Browne

29:44

published on December 19, 2019

Reconstruction and estimation in data driven state-space models

By Valérie Monbet

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