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source: International Centre for Theoretical Sciences 2013年5月28日

PROGRAM: Data Assimilation Research Program

Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science

Dates: 04 - 23 July, 2011

DESCRIPTION:

Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical model to generate state estimates. These techniques are essential for numerical weather and climate predictions, but aplications of DA to many other scientific and engineering disciplines are emerging rapidly. DA is inherently interdisciplinary in nature and requires close collaborations and interactions between researchers in atmospheric sciences, in nonlinear dynamics and complex systems, and in applied mathematics and statistics.

The Data Assimilation Research Program (DARP) is primarily aimed at developing an interest within the academic and scientific community in pure and applied research related to DA, in order to form a group of researchers, developers, and users of data assimilation systems.

The initial set of activities will consist of a "Monsoon School on Mathematical and Statistical Foundations of Data Assimilation" along with an "International Conference on Data Assimilation" at the TIFR Centre for Applicable Mathematics and the Indian Institute of Science, Bangalore.

The school (04-12 July and 18-23 July) will consist of compact courses, tutorials, and hands-on laboratories on these topics during the first half. The participants will conduct short projects during the second part of the school. The expected participants are students and young researchers, as well as scientists at organizations which are interested in operational DA. The list of topics to be covered in the school is as follows.

1. Introduction -- the need for data assimilation

2. Mathematical and statistical methods for assimilation a. nudging, optimal interpolation b. variational methods c. Kalman filtering and related methods d. Statistical sampling techniques

3. Basic introduction to nonlinear dynamics and data assimilation for nonlinear systems

4. Applications to atmospheric, oceanic problems (including basic introduction to modelling)

ICTS Program

Welcome 6:53

ICTS - A new initiative in Indian science - Prof.Wadia 13:05

Prediction of the Indian Monsoon -Gadgil 40:50

Introduce speaker Christopher Jones - Amit Apte 2:06

Overview of Approaches to Data Assimilation - Christopher Jones 1:22:51

Basic setup of tutorials and matlab - Amit Apte 41:08

Introduction to inverse problems - Lakshmivarahan 1:59:14

Introduction to inverse problems - Lakshmivarahan 1:31:05

Tutorial on SVD etc - Lakshmivarahan and Amit 43:15

Participants Questions 1 6:14

Participants Questions 2 20:14

Participants Questions 3 9:30

Participants Questions 4 4:42

Dynamical systems and uncertainty - Eric Kostelich 1:22:20

3D-var and iterative techniques for nonlinear problems - Lakshmivarahan 1:38:16

Tutorial - Random variables and sampling - Elaine Spiller 45:12

Tutorial 6:46

Introduction to Dynamical Models and Theory Behind Seasonal Forecasting - David Dewitt 1:19:28

The local ensemble transform Kalman filter - Eric Kostelich 1:32:37

Brief Introduction to Probability and Simulation: -Elaine Spiller 1:17:25

Ensemble (Transform) Kalman Filter - Amit Apte 46:31

Seasonal Forecasting- David Dewitt 1:12:50

Lakshmivarahan 1:12:43

Nudging methods in geophysical data assimilation: (Part 1) - Didier Auroux 1:33:43

Inverse problems: satellite observations; interpolation - Lakshmivarahan 1:19:18

Participants Questions 22:04

Nudging methods in geophysical data assimilation:(Part 2) -Didier Auroux 1:18:28

Tutorial - Monte Carlo and Importance Sampling - Elaine Spiller 41:29

The Geometry of Data Assimilation in Maths, Physics, Forecasting and Decision Support - Lenny Smith 1:26:07

Nudging methods in geophysical data assimilation: (Part 3) - Didier Auroux 1:24:39

Participants Questions 8:07

Nudging methods in geophysical data assimilation: Hands-on lab - Didier Auroux 24:34

Brief Introduction to Probability and Simulation: Part 3 - Elaine Spiller 1:02:22

welcome talk 5:00

The Geometry of Data Assimilation 2 : Lenny Smith 1:03:41

Kalman filtering - Lakshmivarahan 1:23:09

Tutorial 16:34

Gradient descent - Emma Suckling 42:05

Projects Discussions 7:02

Welcome 1:13

Diffusive Back and Forth Nudging algorithm - Didier Auroux 34:04

Some observations on retrieval of geophysical parameters - D Jagadheesha 24:07

Testing the manifold hypothesis - Hariharan Narayanan 29:28

An Application of Extended Kalman Filter for Spacecraft Orbit Estimation - Akila S 22:02

School Participants Presentations 13:19

School Participants Presentations 16:32

Duality between estimation and control - Sanjoy Mitter 45:31

Kalman Filter Design By Tuning Its Statistics Or Gains? -Ananthasayanam Mudambi 33:56

School Participants Presentations 12:41

School Participants Presentations 8:28

School Participants Presentations 9:21

School Participants Presentations 9:56

School Participants Presentations 6:35

School Participants Presentations 1:42

Forward Sensitivity Approach to dynamic data assimilation - S. Lakshmivarahan 25:22

Towards a multi-satellite radiance assimilation in regional models - Chakravarthy Balaji 38:33

Particle filter for glider data assimilation - Elaine Spiller 29:18

Gradient descent for the point vortex model - Emma Suckling 27:52

Models of Fluid-Structure Interaction and Exact Controllability - M. Vanninathan 31:17

Indian ocean modelling: opportunities and challenges for data assimilation- P.N. Vinaychandran 27:54

Data assimilation of wind field at Kalpakkam - A S Vasudeva Murthy 16:17

Atmospheric parameters from Indian Geostationary Satellites - Pradeep Kumar Thapliyal 43:11

Impact studies with Data Assimilation using Nudging and 3DVAR - C.V. Srinivas 35:01

Assimilation of Lagrangian data - Chris Jones 44:36

3D-VAR assimilation studies over the Indian ocean region: A. Chandrasekar 22:29

Model error and data assimilation - Lenny Smith 38:18

Nonlinear Stochastic Modelling, Critical Phenomena and Entropy - K. Karmeshu 34:01

Optimal control problem for Burgers’ equation - Mythily Ramaswamy 24:13

Closing 13:52

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