A variance constraining Kalman filter for data assimilation

Duration: 50 mins 54 secs

Share this media item:

Embed this media item:

<iframe width="_width_" height="_height_" src="https://sms.cam.ac.uk/media/1076196/embed" frameborder="0" scrolling="no" allowfullscreen></iframe>

Choose size:

About this item

Available Formats

About this item

Description:	Gottwald, G (Sydney) Tuesday 12 October 2010, 10:00-11:00

Created:

2010-10-18 15:16

Collection:

Mathematical and Statistical Approaches to Climate Modelling and Prediction

Publisher:

Isaac Newton Institute

Gottwald, G

Language:

eng (English)

Credits:

Author:	Gottwald, G
Producer:	Steve Greenham


Abstract:	Data assimilation aims to solve one of the fundamental problems of numerical weather prediction - estimating the optimal state of the atmosphere given a numerical model of the dynamics, and sparse, noisy observations of the system. A standard tool in attacking this filtering problem is the Kalman filter. We consider the problem when only partial observations are available. In particular we consider the situation where the observational space consists of variables which are directly observable with known observational error, and of variables of which only their climatic variance and mean are given. We derive the corresponding Kalman filter in a variational setting. We analyze the variance constraining Kalman filter (VCKF) filter for a simple linear toy model and determine its range of optimal performance. We explore the variance constraining Kalman filter in an ensemble transform setting for the Lorenz-96 system, and show that incorporating the information on the variance on some un-observable variables can improve the skill and also increase the stability of the data assimilation procedure. Using methods from dynamical systems theory we then investigate systems where the un-observed variables evolve deterministically (but chaotically) on a fast time scale. This is joint work with Lewis Mitchell and Sebastian Reich.

Abstract:

Data assimilation aims to solve one of the fundamental problems of numerical weather prediction - estimating the optimal state of the atmosphere given a numerical model of the dynamics, and sparse, noisy observations of the system. A standard tool in attacking this filtering problem is the Kalman filter.
We consider the problem when only partial observations are available. In particular we consider the situation where the observational space consists of variables which are directly observable with known observational error, and of variables of which only their climatic variance and mean are given. We derive the corresponding Kalman filter in a variational setting.

We analyze the variance constraining Kalman filter (VCKF) filter for a simple linear toy model and determine its range of optimal performance. We explore the variance constraining Kalman filter in an ensemble transform setting for the Lorenz-96 system, and show that incorporating the information on the variance on some un-observable variables can improve the skill and also increase the stability of the data assimilation procedure.

Using methods from dynamical systems theory we then investigate systems where the un-observed variables evolve deterministically (but chaotically) on a fast time scale.

This is joint work with Lewis Mitchell and Sebastian Reich.

Available Formats

Format	Quality	Bitrate	Size
MPEG-4 Video	640x360	1.84 Mbits/sec	705.98 MB	View	Download
WebM	480x360	690.07 kbits/sec	256.92 MB	View	Download
Flash Video	484x272	568.71 kbits/sec	212.02 MB	View	Download
iPod Video	480x270	506.24 kbits/sec	188.73 MB	View	Download
MP3	44100 Hz	125.01 kbits/sec	46.41 MB	Listen	Download
Auto *	(Allows browser to choose a format it supports)