Using Ensemble KalmanFiltering to improve magnetic field models during vector satellite data ‘gaps’?

Whaler, Kathy; Beggan, Ciaran. 2017 Using Ensemble KalmanFiltering to improve magnetic field models during vector satellite data ‘gaps’? [Poster] In: IAGA 13th Scientific Assembly, Cape Town, Cape Town, South Africa, 27 Aug - 1 Sept 2017. (Unpublished)

Before downloading, please read NORA policies.

[thumbnail of Beggan_Whaler_EnKF_IAGA2017_final.pdf]

Preview

Text
Beggan_Whaler_EnKF_IAGA2017_final.pdf
Download (962kB) | Preview

Official URL: http://www.iapso-iamas-iaga2017.com/index.php

Abstract/Summary

Kalmanfiltering can be used to combine data optimally from different sources assuming that the error or variance of each data type is suitably understood. Typically a physical model is combined with occasional real measurements. Ensemble KalmanFilters (EnKF) extend this idea by making multiple simulations with randomly perturbed models drawn from probability distribution of fixed variance. Here we use EnKFto combine steady core surface flow models of the fluid outer core with magnetic field models derived from periods when no vector satellite data were available. We test if there is an optimal combination of flow and field that minimises the overall root-mean-square misfit to a ‘true’ magnetic field calculated after the resumption of satellite vector measurements.

Item Type:	Publication - Conference Item (Poster)
NORA Subject Terms:	Earth Sciences
Date made live:	05 Sep 2017 08:24 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517688

Item Type:

Publication - Conference Item (Poster)

NORA Subject Terms:

Earth Sciences

Date made live:

05 Sep 2017 08:24 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517688