Magnetic Field Forecasting using Virtual Observatories, Core Flow Modelling and Ensemble Kalman Filtering

Beggan, Ciaran; Whaler, Kathy; Macmillan, Susan. 2009 Magnetic Field Forecasting using Virtual Observatories, Core Flow Modelling and Ensemble Kalman Filtering. [Poster] In: SWARM Meeting, RAS, London, 9 October 2009. (Unpublished)

Before downloading, please read NORA policies.

Preview

Text Beggan_etal_SWARM_Oct2009.pdf Download (2MB) | Preview

Abstract/Summary

In the last decade, accurate and detailed models of the Earth’s magnetic field have been generated from the dedicated satellite missions of CHAMP, Oersted and SAC-C. Models and forecasts of the main magnetic field have valuable economic, social and logistical uses such as in resource exploration, navigation and hazard mitigation. Hence, it is important to produce the most accurate model possible for the magnetic field. As we await the launch of the Swarm mission, there may be a gap in which our present capability is diminished. If the existing set of satellites fail before Swarm is fully operational, we may become reliant upon groundbased observatories alone to produce global magnetic field models. Due to the uneven geographic distribution of observatories these models have low spatial resolution, which is not ideal. This poster looks at potential methods for mitigating the impact of such an event by employing an optimal data assimilation algorithm to make best use of all available data. We investigate if a sufficiently accurate forecast can be obtained using an initial high-resolution satellite field model to start with, combined with a flow model for advection of the field and intermittent updates from low resolution ground-based field model.

Item Type:	Publication - Conference Item (Poster)
Programmes:	BGS Programmes > Seismology and Geomagnetism
NORA Subject Terms:	Earth Sciences
Date made live:	26 Oct 2012 08:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/20087

Actions (login required)

View Item

Document Downloads

More statistics for this item...