Earth Planets Space, Vol. 62 (No. 10), pp. 775-785, 2010
Weijia Kuang1, Zigang Wei2, Richard Holme3, and Andrew Tangborn2
1Planetary Geodynamics Laboratory, NASA GSFC, USA
2Joint Center for Earth Systems Technology, UMBC, USA
3School of Environmental Sciences, University of Liverpool, UK
(Received February 24, 2010; Revised July 2, 2010; Accepted July 6, 2010; Online published December 31, 2010)
Data assimilation has been used in meteorology and oceanography to combine dynamical models and observations to predict changes in state variables. Along similar lines of development, we have created a geomagnetic data assimilation system, DAS, which includes a numerical geodynamo model, a suite of geomagnetic and paleomagnetic field models dating back to 5000 BCE, and a data assimilation component using a sequential assimilation algorithm. To reduce systematic errors arising from the geodynamo model, a prediction-correction iterative algorithm is applied for more accurate forecasts. This system and the new algorithm are tested with 7-year geomagnetic forecasts. The results are compared independently with CHAOS and IGRF field models, and they agree very well. Utilizing the geomagnetic field models up to 2009, we provide our prediction of 5-year mean secular variation (SV) for the period 2010-2015 up to degree L = 8. Our prediction is submitted to IGRF-11 as a candidate SV model.
Key words: Geodynamo, geomagnetism, data assimilation, secular variation, IGRF.