Prediction of geomagnetic field with data assimilation: a candidate secular variation model for IGRF-11

Abstract

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, MoSST-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.