Statistical forecasting techniques applied to observatory data for core field modelling
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AbstractModelling of the geomagnetic field is a complex challenge, hindered by noisy and incomplete ground and satellite observations, and the extent to which we can separate the contributions of the various field sources in these data. Forecasting of the core field and its time variations (secular variation), an activity of key interest for academic and applied studies of geomagnetism and space weather, is further complicated by an incomplete knowledge of the physics controlling magnetic field generation. Predictive core field models often rely on simple mathematical extrapolation to produce short term (<5 year) forecasts, but this technique can struggle when rapid variations known as geomagnetic jerks cause distinctly non-linear secular variation to occur. More advanced physics-based forecasting techniques such as core flow advection and geodynamo data assimilation also currently struggle to capture such short timescale variations. We discuss the applicability of common statistical forecasting techniques to ground observatory time series and compare the results of models based on such data forecasts to those of simple field model extrapolation and core flow advection forecasts.
TypePublication - Conference Item
Brown, William; Beggan, Ciaran; Macmillan, Susan. 2017 Statistical forecasting techniques applied to observatory data for core field modelling. [Poster] In: Good Hope for Earth Sciences Joint IAPSO-IAMAS-IAGA Assembly, Cape Town, South Africa, 27 Aug 2017 - 1 Sept 2017. British Geological Survey. (Unpublished)