Modelling the quiet-time geomagnetic daily variations using observatory data

We present on-going work towards building a global model of the quiet-time geomagnetic daily variation using bservatory data. We select hourly mean data during June 2006 (solar minimum). We fit Fourier series in time, with a fundamental period of 24 hours, to the data at each observatory. We then use global spherical harmonic expansions to separate the daily variation signal, as characterised by the Fourier coefficients in time, into external and induced internal contributions. The models are assessed by comparison with the input data and with Campbell’s Sq model. The robustness of the separation of the field into external and induced internal sources is discussed

Nurses Alumni Association Bulletin, Fall 2014

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Recent work at the World Data Centre for Geomagnetism (Edinburgh)

Observatory data holdings at the World Data Centre for Geomagnetism (Edinburgh) www.wdc.bgs.ac.uk include minute, hourly and annual mean values of the geomagnetic field from over 500 observatories since the early 19th century. We describe ongoing maintenance of this important data resource, data-checking procedures developed with global modelling in mind, and some recent additions and corrections

An appraisal of wind energy conversion systems for agricultural enterprises.

A detailed wind prediction model is developed, which predicts wind regimes and energy outputs from wind turbine generators at locations that are remote from sites where long-term wind data is available. The model accounts for the local, directional influences on the wind flow of topography and surface characteristics. The model for the validation runs performs well and predicts energy outputs over several months to generally within 7% of the actual energy outputs. Experience is described of a 60kW wind turbine generator connected at a pig farm in the North-East of Scotland, with respect to the wind regime, performance, farm energy consumption pattern and overall economics. Long-term economics are assessed by simulating different scenarios of wind turbine generators connected at farms. The different scenarios account for a realistic range of wind regimes, wind turbine generators, farm types and tariffs, all applicable in particular to the NE of Scotland but valid for many other areas in the UK. It is concluded that the main factors affecting economic feasibility of grid connected wind installations at farms are wind regime, local utilisation of wind generated electricity and availability of capital grants. Other factors include the choice of tariff and maintenance costs. The wind prediction model is shown to be a useful tool in assessing economic feasibility of wind installations on farms as both the wind regime and utilisation are dependent on accurate wind speed predictions

MEME08: A global magnetic field model with satellite data weighting

A new data weighting scheme is introduced for satellite geomagnetic survey data. This scheme allows vector samples of the field to be used at all magnetic latitudes and results in an improved lithospheric model, particularly in the auroral regions. Data weights for 20-second spaced satellite samples are derived from two noise estimators for the sample. Firstly the standard deviation along the 20 seconds of satellite track, centred on each sample, is computed as a measure of local magnetic activity. Secondly a larger-scale noise estimator is defined in terms of a ‘local area vector activity’ (LAVA) index for the sample. This is derived from activity estimated from the geographically nearest magnetic observatories to the sample point. Weighting of satellite data by the inverse-sum-of-squares of these noise estimators leads to a robust model of the field (called ‘Model of Earth’s Magnetic Environment 2008, or ‘MEME08’ - to rhyme with ‘beam’) to about spherical harmonic degree 60. In particular we find that vector data may be used at all latitudes and that there is no need to use particularly complex model parameterizations, regularisation, or prior data correction to remove estimates of un-modelled source fields

Improving time-dependent parameters of magnetic field models

An important part of modelling the Earth's magnetic field is to accurately characterise its temporal variation, in particular the secular variation, and secular acceleration. These quantities are sensitive to the data selection and the time-dependent parameterisation and we present modifications to these strategies. When selecting satellite data for magnetic field modelling it is normal practice to use less disturbed data collected when the local time is between certain hours during the night and perhaps additionally when the data are not sunlit. However this approach results in gaps in the temporal data distribution which are likely to compromise the model parameters that depend on time. If the solar zenith angle is also a selection criterion, parameters which depend on location will also be compromised as an annual signal is introduced into the data distribution at high latitudes. Here we strive for a more continuous coverage in time. Rather than eliminating large amounts of data which are normally considered to be too noisy to include in the model, we downweight these data. This builds on work done previously involving small-scale noise

Geomagnetic jerks during the Swarm era and impact on IGRF-12

Global geomagnetic field models can be used to study the dynamics of the core, aid satellite operation and make global digital navigation possible – from smartphones to guided drilling. While current models capture the long-period and large-scale features and variations of the core field well, it is often difficult to represent the poorly understood small-scale and rapid behaviour of the field, thus making prediction difficult. The mantle and crust filter small-scale spatial and temporal features originating in the Earth’s core, and field sources external to the Earth contaminate the observations we have. Geomagnetic jerks represent the most rapid observed variations of the internal field, on the scale of months to years. We investigate the occurrence and spatiotemporal characteristics of jerks in ground observatory data and a recent global field model, during the Swarm era. Previous reports suggest that global models show regions of high secular acceleration associated with the 2014 jerk and that expressions of this might be seen at European observatories post-2014. We find limited evidence of a jerk in European observatories around 2014, but do find globally widespread evidence, and our models of this signal are in agreement with independent field models. We also find evidence of a new jerk, after the 2014 event, in observatory data. In response to these events we show the impact on early, and potential future, discrepancies between International Geomagnetic Reference Field (IGRF) predictions and observations in the period of 2015-2020 as a result of the unpredictable, non-linear secular variation of jerks now known to have occurred. With the 2014 jerk occurring during / immediately after the data collection period for IGRF-12 and a subsequent jerk occurring shortly after release, we highlight the deviation from observations, the comparable performance of both simple and complex predictive models and the importance of utilising the ability to regularly update field models. This is likely to remain the case until the rapid dynamics of the core are better understood

Impact of Swarm data on global magnetic field models

We investigate the impact of early Swarm data on global magnetic field models. Since November 2013, Swarm has been providing satellite vector magnetic data, the first since the end of the CHAMP mission over three years ago. We describe two models of the Earth's magnetic field, one of which incorporates Swarm data. Both models include data from the CHAMP and Ørsted satellites and from observatories. All data are similarly selected on the basis of local time, solar zenith angle, upstream solar wind conditions and magnetic activity. The different data are weighted according to their type, location and estimation of content of signal not being modelled. The time-varying large-scale magnetospheric field is co-estimated with the internal field. We also provide an update on efforts to collate and check ground-based observatory measurements in support of the Swarm mission. These are particularly important for bridging the gap between CHAMP and Swarm as they provide the only accurate vector observations of the magnetic field during that time

Validation of Swarm satellite magnetic data using observatory measurements

The scientific use of Swarm magnetic data and Swarm-derived products is greatly enhanced through their combination with geomagnetic observatory data and indices. The strength of observatory data is their long-term accuracy, with great care being taken to ensure temperature control/correction, platform stability and magnetic cleanliness at each site. Recent work to improve the coverage and timeliness of observatory data has been encouraged and now over 60 INTERMAGNET observatories and several other high-quality observatories are providing close-to-definitive data within 3 months of measurement. An effort is being made to use these measurements to ground-truth Swarm data as part of the Calibration/Validation phase. These observatory data are gathered and homogenised on a regular basis by BGS and those collected during overhead passes of the Swarm satellites are identified. For each pass, we remove an estimate of the main field from both the data collected at altitude and that collected on the ground. Both sets of data are then normalised relative to the data variance during all passes since launch. The absolute differences of the two sets of normalised data can be used as a metric of satellite data quality relative to observatory data quality. When we analyse the results obtained for each Swarm satellite alongside those found when a complementary analysis is carried out on CHAMP, the findings are similar