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This could be done either by waiting until more observatory data become available for the considered time interval, or by calculating new models from subsampled datasets.
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Overall, satellite data carries more weight than observatory data when fitting the parent model.
The main improvement of CHAOS-5 over CHAOS-4 is its use of 10 months of Swarm data, as well as more recent ground observatory data.
We refer for instance to Matzka et al. (2010) for a more complete description of observatory data and the several signals they contain.
It can be seen that the observatory data provide a more continuous time-series than the satellite data, which is desirable for the robust time parametrisation of the model.
However, analysis of satellite data is more challenging compared to analysis of observatory data for two reasons: First, LEO satellites move typically with a speed of 7 8 km/s and thus measure a mixture of temporal and spatial changes of the magnetic field.
The main reason is that the analysis of satellite data is more challenging than the analysis of observatory data, since LEO satellites move typically with a speed of 7 8 km/s and thus, in reality, measure a mixture of temporal and spatial changes of the geomagnetic field.
When it comes to testing the predictive secular variation, comparisons between models and observatory data appear to be more discriminant than intercomparisons between models.
As observatory data, in general, are more accurate than repeat survey data (because of better baseline control and because seasonal and other short-term variations are more effectively removed by using annual means), we weighted observatory and repeat station secular variation data in a ratio 1 0.7 in the least-squares solution.
This model is derived from more than 10 years of satellite and ground observatory data.
We seek to address this problem by including observatory data, which helps provide a more continuous time-series.
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