estimating error statistics for chambon-la-forêt observatory definitive data
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2017
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Abstract
We propose a new algorithm for calibrating definitive observatory
data with the goal of providing users with estimates of the data error
standard deviations (SDs). The algorithm has been implemented and tested
using Chambon-la-Forêt observatory (CLF) data. The calibration process uses
all available data. It is set as a large, weakly non-linear, inverse problem
that ultimately provides estimates of baseline values in three orthogonal
directions, together with their expected standard deviations. For this
inverse problem, absolute data error statistics are estimated from two series
of absolute measurements made within a day. Similarly, variometer data error
statistics are derived by comparing variometer data time series between
different pairs of instruments over few years. The comparisons of these time
series led us to use an autoregressive process of order 1 (AR1 process) as a
prior for the baselines. Therefore the obtained baselines do not vary
smoothly in time. They have relatively small SDs, well below 300 pT when
absolute data are recorded twice a week – i.e. within the daily to weekly
measures recommended by INTERMAGNET. The algorithm was tested against the process
traditionally used to derive baselines at CLF observatory, suggesting that
statistics are less favourable when this latter process is used. Finally, two
sets of definitive data were calibrated using the new algorithm. Their
comparison shows that the definitive data SDs are less than 400 pT and
may be slightly overestimated by our process: an indication that more work
is required to have proper estimates of absolute data error statistics. For
magnetic field modelling, the results show that even on isolated sites like
CLF observatory, there are very localised signals over a large span of
temporal frequencies that can be as large as 1 nT. The SDs reported here
encompass signals of a few hundred metres and less than a day wavelengths.
| Reference Key |
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| Authors | ;V. Lesur;B. Heumez;A. Telali;X. Lalanne;A. Soloviev;A. Soloviev |
| Journal | journal of food measurement and characterization |
| Year | 2017 |
| DOI |
10.5194/angeo-35-939-2017
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| URL | |
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