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| Titel |
Noise variance estimation and optimal weight determination for GOCE gravity recovery |
| VerfasserIn |
J. Kusche |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: G1. The new gravity field mission (CHAMP, GRACE, GOCE): from measurements to geophysical interpretation ; Nr. 1 (2003-06-23), S.81-85 |
| Datensatznummer |
250000039
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| Publikation (Nr.) |
 copernicus.org/adgeo-1-81-2003.pdf |
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| Zusammenfassung |
In the course of level 2 data processing for the
GOCE (Gravity Field and Steady–State Ocean Circulation
Explorer) satellite mission different streams of level 1b data
will be merged in a single solution providing the Earth’s
gravity field, but also state-vector parameters and other quantities.
A proper weighting of orbit tracking data, gravity gradiometry
data and certain a priori information, usually considered
as ‘solution constraints’, can be expected as crucial
for the solution quality. But the a priori stochastic models,
based on pre–mission assessment of the expected instrument
behaviour, may be over–optimistic or even too pessimistic
since they refer to an unprecedented mission with scientific
payload never tested in space. One way to derive an optimal
weighting scheme on a statistically sound basis while
simultaneously validating the stochastic noise levels of the
data is by including variance component estimation as a part
of the level 1b to level 2 data analysis process. The idea is
that by applying Monte-Carlo techniques this method can be
extended to a large-scale problem like GOCE data analysis,
using software modules that already exist or are currently
under development. The proposed method has been tested
using simulated GOCE orbit trajectories as well as gravity
gradiometry data corrupted by colored random noise.
Key words. GOCE, gravity field modelling, combination
solutions, weight estimation, variance component estimation |
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