Estimation and modelling of the local empirical covariance function using gravity and satellite altimeter data |
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Authors: | Knudsen |
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Institution: | (1) Geodaetisk Institut, Gamlehave Alle 22, DK-2920 Charlottenlund, Danmark |
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Abstract: | The estimation of a local empirical covariance function from a set of observations was done in the Faeroe Islands region.
Gravity and adjusted Seasat altimeter data relative to theGPM2 spherical harmonic approximation were selected holding one value in celles of1/8°×1/4° covering the area. In order to center the observations they were transformed into a locally best fitting reference system
having a semimajor axis1.8 m smaller than the one ofGRS80. The variance of the data then was273 mgal
2 and0.12 m
2 respectively. In the calculations both the space domain method and the frequency domain method were used. Using the space
domain method the auto-covariances for gravity anomalies and geoid heights and the cross-covariances between the quantities
were estimated. Furthermore an empirical error estimate was derived. Using the frequency domain method the auto-covariances
of gridded gravity anomalies was estimated. The gridding procedure was found to have a considerable smoothing effect, but
a deconvolution made the results of the two methods to agree.
The local covariance function model was represented by a Tscherning/Rapp degree-variance model,A/((i−1)(i−2)(i+24))(R
B
/R
E
)2i+2, and the error degree-variances related to the potential coefficient setGPM2. This covariance function was adjusted to fit the empirical values using an iterative least squares inversion procedure adjusting
the factor A, the depth to the Bjerhammar sphere(R
E
−R
B
), and a scale factor associated with the error degree-variances. Three different combinations of the empirical covariance
values were used. The scale factor was not well determined from the gravity anomaly covariance values, and the depth to the
Bjerhammar sphere was not well determined from geoid height covariance values only. A combination of the two types of auto-covariance
values resulted in a well determined model. |
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Keywords: | |
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