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Journal Article

Application of time-variable process noise in terrestrial reference frames determined from VLBI data

Authors
/persons/resource/bsoja

Soja,  B.
External Organizations;

Gross,  Richard S.
External Organizations;

Abbondanza,  Claudio
External Organizations;

Chin,  Toshio M.
External Organizations;

Heflin,  Michael B.
External Organizations;

Parker,  Jay W.
External Organizations;

Wu,  Xiaoping
External Organizations;

/persons/resource/balidak

Balidakis,  K.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/nilsson

Nilsson,  T.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/sglaser

Glaser,  Susanne
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/karbon

Karbon,  Maria
External Organizations;

/persons/resource/rob

Heinkelmann,  R.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/schuh

Schuh,  H.
1.1 Space Geodetic Techniques, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Soja, B., Gross, R. S., Abbondanza, C., Chin, T. M., Heflin, M. B., Parker, J. W., Wu, X., Balidakis, K., Nilsson, T., Glaser, S., Karbon, M., Heinkelmann, R., Schuh, H. (2018): Application of time-variable process noise in terrestrial reference frames determined from VLBI data. - Advances in Space Research, 61, 9, 2418-2425.
https://doi.org/10.1016/j.asr.2018.02.023


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_3471889
Abstract
In recent years, Kalman filtering has emerged as a suitable technique to determine terrestrial reference frames (TRFs), a prime example being JTRF2014. The time series approach allows variations of station coordinates that are neither reduced by observational corrections nor considered in the functional model to be taken into account. These variations are primarily due to non-tidal geophysical loading effects that are not reduced according to the current IERS Conventions (2010). It is standard practice that the process noise models applied in Kalman filter TRF solutions are derived from time series of loading displacements and account for station dependent differences. So far, it has been assumed that the parameters of these process noise models are constant over time. However, due to the presence of seasonal and irregular variations, this assumption does not truly reflect reality. In this study, we derive a station coordinate process noise model allowing for such temporal variations. This process noise model and one that is a parameterized version of the former are applied in the computation of TRF solutions based on very long baseline interferometry data. In comparison with a solution based on a constant process noise model, we find that the station coordinates are affected at the millimeter level.